Managing Risks in Electrical Infrastructure Assets from a Strategic Perspective

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M

ANAGING

R

ISKS IN

E

LECTRICAL

I

NFRASTRUCTURE

A

SSETS

FROM A

S

TRATEGIC

P

ERSPECTIVE

Qikai ZHUANG

November 2015

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M

ANAGING

R

ISKS IN

E

LECTRICAL

I

NFRASTRUCTURE

A

SSETS

FROM A

S

TRATEGIC

P

ERSPECTIVE

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft

op gezag van de Rector Magnificus prof.ir. K.C.A.M. Luyben voorzitter van het College voor Promoties,

in het openbaar te verdedigen op 16 november 2015 om 15.00 uur door

Qikai ZHUANG

Master of Science in Electrical Engineering, Shanghai Jiao Tong University, China

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Dit proefschrift is goedgekeurd door de

Promotor: Prof. dr. J.J. Smit Copromotor: Dr. ir. D. Djairam

Samenstelling promotiecommissie:

Rector Magnificus,

Prof. dr. J.J. Smit, Technische Universiteit Delft, promotor Dr. ir. D. Djairam, Technische Universiteit Delft, copromotor Independent members

Prof. dr. ir. J.A. La Poutré, Technische Universiteit Delft Prof. dr. S. Rowland, University of Manchester Prof. dr. K. Wu, Xi’an Jiao Tong University Prof. dr. ir. P.M. Herder, Technische Universiteit Delft Other member

Ir. A.L.J. Janssen, Liander, Arnhem, the Netherlands

This research was funded by the Next Generation Infrastructures Foundation ISBN: 978-94-62331-25-9

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This book is dedicated to my parents

Zhimin Zhuang and Xueqin Cao

谨以此书献给我的父母

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i

Summary

Since the deregulation and corporatization of the electricity transmission and distribution sector in the last decade, the utility companies in Europe have accepted the concept of “asset management”. Today’s asset management practices focus on two goals: improving reliability through maintenance and coordinating capacity upgrades with replacements.

Meanwhile, the business environment of the electricity sector keeps changing. Long-term trends, such as sustainable energy, demand-side management, an aging workforce, and public concerns on the environment introduce new elements of risk through the stakeholders. These risks, called strategic risks in this thesis, are not yet handled in a structured way within the decision-making process.

This thesis aims to provide a solution to the difficulty of handling strategic risks in the widely-accepted triple-level model of asset management. This model states that asset management should be performed simultaneously at the operational, tactical and strategic level. Through comparing today’s practice with PAS 55, a popular asset management standard for industry in general, we find out that: reliability and capacity are typical concerns at the tactical level, while the long-term stakeholder oriented risks should be investigated and managed at the strategic level.

* * *

Our investigation starts at the tactical level, with a topic which is familiar to today’s asset manager: the failure and life data analysis on an asset population. In a few researches, statistical methods have been applied to a complete collection of service failure data, such as of power transformers. Since this completeness is usually not the case (especially for new assets), we developed statistical methods for the situations that failure data is incomplete or only available from a limited number of samples in lab tests. For the incomplete data sets, we develop a method called Iterative Approximation Maximization (I-AM) to fit the observed data to a probability distribution. For the lab tests, we use the accelerated test data analysis to estimate confidence bounds on the lifetime of full-scale high voltage components. The two methods are important supplements to life data analysis on field failure statistics.

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The service failure data and accelerated life test data, as well as so-called operation stress data and diagnostic data, are the four categories of data which reflects reliability of assets. Therefore we collectively denote them as the “reliability data”. We reveal that each category plays a different role in risk-based AM. The role of accelerated life test data at the tactical level, as well as that of service failure data and operation stress data at the operational level, are precaution-based, i.e. tends to provide a safe zone for operation and maintenance. In contrast, the service failure data and the diagnostic data are applied respectively at the tactical level and operational level to optimize corresponding decisions. These roles are risk-based.

The evolvement from precaution-based to risk-based decision-making is a concern of the strategic level, since it is a long-term development. We formulate this evolvement in a knowledge maturity model for maintenance management. In this model, two knowledge maturity scores can be rated for maintenance scheduling at the operational level and for maintenance planning at the tactical level. The standards we establish for rating the maturity scores reflect the roadmap of setting up risk-based maintenance as an AM strategy.

The knowledge maturity model reveals that the widely accepted risk-based AM strategy should be coordinated with two other strategies. The first is a precaution-based AM strategy which prevents risks without requiring sufficient knowledge on them. The second is an information strategy which collects, accumulates and interprets knowledge as decision-making processes.

According to modern theory of risk management, the precaution-based, risk-based and information strategies aim to deal with the risks which cannot be analyzed with a risk matrix or other quantitative methods at the tactical level. We name these risks as “strategic risks” to indicate that they should be analyzed at the strategic level.

* * *

With this definition of strategic risks, we shift our focus to the strategic level, on how strategic risks can be studied according to the characteristics and patterns of their causal chains.

Through identifying the characteristics of individual strategic risks, a method developed originally by Klinke and Renn for risk management is adapted by us for strategic asset

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iii management. This method includes a decision tree to divide them into six types named by ancient Greek mythology. They are:

•_{Pythia, i.e. the knowledge is insufficient to identify technical consequences,} • Pandora (‘s box), i.e. the loss of a scarce resource due to a stakeholder can cause

a wide range of troubles through preventing an asset manager from performing certain essential activities,

• Cyclops, i.e. a potentially important causality was unknown or neglected, therefore only one side of the fact was revealed,

• (Sword of) Damocles, i.e. a stimulus can lead to catastrophic results, when asset managers fail to realize that several causalities can be triggered at the same time,

• Cassandra, i.e. an expert foresees a huge loss, but the decision maker refuses to take actions because it takes decades to develop.

• Medusa, i.e. under the influence of media or society, a stakeholder is occupied with negative emotions to certain assets, by which risk is stimulated.

Using the classification, we will generally explain how to tackle each type of strategic risk with an AM strategy. Then, the AM strategies will be illustrated in our cases on medium voltage cables.

* * *

In addition, we recognize that a complex network of causalities can be built by a number of interdependent strategic risks. Such complexity can be analyzed with the system diagrams. Thus we adapt the system diagrams method to the strategic AM, as possibly the first structured method for analyzing strategic risks and deciding AM strategies in our sector.

Our adaption introduces three sets of rules. The first set of rules decides how to symbolize in a system diagram:

• the technical, economic and societal factors concerned in the long-term management of an asset population with basic shapes termed as “nodes” , •_{their mutual qualitative relationships with arrow lines termed as “edges”, and} • the amount of knowledge on each factor depicted by color in the corresponding

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The second set of rules specifies the critical patterns of causalities in the system diagram which the strategic level of AM should tackle. These rules also define how each type of AM strategy can be applied to tackle these patterns.

The third set of rules can reveal the feasibility to apply any proposed risk-based AM strategy through checking the color of “nodes”. This method has been developed through embedding our knowledge maturity model into the system diagram.

* * *

In summary, this thesis reveals the limited capability of quantitative risk analysis to identify long-term stakeholder oriented risks within the knowledge maturity model. These risks, called strategic risks in this thesis, can be tackled in a structured way, through coordinating precaution-based, risk-based and information strategies with the system analysis method we developed in this thesis.

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Samenvatting

Sinds het proces van deregulatie and privatisering van de elektriciteitssector (transmissie & distributienetten) zich in de afgelopen decennia heeft ingezet, hebben de nutsbedrijven in Europa het concept van “asset management” geaccepteerd. De hedendaagse praktijk op het gebied van asset-management richt zich op twee doelen: 1) het verbeteren van de betrouwbaarheid door middel van onderhoud en 2) het coördineren van capaciteitsuitbreiding door middel van vervangingen.

Tegelijkertijd met bovenstaand proces is ook de zakelijke context van de elektriciteitssector aan verandering onderhevig. Lange termijn trends zoals duurzame energie, demand-side management, veroudering van beroepsbevolking en maatschappelijke onrust op het gebied van milieuvraagstukken introduceren nieuwe risico-elementen via de stakeholders. Deze risico’s, in dit proefschrift aangeduid als strategische risico’s (strategic risks), worden nog niet op een gestructureerde manier meegenomen in het besluitvormingsproces.

Dit proefschrift beoogt een oplossing te geven voor de uitdaging van het omgaan met deze strategische risico’s binnen het kader van het algemeen aanvaarde triple-level

model van asset-management. Dit model stelt dat asset-management gelijktijdig op drie

niveaus beoefend moet worden, namelijk op operationeel, tactisch en strategisch niveau. Door het vergelijken van de hedendaagse praktijk met PAS 55, een veelvuldig toegepaste standaard voor asset-management voor de industrie in het algemeen, vinden we dat betrouwbaarheids- en capaciteitsvraagstukken typische zorgen zijn op het tactische niveau, terwijl lange termijn risico’s van de stakeholders op het strategische niveau onderzocht en beheerd moeten worden.

* * *

Ons onderzoek begint op het tactische niveau met een onderwerp dat de hedendaagse asset manager goed kent: de analyse van faal- en levensduurgegevens van een populatie/vloot van assets. In een aantal onderzoeken zijn statistische methoden toegepast op een complete verzameling van storingsgegevens, zoals bijvoorbeeld die van vermogenstransformatoren. Omdat de verzameling doorgaans niet compleet is (zeker in het geval van nieuwe assets), is er in dit proefschrift een aantal statistische

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methoden ontwikkeld voor situaties waar de faalgegevens incompleet zijn of verkregen zijn uit beperkte steekproeven in laboratoriumtests. Voor de incomplete datasets is een methode ontwikkeld genaamd “Iterative Approximation Maximization” (I-AM) om de geobserveerde data te fitten naar een kansverdeling. Voor de laboratoriumtests is de “Accelerated Test Data Analysis” gebruikt om de betrouwbaarheidsintervallen van de levensduur van de eigenlijke hoogspannings-componenten te schatten. Deze twee methoden zijn belangrijke aanvullingen op de analyse van levensduurgevens van assets in het veld.

De storingsgegevens en de gegevens uit de accelerated life tests, samen met de zogenaamde operationele belastingsgegevens en diagnostische gegevens, zijn de vier categorieën van gegevens die de betrouwbaarheid van assets reflecteren. Daarom worden deze collectief ook aangeduid als betrouwbaarheidsgegevens (reliability data). We laten zien dat elke categorie een andere rol in het risico-gebaseerde asset-management speelt. De rol van de gegevens uit de accelerated life tests op het tactische niveau is precaution-based, d.w.z. het voorziet in een veilige zone voor uitvoering en beheer. Deze rol is ook van toepassing voor de storings- en belastingsgevens op het operationele niveau. Daarentegen worden de storingsgevens op het tactische niveau en de diagnostische gegevens op het strategische niveau toegepast om de bijbehorende besluiten te optimaliseren. De rollen van deze gegevens zijn risk-based.

De ontwikkeling van een voorzorgsgebaseerd naar een risico-gebaseerd besluitvormingsproces is een aandachtspunt op strategisch niveau gezien het feit, dat het een lange termijn ontwikkeling is. Deze ontwikkeling wordt geformuleerd in een knowledge maturity model voor onderhoudsmanagement. In dit model kunnen twee scores voor knowledge maturity toegekend worden: voor maintenance scheduling (operationeel niveau) en maintenance planning (tactisch niveau). De standaarden die we opstellen voor het toekennen van de maturity scores vormen de roadmap voor het opzetten van risico-gebaseerd onderhoud als een asset-management strategie.

Het knowledge maturity model laat zien dat de algemeen aanvaarde risico-gebaseerde asset-management strategie gecoördineerd moet worden met twee andere strategieën. De eerste is een voorzorg-gebaseerde (precaution-based) AM strategie, die risico’s voorkomt zonder dat voldoende kennis van deze risico’s nodig is. De tweede is een informatie strategie waarin kennis verzameld, opgebouwd en geïnterpreteerd wordt voor het besluitvormingsproces.

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vii Volgens de moderne theorieën van risicomanagement, beogen zowel de op voorzorg- en risico-gebaseerde strategieën als de informatie strategieën om te gaan met risico’s, die niet geanalyseerd kunnen worden in een risicomatrix of met één van de andere kwantitatieve methoden op het tactische niveau. Deze risico’s worden als strategische risico’s aangeduid om duidelijk te maken dat deze op het strategische niveau geanalyseerd moeten worden.

* * *

Met deze definitie van strategische risico’s wordt de aandacht gericht op het strategische niveau en hoe deze risico’s bestudeerd kunnen worden op basis van hun eigenschappen en casuale verbanden.

Met het identificeren van de eigenschappen van de individuele risico’s is een door ons aangepaste risico-managementmethode van Klinke en Renn gebruikt voor strategisch AM. Deze methode omvat een beslissingsboom om deze risico’s onder te verdelen in zes types, elk gekarakteriseerd met een naam uit de Grieke mythologie:

• Pythia: hier is de kennis ontoereikend om technische gevolgen te identificeren. •_{Pandora (doos): het verlies van een zeldzaam middel (ten gevolge van een}

stakeholder) kan een groot scala aan moeilijkheden veroorzaken, doordat een asset manager bepaalde essentiële activiteiten niet meer kan uitvoeren.

• Cyclops: een mogelijk belangrijke oorzaak was onbekend of werd genegeerd, waardoor maar een kant van de feiten belicht werd.

•_{(Zwaard van) Damocles: een stimulus/oorzaak dat kan leiden tot catastrofale} gevolgen wanneer asset managers niet realiseren dat een aantal causaliteiten gelijktijdig getriggerd kunnen worden.

• Cassandra: hier is sprake van een expert, die een groot verlies aan ziet komen, maar de beslisser weigert hier op actie te ondernemen, omdat dit risico zich over een lange termijn voltrekt.

• Medusa: onder invloed van de media en/of maatschappij is een stakeholder bezig met negatieve emoties m.b.t. bepaalde assets, waardoor risico wordt gestimuleerd.

Met deze onderverdeling wordt eerst op algemene wijze uitgelegd hoe elk van de strategische risico’s met een AM strategie aangepakt kunnen worden. Daarna worden deze AM strategieën geïllustreerd in cases over middenspanningskabels.

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Verder wordt ingezien dat een complex netwerk van causaliteiten opgebouwd kan worden door een aantal onderling afhankelijke strategische risico’s. Deze complexiteit kan geanalyseerd worden met systeemdiagrammen. Daarom is de systeem-diagrammethode aangepast voor strategisch AM, waarmee de eerste gestructureerde methode voor het analyseren van strategische risico’s and het bepalen van AM strategieën in de elektriciteitssector.

De aangepaste methode introduceert drie sets van regels. De eerste set bepaalt hoe alles gesymboliseerd wordt in een systeemdiagram:

• de technische, economische en maatschappelijke factoren betrokken bij de lange termijn management van een assetpopulatie worden weergegeven met basisvormen genaamd nodes

•_{de onderlinge kwalitatieve relaties worden weergegeven met lijnen en pijlen} genaamd edges

• de hoeveelheid kennis over elke factor wordt weergegeven met kleur in de corresponderende node

De tweede set van regels specificeert de kritische patronen van causaliteiten in de systeemdiagram die door het strategisch niveau van AM aangepakt worden. Deze regels definiëren ook hoe elk type AM strategie toegepast kan worden om deze patronen te behandelen.

De derde set van regels kan gebruikt worden om de haalbaarheid om een voorgesteld risico-gebaseerde AM strategie te onthullen. Dit gebeurt door het controleren van de kleur van de nodes. Deze methode is ontwikkeld door het inbedden van het knowledge maturity model in het systeemdiagram.

* * *

Samenvattend, dit proefschrift toont de beperkte mogelijkheid van kwantitatieve risicoanalyse om lange termijn stakeholder georiënteerde risico’s te identificeren binnen het knowledge maturity model. Deze strategische risico’s kunnen op een gestructureerde manier aangepakt worden door het coördineren van voorzorg-gebaseerd, risico-gebaseerd en informatie strategieën met de systeemanalyse-methode ontwikkeld in dit proefschrift.

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List of Tables

Table 1.1: Maintenance management has become more proactive in the last decades, but it still has gaps from the asset management defined in PAS 55 ... 5 Table 2.1: Features of the Three Levels of Asset Management (Adapted from [21]) .... 19 Table 2.2: Risk consequence matrix of Dutch utility sector translated from [12] ... 33 Table 2.3: Risk (assessment) matrix in [12] ... 35 Table 2.4: Contents of asset management strategies illustrated in the case of risk-based

decision-making ... 44 Table 2.5: Differences between the tactical level and the strategic level in their

decision-making procedures ... 46 Table 3.1: The degree of completeness of failure data determines whether it is possible

to convert to life data and which probabilistic model it can fit into ... 56 Table 3.2: Converging Speed and Errors of Weibull Parameters Estimated from

Simulated Incomplete Data Pools with I-AM Algorithm ... 67 Table 3.3: Probability distribution of lifetime of specimen, estimated separately from

TTF of Type 1 samples at respective voltages ... 101 Table 3.4: Inverse Power – Weibull Parameters estimated from time-to-failure data of

Type 1 samples at 0.9 kV and 0.75 kV ... 102 Table 3.5: Lifetimes of specimen, estimated from time-to-failure of Type 1 samples at

0.9 kV and 0.75 kV ... 103 Table 3.6: Lifetimes for full transformer scale, estimated from time-to-failure of Type 1 samples at 0.9 kV and 0.75 kV ... 104 Table 3.7: Role of three models of failure and life data analysis in different stages of

asset life cycle ... 108 Table 4.1: Different measurement systems are required to acquire the four categories of

reliability data ... 123 Table 4.2: The role of four data sources in different levels and strategies asset

management ... 125 Table 4.3: Four Maintenance Scheduling Methods can be Identified according to Their

Goals and Constraints within Optimization (Summarized from Appendix C.5) ... 128 Table 4.4: The Evolvement of Processes of Maintenance Scheduling Methods (Derived

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Table 4.5: Using Readiness of Knowledge to Score the Maturity of Maintenance Scheduling Method, as a Part of the Regime of Risk-based Asset

Management Strategy ... 130 Table 4.6: One method to perform maintenance planning at different maturity levels

(According to Figure D.1 and D.2) ... 136 Table 5.1: Comparing Different Names of the Components of a Risk ... 143 Table 5.2: The different sequences of strategic risks decide that they have

characteristics different from normal risks. ... 149 Table 5.3: Main characters of the six types of strategic risks, derived from the

classification tree ... 158 Table 6.1: AM strategies to tackle homogenous forwards and heterogeneous

feed-forwards illustrated in Figure 6.8 ... 192 Table 6.2: Legends of edges in a system diagram ... 198 Table 6.3: Scoring standards for knowledge maturity of internal business factors ... 201 Table 6.4: Precaution-based strategies currently applied in O&M of MV cables to

control the positive feedback loop of thermal aging. ... 210 Table 6.5: Risk-based and precaution-based AM strategies applied in future O&M of

MV cables to control all identified critical patterns in our system diagram. ... 214 Table 6.6: Information strategies necessary to build knowledge for risk-based AM

strategies in Table 6.5. ... 215 Table C.1: Definition and examples of condition indicators shown in Figure C.1 ... 244 Table C.2: Probability of interruption, escalation and insufficient recovery caused by a

subsystem failure are inferred from condition indicators and health indices in different ways ... 246 Table C.3: Differences between failure modes and failure scenarios... 248

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List of Figures

Figure 2.1: Asset management decision support and making at three levels. ... 22 Figure 2.2: Demonstration of increasing failure rate during the life cycle of assets when

maintenance are implemented. ... 25 Figure 2.3: Organizational relationship illustrating the responsibilities of asset owner,

asset manager and service provider, from page 16 of [18]. ... 38 Figure 3.1: Demonstration of the process to convert raw failure records (demonstrated

by a figure from [35]) into failure frequency. ... 54 Figure 3.2: Demonstration of the process to model failure data in lifetime models, in

which the failures with their installation time unknown are excluded. ... 55 Figure 3.3: Mathematical relationships between probabilistic models of failure and life

data... 59 Figure 3.4: Histogram of Weibull parameters estimated from I-AM algorithm from

1000 simulated incomplete life data pools. ... 66 Figure 3.5: Typical converging processes of Weibull parameters in I-AM iterations. .. 79 Figure 3.6: Histogram of 100 simulated Weibull parameters estimated from different

I-AM programs, showing the advantages of I-I-AM with grouping equations.. 81 Figure 3.7: Histogram of 1000 simulated Weibull parameters estimated from the I-AM

program with grouping equations in A3-steps. ... 82 Figure 3.8: Bias of I-AM estimations on Weibull shape parameter, indicated by the

mean of estimation error. ... 83 Figure 3.9: Width of confidence interval of I-AM estimations on Weibull shape

parameter, indicated by the standard deviation of estimation error. ... 83 Figure 3.10: Bias of I-AM estimations on Weibull scale parameter, indicated by the

mean of estimation error. ... 84 Figure 3.11: Width of confidence interval of I-AM estimations on Weibull shape

parameter, indicated by the standard deviation of estimation error. ... 84 Figure 3.12: Range of Weibull parameters in which I-AM estimator is biased and/or has a wide confidence interval (C-I). ... 85 Figure 3.13: Procedure of statistical life prediction through utilizing accelerated life data. ... 90 Figure 3.14: Demonstration of voltage vs. lifetime characteristics of epoxy resin

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Figure 3.15: V-t characteristics of the Type 1 samples, created through plotting the MTTF’s estimated separately at 5 voltage levels on the log. V vs. log T sheet. ... 101 Figure 3.16: Weibull probability plot of the lifetime of specimen, estimated from

time-to-breakdown of Type 1 samples at 0.9 kV and 0.75 kV. ... 103 Figure 4.1: Two aging mechanisms occur simultaneously with various mutual

relationships ... 114 Figure 4.2: The simplified causal chain from aging to failure of components and asset

systems, based on which four category of reliability data can be acquired. ... 118 Figure 5.1: Typical components of a normal risk handled by the tactical level of AM, as

well as their characteristics. ... 144 Figure 5.2: Components of strategic risks in different types of sequences. ... 147 Figure 5.3: Decision tree to classify strategic risks in asset management into six risk

types. ... 154 Figure 6.1: Positive (left) or negative (right) causality relationship from international

business factor A to B. ... 184 Figure 6.2: Factor A has two consequences, Factors B and C, in a system diagram. ... 186 Figure 6.3: Factor A and B have opposite effects on Factor C, while Factor B is

controlled by an AM strategy. ... 187 Figure 6.4: Two causal chains A-C and D-B-E are coupled in (a). In (b), they are

decoupled in a risk-based way, while in (c) the coupling effect of Factor B on Factor C is limited by the AM strategy. ... 188 Figure 6.5: Interchangeability between (a) two consequences of the same stimulus in, or

(b) two stimuli with the same consequence. ... 189 Figure 6.6: A positive feedback loop A-B-C-D is formed, while Factor B is controlled to avoid instable output of the loop. ... 190 Figure 6.7: A negative feedback loop in (a) can be simplified as conditional causal link

(b), given that AM strategy is limiting the effect of the feedback. ... 191 Figure 6.8: Homogenous feed-forward (a) and heterogeneous feed-forward (b). ... 192 Figure 6.9: Four types of nodes in a system diagram. ... 195 Figure 6.10: Part of system diagram for O&M of MV cables, in which strategic

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xix Figure 6.11: Part of system diagram for O&M of MV cables, in which the failures have

different effects on performance and repair activities intend to control them. ... 208 Figure 6.12: A causal link from performance indicators to a strategic problem builds an

additional feedback loop. ... 208 Figure 6.13: The complete system diagram about O&M of MV cables, on which today’s knowledge maturity levels are shown with colors. ... 211 Figure 6.14: The complete system diagram about O&M of MV cables in the future,

which include the future AM strategies necessary for a risk-based

management. ... 213 Figure 6.15: The complete system diagram about R&U of MV cables, in which the

risk-based AM strategies at the left side provides flexibility to the precaution-based AM strategies at the right side. ... 218 Figure C.1: Composing diagnostic condition indicators (DCI), statistical remaining life

indicators (SRLI), operation stress indicators (OSI) into condition indicators and health index for an asset. ... 243 Figure C.2: Information flows of different maintenance scheduling methods shows that

they are supported by different knowledge. ... 253 Figure D.1: The procedure proposed by our knowledge maturity model to identify

failure modes (Step 1) and find maintenance plans (Step 2) at the standard maturity level. ... 260 Figure D.2: The procedure proposed by our knowledge maturity model to establish

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Chapter 1 _Introduction

Electricity transmission and distribution system operators (TSO/DSO) have been unbundled from a natural monopoly utility sector into groups of independent corporations. This reorganization brings challenges to maintenance departments of these new corporations. Traditionally, maintenance departments focus on developing repair techniques and performing repair activities in order to guarantee a high reliability of the network. Nowadays, economic and environmental responsibilities have also been assigned to maintenance managers. They are expected to organize the maintenance activities optimally in such a way that the profits and performances of the TSO/DSO are maximized. This elevates maintenance management to a new level, namely “asset management”.

Asset management (AM) develops both from the societal and technical side. Section 1.1.1 presents, at the societal side, the commercial and political environment that TSO/DSO’s are facing. Correspondingly, a “top-down” solution, namely the performance indicator system introduced in Section 1.1.2, is being developed by TSO/DSO’s. Meanwhile, at the technical side, maintenance engineers attempt to develop engineering-based, “bottom-up” solutions guided by performance indicators (See 1.2.1). However, in Section 1.2.2, we point out that the connections between currently available “top-down” and “bottom-up” solutions remain weak. Therefore, the motivation of this thesis is to investigate the gaps between technology, business and society. The solution lays in the development of “strategic level” of asset management, which can be performed from a number of aspects which will be listed in Section 1.3.

1.1 Electricity Transmission and Distribution Business

1.1.1 Business Environment: the Deregulation of Utility Service

The liberalized electricity market launched in the 1980’s has brought a complete change to the power delivery sector. During the liberalization processes, governments divested themselves from the utility services and established the trade systems of utility products. Consequently, the corporatized electricity sector is operated by multiple companies whose business areas are defined by the regulator. This means, in the case of

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2

an electricity TSO/DSO, the involvement in generation and trade is forbidden[1]. In the Netherlands, an additional boundary line between transmission and distribution was drawn: the TSO only owns the high-voltage network at and above 110 kV, while the DSO’s owns and operates the medium-voltage networks below 110 kV[2, 3].

Before the implementation of the above mentioned regulations, the utility sector was protected by its natural monopoly status. This enabled the system operators to adopt risk-free development strategies such as “expanding the network up to its technical limits”, or “enhance the reliability and redundancy with all available budgets”. After the market liberalization, an electricity transmission or distribution company can no longer afford such expensive development strategy, since it exposes the asset owner to commercial risks[4]. Coincidently, the growing public concerns regarding the environment and sustainability have led to stricter restrictions on the existence and expansion of the power grid[5, 6] and its subsystems including overhead lines[7], substations[8] and cables[9]. These restrictions are not only technical, but also economic and societal, such as:

•_{Risks with technical triggers (related to assets) and economical and societal} impacts:

o Reliability needs to be maintained for the long-term continuity of the company.

o The age of components within the grid is approaching their design lifetime.

o New components (e.g. power electronics) are widely installed, but their influence on the existing network is insufficiently understood. o New generators (e.g. wind turbine) and appliances (e.g. electric

vehicle) require more ampacity of components and hosting capacity of the system.

• Risks and restrictions with economical and societal triggers (related to stakeholders) that have impact on the technical network:

o Investors and creditors are sensitive to anything that could jeopardize the profitability of the company.

o Extensive knowledge on components will be lost during large-scale retirement of employees born during the post-World War II baby boom.

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3 o Consumers and the regulator strive to reduce the transmission

tariff.

o Concern on safety, environment and other public values add to the costs of expansion, reinforcement, maintenance and failure of the network.

1.1.2 Performance Benchmarking of Electricity Transmission and Distribution Business: Principle and Challenges

In response to the requirements listed above, TSO/DSO’s have started to track the occurrence of a number of events, such as outages, injuries, etc., and trace their linkage with specific engineering activities, such as repair, replacements, etc. Records of these events are the basis of a performance indicator system, which is widely recommended as the primary approach of asset management benchmarking[10].

A performance indicator system has two essential properties: measurability and convertibility. On one hand, measurability means that each recorded event can be evaluated as a number indicating its (positive or negative) contribution to some business goals. On the other hand, convertibility means that the measured performance indicators can be converted into currency [11, 12], e.g. evaluate customer minutes loss, the unit of unreliability, with euros, dollars, etc..

However, in practice, the above two properties of performance indicators show their limits. Firstly, the measurability of future performance depends on the accurate prediction of the triggering events. However, this prediction is frequently not feasible. For technical triggers, such as failures, statistical methods can be applied only when sufficient records exist, see Chapter 3 for details. In contrast, societal triggers are even less predictable. They can only be described ambiguously and qualitatively with scenarios[4], because their occurrence relies on the economic, societal and political conditions outside the reach and control of a TSO/DSO[13]. These conditions are so sensitive that large incidents have the ability to dramatically reverse development policy on a possible global scale, such as the Fukushima nuclear disaster did with nuclear energy[14, 15].

Secondly, convertibility between performance indicators is based on the assumption that resources other than capital are available in the market or, at least, acquirable with some expenditure. However, this is not necessarily true. For example,

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outage hours become a scarce resource when the system is fully loaded. In this situation, maintenance activities should be performed whenever there is possibility to disconnect a component out of service, regardless of the costs. Moreover, societal resources, such as availability of work force, availability of land, etc., are frequently not purchasable. Hence, performance evaluation will become much more complex, given that the restrictions from society will likely increase in the next decades.

The two problems stated above can be solved with two methodologies. The relatively conventional methodology is network planning and design. Network planners and designers change the parameters of the circuit to obtain capacity for future power loads and controllability on the power quality. Comparatively, AM is the newer methodology which links problems with specific type of components and devices. These linkages, introduced in brief in the next section and in detail in chapters 2 and 4, provide utility companies with a practical way to achieve success on expenditure, reliability, safety and environment.

1.2 Physical Asset Management for Electricity Transmission and

Distribution Business

1.2.1 Maintenance Management: State of the Art

Physical asset management, or asset management (AM) in short, is a field of emerging importance in the utility sector. Obviously, the development of AM differs between companies and countries, because TSO/DSO’s have their own specific set of business circumstances and technical capabilities. However, developments in AM do have some ideas in common, which are introduced below.

The managed objects are physical assets and asset systems. Distinguished from human, information, financial and intangible assets, physical assets are a subset of fixed assets [16]. They serve as the backbone of the daily operation within a capital intensive enterprise. In a power grid, physical assets include the visible machineries and constructions, such as lines, cables, switches and transformers, as well as the surrounding supportive items, such as sensors, meters, instruments, controllers, buildings or even clearings.

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5 Maintenance is a collection of the most discussed activities and practices within in the domain of AM. Historically, three major advancements have been achieved and applied in maintenance management, as Table 1.1 shows.

TABLE 1.1: MAINTENANCE MANAGEMENT HAS BECOME MORE PROACTIVE IN THE LAST DECADES, BUT IT STILL HAS GAPS FROM THE ASSET MANAGEMENT DEFINED IN PAS55

Origin >>>>>>>>>>>>>>>>>>>>>>>>> Future

CM TBM CBM RCM RBM AM in

PAS55 1. Managed tasks on assets

Replacement of components Yes Yes Yes Yes Yes Yes

Repair of components, and/or recovery

from faults Can be no Yes Yes Yes Yes Yes

Inspection, testing, diagnosis No Sometimes Yes Yes Yes Yes

Renewal, installation & disposal No No No No Seldom Yes

Design, capacity rating No No No No No Yes

2. Management activities at the operation level

Scheduling of tasks No Yes Yes Yes Yes Yes

Assess condition of individual asset No Partly Yes Yes Yes Yes Assess importance of individual asset No Sometimes Seldom Yes Yes Yes

Risk assessment on components No No No Partly Yes Yes

3. Relevant management activities at higher levels

Failure mode identification No Yes Yes Yes Yes Yes

FMECA No Sometimes Sometimes Yes Yes Yes

Analyze risks on reliability No No No Yes Yes Yes

Analyze risks on finance No No No No Yes Yes

Analyze risks on other business values No No No No Partly Yes

Involvement in investments decisions

made by asset manager and asset owner No No No No No Yes

Involvement in network planning

made by asset manager and asset owner No No No No No Partly

List of abbreviations in the heading row:

CM Corrective maintenance TBM Time-based maintenance CBM Condition-based maintenance RCM Reliability-centered maintenance RBM Risk-based maintenance

Legend for the shadings:

Activities added by preventive maintenance (PM)

Activities added by maintenance scheduling and maintenance strategies Activities added by risk analysis

Gap from asset management defined in PAS 55

The first advancement is the abandoning of “Corrective Maintenance” (CM). Maintenance was, originally and yet in many occasions, corrective. In CM, components

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are completely replaced after they fail. In contrast, “Preventive Maintenance” (PM), shown in all columns in Table 1.1 other than the “CM”, intend to recover a component from a faulty state through proper repair techniques. The purpose of PM is primarily to avoid major failures and consequent interruptions of routine operation. However, in many cases, the use of PM can lead to the extension of the service life of components.

The second advancement is the introduction of maintenance strategies. In current practice, a maintenance strategy is essentially a method to schedule individual PM tasks within an asset fleet. Proper scheduling controls repair costs significantly, because required resources such as spare parts, transportation services, work force or cash flow are usually lower in costs if arranged in advance rather than requested on site. A basic scheduling approach is ranking health condition indices estimated for individual components. If the index is derived from the usage history of each component, the maintenance strategy is called “Time-Based Maintenance” (TBM). If diagnostic information from inspections, tests and monitors are utilized to assess the index, the maintenance strategy is “Condition-Based Maintenance” (CBM).

The third advancement is the introduction of risk analysis. Generally speaking, risk is an approach to describe the potentiality of an incident such as failure of a certain component. In management [17], a risk is featured with its probability and consequence and rated with the expected value – the multiplying product of the probability and the consequence.

Maintenance scheduling can be achieved through risk analysis. Risk analysis on an individual component consists of estimating probability and consequences quantitatively. Firstly, the rough level of failure probability can be derived from the health condition index. Secondly, the failure consequence is measured in multiple items[18]. Traditionally in power grids, the network reliability, mainly measured by the customer minute loss of the blackout, is the only item. This is the Reliability-Centered Maintenance (RCM) strategy. Recently, additional aspects such as finance, safety and environment were added in response to regulations and stakeholder requirements. This extends RCM to a complete Risk-Based Maintenance strategy (RBM).

Risk analysis can be performed not only on components to implement a maintenance strategy, but also on asset systems to guide and select a maintenance strategy on its composing components. This technique is frequently referred as the

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7 failure mode, effect and criticality analysis (FMECA). In FMECA, the risk analysis is performed on a failure mode. A failure mode describes a function loss on asset systems of different scales, e.g. switchgear, bus, or complete substation [19, 20]. Failure modes are distinguished from each other according to their original faulty component and the physical degradation mechanism. After determining probability, consequence and risk analysis procedures, FMECA is capable to rank failure modes with their expected risk indices. Using this ranking, asset managers can adapt maintenance strategies to the failure mode it tackled. e.g. for a component with highly ranked failure modes, CM is abandoned, frequency of TBM is increased, proper diagnostic tools for CBM is introduced, etc.

RBM and FMECA represent the up-to-date advancement of AM in electricity transmission and distribution. This enables the utility company to optimize the

allocation of the maintenance budget [21].

1.2.2 Gap between Risk Based Maintenance and Physical Asset Management in Practice

PAS 55 - 2008, a widely accepted standard for asset management, defines asset

management (AM) as: systematic and coordinated activities and practices, through

which an organization optimally and sustainably manages its assets and asset systems, their associated performance, risks and expenditures over their life cycles for the purpose of achieving its organizational strategic plan

in which the “organizational strategic plan” (OSP) is: overall long-term plan for the

organization that is derived from, and embodies, its vision, mission, values, business policies, stakeholder requirements, objectives and the management of its risks.

Based on the PAS 55 definition, AM should be performed simultaneously at three levels[21]. In brief, the focuses of these levels are listed below:

•_{Operation level: Optimize life cycle activities on assets. This includes not} only the maintenance, but also the acquisition, installation, utilization, renewal and disposal, as Table 1.1 shows.

• Tactical level: Justify investment decisions on asset systems in compliance with OSP. Coordinate the investment plans with budget and other resources.

• Strategic level: Interpret the OSP’s into rules which can further guide the investment decisions at tactical level.

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Currently, most advancements in AM concentrate on the operation level. Moreover, many TSO/DSO’s have started to construct their risk-based AM regime, including RBM at the operation level and FMECA at the tactical level. Extensive activities, including life cycle optimization at the operational level (such as [22-25]) and investment decisions at the tactical level (such as [11, 26-29]) have been gradually introduced in specific cases to manage asset systems such as medium voltage cables, overhead lines and power transformers.

However, deep gaps exist between RBM and OSP’s in today’s AM practice. In our opinion, this gap results from the difficulty of the strategic level of AM in two situations: At the engineering side, the information exchanged between the maintenance management system at the operation level and the network planning at the tactical/strategic level, is limited to technical parameters such as reliability and ampacity. Today’s decision making methods are insufficient to assess the contribution of asset performance to stakeholder satisfaction or OSP’s. At the business side, stakeholder requirements, as the essential part of OSP, are often not (numerically) measurable or convertible as Section 1.1 mentioned. Thus very few of them can be quantified as performance indicators.

1.2.3 Relevance to Other Researches on Infrastructure Asset Management

Within the European Research Network for Strategic Engineering Asset Management (EURENSEAM), eight features has been summarized for infrastructure assets in [30]. We believe that seven of them are applicable to electricity transmission and distribution and illustrate some of them in this thesis.

_{Infrastructure assets have long life span (decades or even centuries as} Chapter 3 will show). The demand on infrastructure system can change during their life span.

 Different (technical and management) standards on assets are applied within the long life span. (Section 3.3 shows an example of data collection standards)

 Infrastructures are evolving systems.

_{Infrastructure assets are distributed widely. They are penetrating or even} structuring the public domain.

_{Unlike industrial assets, very few infrastructure assets have a resale value.} _{The owner of infrastructure is not the same entity as the user, which leads}

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9  Infrastructure assets are anonymous, which means there is little control on how they are used. (The ampacity of cables discussed in Section 5.3.4 and 6.3 is an example)

According to our knowledge on high-voltage assets, we would like to add three features which distinguish high-voltage assets from other infrastructure assets, or non-electrical assets in electricity grids.

Firstly, the complexity of physical phenomena in high-voltage assets is higher than those in most mechanical or civil infrastructure, such as roads, dams, bridges, etc. The design of a high-voltage asset is considering thermal, mechanical and chemical phenomena as in civil infrastructure assets, as well as electrical phenomena which are seldom considered in civil infrastructure assets. See the summary of “aging mechanism” in Section 4.1 for details. Since electrical phenomena, as a late branch of physics, is still poorly understood as we will show in Section 3.4, many behaviors of high-voltage assets are currently beyond current human knowledge. In this sense, there is also a large uncertainty when high-voltage assets are managed.

Secondly, the cost and effort of acquiring decision support data can reach a very high level, which needs to be controlled by asset managers. Specifically, technical states of electrical infrastructure are changing in a time scale down to milliseconds, which is much faster than mechanical and civil structures. Sensors of such high sensitivity and reasonable reliability are costly not only to purchase but also to develop. Besides, the disturbance of data acquisition devices and activates to normal operation is a main non-economic barrier of large scale application of condition diagnosis.

Thirdly, there are many maintenance method which plays important role, even at the strategic level. Section 6.4.2 will show their contribution to extending lifetime or enlarging capacity of cables. Moreover, it is vital to consider the convenience of maintenance and the integration of sensors within the design. In this sense, the design and the maintenance planning should not be separated as they are in the financial reports. In this sense, we have reservations about one feature of infrastructure assets in [30].

 Infrastructure assets are “passive elements”. They need little attention to function. Their performance and risks are exclusively determined in design.

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The above features of electrical infrastructures explain our observation below. Decision-support data, or even the technologies to acquire them, are frequently not yet ready to be used in today’s TSO’s and DSO’s, due to the high complexity of electrical systems and high cost of applying sensors. This prevents most data-driven decision-making methods, such as those introduced by Morgan[31], from being applied. In reverse, asset manager should be able to specify the content and volume of data necessary to optimize investments on high-voltage assets, when there is little information or knowledge about technical risks.

Therefore, within the area of infrastructure AM, this thesis will concentrate on how to make decisions (1) with little data support and (2) on the roadmaps to build data acquisition systems. The decision-making method is relevant to the following models of infrastructure AM which are summarized by Herder and Wijnia [30].

•_{When regarding infrastructures as socio-technical systems, our research is} applicable to technical subsystem in the time scale of several years to several decades.

•_{Within the process hierarchy of risk-based AM introduced by Wijnia [32],} our research can be applied in the step “risk assessment” under the theme “managing asset base”.

• “Real option” is an advanced decision-making method which models engineering solutions to risks as “options”, e.g. in [33]. In contrast, our focus is how to develop data acquisition systems which realize these “options”. Thus, we expect that our decision-making method is applied prior to “real options” and benefit it through stimulating the developments of engineering “options”.

1.3 Aim of this Thesis

The main scientific goal of this thesis is to understand the limits of the risk-based approach to identify the long-term risks of owning electrical infrastructure assets, and to develop new methodologies to manage these risks. These new

methodologies can be applied by asset managers in electricity transmission and distribution sector at the strategic level to realize their organizational strategic plans.

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11 In today’s AM, the mainstream decision making method is risk-based, i.e. based on quantitative risk analysis. Many asset managers in TSO/DSO’s believe that: through applying the risk-based AM, their business can satisfy the majority of, if not all, requirements of stakeholders in the future.

However, from the gap between RBM and OSP, we learned that the risk-based approach can be not effective in two situations.

(1) Infrastructure assets are so long-living that acquiring data and knowledge on the technically-oriented risks is also a long-term process. In the first few decades of risk management, the data and knowledge are often insufficient to support the risk assessment or risk-based decision making.

(2) The long-term, business oriented risks cannot be quantitatively assessed with stochastic models, because they have characteristics that technically oriented risks do not have. As a result, the investments to control strategic risks cannot be optimized. These risks should be modelled, for example, as “strategic risks” in Chapter 5.

Corresponding to the above two situations, this research will be performed in two steps.

In the first step of this research, the investigated risks are normal, i.e. triggered technically by incidents on assets, especially aging and failures. The first step aims to improve the risk-based AM, regarding its capabilities to cope with insufficient knowledge and data described in situation (1). The improvements can be divided into three parts.

1. At the tactical level: Two new statistical life data analysis methods are

introduced, for estimating the reliability of a population of assets from “incomplete data”, i.e. partly missing data and data, and “small data”, i.e. data sets of small sample sizes. It aims to solve the first scientific challenge to this thesis: to reduce the bias caused by missing data and to quantify the effect of small sample sizes as confidence bounds.

2. At the operational and tactical level: The reliability data model. It

identifies four categories of reliability data, and reveals their roles in evaluating technically oriented risks.

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3. At the tactical and strategic level: The knowledge maturity model for

maintenance management. It is developed for the asset manager to evaluate his maintenance scheduling and planning processes, regarding the extent to which they realize the risk-based maintenance. Evaluating the knowledge maturity can belong to the strategic level, since it ensures the capabilities to manage technically oriented risks in long-term.

Through studying the based AM regime in the first step, we learned that: risk-based AM assumes that the causal chain of any risks are straightforward and can be quantified with probabilistic models. However, most strategic risks are not reoccurring probabilistic events as technically oriented risks. Their causal chains do not satsify the condition to apply risk-based AM.

In the second step of this research, the strategic risks will be investigated, not according to their probability and consequences, but according to the characteristics of their causal chains. The purpose is to formulate a new AM regime at the strategic level of AM from three aspects below.

4. As the second scientific challenge to this thesis: Define the strategic risks, according to the characteristics of their causal chains which

distinguish them from technically-oriented risks. Specifically, a decision tree will be developed to classify the strategic risks according to their characteristics into six types.

5. As the third scientific challenge to this thesis: Define the three categories of AM strategies, according to the ways they guide the

investments at the tactical-level of AM. In our model, each category of AM strategy is designed as the solution to two categories of strategic risks. 6. As the fourth scientific challenge to this thesis: Establish a method to

evaluate the AM strategies regarding their effects of realizing OSP’s and correspond to the strategic risks. The method will model a mesh of

causal links in which multiple stakeholders interact with each other. Through applying our method, multiple AM strategies can be coordinated, so that multiple stakeholders can be satisfied at the same time.

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1.4 Layout of this Thesis

Following the storyline in Section 1.3, we divide the remaining part of this thesis (excluding conclusions) into five chapters.

Chapter 2 introduces the triple-level AM model. In this chapter, the focus will be the definitions of the three levels and the boundaries between them. Moreover, the terminologies from PAS 55 are made for all capital-intensive sectors. We will adapt them into the electricity sector.

According to Chapter 2, the technically oriented risks and business-oriented risks should respectively be managed at the tactical level and the strategic level. Chapter 3 and 4 will investigate technically oriented risks in the regime of risk-based AM, while Chapter 5 and 6 will establish a new regime of strategic AM to manage business oriented risks.

Chapter 3 develops new statistical methods for analyzing life data in two situations. The first situation is “incomplete data”, i.e. when failure and installation data are missing in the early stage of asset life cycle. The second situation is “small data”, i.e. when a limited number of specimens are tested. As a result, the probabilistic model for service failure data and accelerated life test data are extended respectively with

(1) iterative approximation-maximization method for incomplete data analysis, and (2) inverse power-Weibull model for accelerated life data analysis methods

as the first major contribution of this thesis.

Chapter 4 discusses how reliability data can support risk assessments at the operational and tactical level. The discussion is performed in three steps.

Firstly, we establish the reliability data model. The model divides reliability data into four categories according to how they are acquired from the field. In the model, we show that these data categories are available at different stages when the asset manager gradually introduces risk analysis as his decision-making method.

Secondly, we describe the condition-based, reliability-centered and risk-based methods for making decisions on maintenance schedules and plans. The description on each method emphasizes how much reliability data is required to support a decision.

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Finally, we combine the reliability data model and the decision-making methods to build a knowledge maturity model for maintenance management, as the second major contribution of this thesis. The “maturity” is a score provided to each decision-making method. It evaluates how much the decision-making is in line with the risk-based AM strategy. The word “knowledge” emphasizes that the availability of reliability data is the main obstacle of achieving a higher maturity level. Hence, the knowledge maturity model provides a roadmap to evolve towards the risk-based maintenance through accumulating data and knowledge.

Chapter 5 studies the properties of and the solutions to the strategic risks. We reveal how the strategic risks can be distinguished from the “normal risks”, i.e. the short to mid-term, technically-oriented risks studied at the tactical level. Through checking these differences, the strategic risks can be further classified in the decision tree we developed in Section 5.2.

Based on the classification of strategic risks, we define three types of AM strategies, as the third major contribution of this thesis. They are risk-based, precaution-based and information AM strategies, which are introduced respectively in Section 5.3, 5.4 and 5.5. Each is the solution to two types of strategic risks.

Chapter 6 develops a new method called system diagram for making decisions on AM strategies at the strategic level. This is the fourth major contribution of this thesis.

Strategic risks are studied individually in Chapter 5. In contrast, several strategic risks on the same asset population are studied together in the “system diagram” in Chapter 6. This “system” refers not to the power system, but to the asset management system. Our “system diagram” qualitatively models the interdependency among strategic risks. In Section 6.2, we develop rules to identify critical patterns in system diagram and recommend how AM strategies can be introduced to deal with these patterns.

In addition, we also embed the knowledge maturity model developed in Chapter 4 into the system diagram. Through following the rules we designed for this embedment, multiple AM strategies can be coordinated with each other. As a whole, our system diagram can serve as a tool to find out a solution to multiple interdependent strategic risks stimulated by multiple stakeholders.

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15 Medium-voltage cables are used as the case to illustrate our methods and models developed in Chapter 5 and 6. Specifically, Section 5.3.4, 5.4.2 and 5.5.3 introduces respectively the precaution-based AM strategy, the risk-based AM strategy and the information strategy on medium-voltage cables. The complete system diagram approach developed in Section 6.2 is applied on medium-voltage cables in Section 6.3.

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Chapter 2 _{Physical Asset Management}

In the TSO and DSO’s, asset management started from implementing engineering maintenance activities. After its development in the 2000’s, asset management is currently approaching the stage where it links the societal OSP’s (see Section 1.2.2) with engineering activities. This linkage will be formalized by applying the triple level model of asset management systems in Section 2.1. This is the first part of this chapter.

The second part of this chapter introduces the terminology of asset management. Researches in the last decade show that asset management is more and more a multidisciplinary field. The terms used in interdisciplinary subjects, such as asset systems, risk management and strategies, are differently interpreted by researchers with either societal or technical knowledge backgrounds. Therefore, in Section 2.2, the terms defined in PAS 55 are explained with concrete technical items or practical management processes in the electricity sector.

The introduction of this terminology will further reveal the position of risk management in future asset management, which is discussed in Section 2.3. The emerging risk-based decision-making is needed to realize the goal of tactical-level asset management. In Chapter 3 and 4, the data and knowledge supporting risk-based decision-making will be modelled.

Nevertheless, the risk matrix which directs the decisions is only one part of the strategic level of asset management. Section 2.4 shows the other features of the strategic level through comparison with the tactical level. This lays the foundation for further developments of the strategic level in Chapter 5 and 6.

2.1 Triple Level Model for Future Asset Management

Asset management (AM) has been historically developed from maintenance activities and reliability engineering. Currently in practice, the development has reached the level of RBM and FMECA, as introduced in Chapter 1.

Managing Risks in Electrical Infrastructure Assets from a Strategic Perspective

M

ANAGING

R

ISKS IN

E

LECTRICAL

I

NFRASTRUCTURE

A

SSETS

FROM A

S

TRATEGIC

P

ERSPECTIVE

Qikai ZHUANG

November 2015

M

ANAGING

R

ISKS IN

E

LECTRICAL

I

NFRASTRUCTURE

A

SSETS

FROM A

S

TRATEGIC

P

ERSPECTIVE

Qikai ZHUANG

This book is dedicated to my parents

Zhimin Zhuang and Xueqin Cao

谨以此书献给我的父母

Summary

Samenvatting

Table of Contents

List of Tables

List of Figures

Chapter 1

Introduction

1.1

Electricity Transmission and Distribution Business

1.2

Physical Asset Management for Electricity Transmission and

Distribution Business

1.3

Aim of this Thesis

1.4

Layout of this Thesis

Chapter 2

Physical Asset Management

2.1

Triple Level Model for Future Asset Management

_Introduction

_{Physical Asset Management}