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B3-209 21, rue d'Artois, F-75008 Paris http://www.cigre.org Session 2004 © CIGRÉ Knowledge Base Approach in Relation to Risk Management of Distribution and Transmission Assets PH.WESTER∗ NUON E.GULSKI TU Delft THE NETHERLANDS E.R.S.GROOT NUON I.RING ESBI IRELAND Present asset management processes are going in the direction of risk management. Risk management expresses the relation between consequences/acceptance in function of probability of occurrence of supply failures. The translation from accepted risk levels into activities necessary to maintain the supply function through the upkeep of the asset performance is thus of extreme importance. Methods to measure the quality/performance of assets strongly depend on the information with respect to the specific asset type. This would be maximal if the relevant data per asset type is shared by the owners. Application of data-mining techniques are than practiced to gain knowledge from a growing amount of asset type information in order to interpret future asset behaviour and performance properly. Key-words: Asset management – Risk strategies – Knowledge sharing – Data mining – Performance measurement 1. INTRODUCTION The asset management strategy promises to achieve the optimum balance between investment return and other stakeholders’ values as availability and environmental load. Risk decisions are based upon, at the one hand, the judgement of the acceptable risk and at the other the (assumed or predicted) performance of the asset “string” concerned. To be informed about the related assets performances condition based maintenance is applied intensively. A FMECA (failure mode effect and criticality analysis) based approach that, if designed correctly, supports a standardized and human independent environment for the execution of condition measurements and maintenance. The approach also provides data that, if analysed properly, give insight in future asset performance. The Delft University of Technology, High Voltage and Management group together with her partner InfraCore designed an expert system capable of analysing condition data. It is obvious that maximization of asset type data population strongly improves the quality of the analysis mentioned. The designed expert system has this capability of gathering a large amount of (measured or registered condition and fault data representing the actual or historic performance of the asset concerned on a type and piece basis. In order to maximize the relevant data a business model to create an environment where other utilities support by the disposal of relevant data was implemented and checked via a world wide executed survey. Stored historic and type data are run through a data mining process thus generating new knowledge. The approach can as such be considered a knowledge based, even ISO certificated, system supporting the risk analysis process of asset management divisions. 2. RISK MANAGEMENT MAINTENANCE Risk management is generally characterized by assessing the consequences for all stakeholders, not only the more result driven shareholder. The secret of a well designed and applied risk management process lies in finding the optimal balance of value added versus risk taken between shareholders, regulation, employees, consumers and society. It will thus depend on decisions upon defining the ∗ Ph.Wester, NUON, [email protected] results in financial, quality, environmental and strategic point of view. Compared to the often used concept of reliability centered maintenance (RCM), risk management also means a process of a continuous improvement cycle. Based upon the risk analysis, control measures, execution of actions and evaluation and actualization improvement steps are taken in a more or less continuous process. It implies that decisions are not only based upon realizing the reliability level agreed or profit promised but that all aspects are judged including the measures that limit the risk relevant for a combination of economical, technical, environmental, customer and employee values. impact assessment 3. RELIABILITY OF POWER SYSTEMS As the issue of risk management is a constant and complex theme for a transmission grid asset manager. The Cigre brochure on principles for risk assessments [1] poses that the best way to manage is the separation of the observations and studies into a system approach and a component approach with reliability management as linking process. This approach eases the handling of data, distinguishes between the “system risk” and the “asset failure mode” part and guarantees the interaction between supply demand and decisions regarding executing of (maintenance) activities. Cigre JWG B3/C2-14 shows in her publication on “Information strategy for asset management” [2] that the asset manager basically applies a step by step decision process as shown in figure 1. The decision process again is separated into a (network) risk and a (component) condition orientation with Decision corporate level reliability management as the link between both. Risk management Based on the information available system level (external, financial, asset information Safety Reliability management source) different “impact” assessments Social asset level System are executed. The result: an optimum balancing of the different variables and S3 Risk assessment Condition assessment alternatives, leading to the best solution given the situation. Reliability management aims to Societal information Financial information Asset information identify the processes of degradation, while describing and quantifying their effects on the reliability of the figure 1: risk decision process JWG B3/C2-14 components of a system. It is, in other words, an approach to set the (expected) condition of the system elements into a financial consequence setting. Too weak results can concede into a change in design of the system as well as a replacement of a component or another maintenance interval applied. Taking into account societal information one can ultimately speak from risk management. Items like environmental and personnel safety as well as consequences at social/strategical level is taken into account at this level. The described impact assessment approach necessitates three elementary types of information: 1. feedback from corporate level: analysis reports of effects applied investment and upkeep activities on corporate “strategic” level related to stakeholder satisfaction 2. feedback from system level: information on interruptions, quality of the supply to customers, reports on environmental consequences, energy not supplied 3. feedback from component level: information on equipment failures and measured condition, in order to maintain the network in good operating condition. 4. RISK ASSESSMENT APPROACH Where figure 1 covers a more or less theoretic model of a risk decision process a more practical format for risk assessment is visualized in figure 2. Both the risks accepted at network level as the performance at component level are addressed. The left matrix can be considered as representing the consequences of a certain risk at network level, the right as the related activity depending on the performance of the asset or circuit concerned. The two matrices are linked via the consequence judgement “vital”, “critical” or “non-critical”. 2 figure 2: Matrices visualizing both the risk accepted at network level and the performance at component level. The left matrix shows the probability of failures through the assessment of susceptability of a circuit. Subjects like aggressive soil, distance to sea, influence of sand or ice, age of the circuit, redundancy etc. are covered. By measuring this susceptability against classified consequences for (all) stakeholders including penalties and constraint costs a non-critical, critical or vital situation is judged and transponded to the risk axis of the right positioned matrix. Obviously decisions regarding maintenance and/or re-investment priorities are consequences this judgement process. In line with the previously mentioned Cigre publications the left hand matrix could be regarded as risk analysis connected to the right, asset directed matrix through reliability management. The basic idea: translate the network failure consequences for stakeholders into decisions at component level 5. DATA MAXIMIZATION AND OPTIMIZATION As shown in chapter 4 it is clear that data regarding the performance of assets/components are of extreme importance for realizing the best possible decisions in a risk strategy. Apart from the information regarding the consequences of a system failure it is the only controllable and steering variable in the decision approach. An important difference with the system allowable risks is that performance information is not only depending on the local situation. One can use information from other assets as well, especially those assets of the same type and operational requirements, thus decreasing the subjectivity part of this part of the decision process. A maximum objective decision regarding asset performance is supported by broadly collected asset condition information, analysed and translated into knowledge. For this reason the initiators of the iCore data bank stared a knowledge platform (KSANDR, knowledge sharing and research) with the pure aim to store a maximum amount of measured condition data for analysis purposes. [6] By making the platform and the database accessible for all interested participants responsible for the management of maintenance and investment of high- and medium voltage equipment it is the intention to maximize the relevant condition and performance data. As a consequence a far better judgment could be taken by the participants with regard to risk based prioritization of maintenance. As shown in figure 3 and stated earlier, unlike the local judgment process regarding acceptable risks (expected) asset performance can be objectified by the maximization and sharing of condition data. Chapter 6 describes the further possibilities that this approach is given to the data mining process resulting in new expert rules and efficiency increase due to experienced correlations. 3 at disposal for other utilities to maximize data population and improve decisions new, independent, non for profit organization operates platform The ultimate goal: combining all practical, design, scientific know-how and measurement results in a data-base, thus creating an independent knowledge platform in the field of medium- and high voltage equipment. Figure 3: knowledge sharing platform growing knowledge, applied analysis i- Data sources 6. DATA MINING AND DECISION SUPPORT In general the data mining involves the earlier stages of data selection and data transformation and the subsequent stages of validation and interpretation [4]. Furthermore data mining aims to provide an alternative to the traditional scientific method, where data analysis is largely directed by hypothesis and theory. The aim of data mining is to find intelligible patterns which are not predicted by established theory [6]. Formatting the output data in a visual form that human intelligence can interpret is important. In particular evaluation existing experiences in data mining applications for condition monitoring, including validity and relevance of results is of importance [4]. In the next sections, use of partial discharge data of (A) Knowledge power cables is discussed in the scope of these two Failure rules data aspects. Partial discharges, because of their wide variation with equipment type, and with electrical Condition configuration and material type, provide a very Based Component Data-mining (B) suitable vehicle for detailed data mining Maintenance data approach Activities applications. In addition to the example given relating to cables, partial discharges can be used as Condition the basis for data mining applications. Norms & data criteria (C) The full potential of condition and maintenance information cannot always be realized by using traditional techniques of data handling and analysis. There are often underlying trends or features of the data that are not evident from the usual analysis techniques. Such detail and trends can be important for the assessment of the equipment operation and there are increasing demands from operators and asset managers to fully exploit the capabilities of this data in order to optimise the utilization of high voltage electrical plant. The method of extracting full value from such extensive databases, using new analysis techniques, is commonly called data mining [6]. In its basic essence, data mining is the application of relatively novel datadriven approaches to find patterns in data obtained from electrical equipment. The data mining techniques are then used to relate these patterns to the operational condition of the equipment and to provide new knowledge about aging mechanisms, norms, and required maintenance activities. In order to support the data mining process utility may apply the knowledge expert database in which all the measured data and failure information is stored and analysed. The general features of this approach are shown in figure 4. This process provides three outcomes (A, B and C) of the data mining approach. Outcome (A) refers to new knowledge about aging mechanisms of power cable components and outcome (B) to recommended maintenance activities on power cable components, resulting from the database analysis. Due to the large amount of measurement data stored in the software data system, operating norms and criteria are continuously updated and fed back to workers in the field as determined by result (C). Figure 4: Schematic structure of data-mining process for condition-based maintenance of HV assets, e.g. power cables. 4 6.1 Examples of Insulation Condition Knowledge Rules In [2] schematic example of interpretation rules for PD measurements on power cables are shown. These are rules of thumbs supporting the analysis of the measurement results from a cable section. Different aspects of the ‘fingerprint’ are used in these interpretation rules. After measuring and analysing PD activity in a cable section, the second step is to make a decision on the insulation condition of the tested cable sample. Using the measured PD quantities and their interpretation rules (3) three condition classes can be derived from the analysis: • cable section NOT OK: weak spot in the cable section should be replaced; • cable section NOT OK?: trending on the cable component is required (e.g. 1 year 3 years); • cable section OK: no weak spots in the cable section, cable section is OK. Analysing the derived measurement data through a decision diagram, the cable systems insulation condition can be determined in one of the four classes. The decision diagram is Table 3: Indication of some typical PD levels for based on all PD quantities for measurements different types of cable insulation and accessories on power cables. Not concentrated PD in the cable insulation is not an indication of aging based on experiences from the Netherlands. of the cable insulation materials. For PILC Type Trend values Cable related insulation, certain levels of PD can be element accepted in the cable insulation, joint and PILC 10.000pC insulation termination, depending on the design of the XLPE <20 pC component. Also, XLPE related accessories Oil-joint 5.000pC splices could stand certain PD levels (generally lower Type 1(resin 500pC pC levels compared to PILC). However, insulation (asymmetric) XLPE cable insulation is required to be PD (oil insulation) >10.000 pC free (PDIV>1.3U0, PDEV>1.1U0), where the (oil/resin 5.000 pC background noise during the measurement is insulation) not allowed to exceed 20pC. The typical PD Type 2 (resin 4.000 pC values for the different components can be insulation) derived from the statistical analysis of all 6.000 pC terminations Oil-termination required measurement data in a database. As a Dry termination 3.500 pC result for particular components threshold Type 3 250 pC values can be derived to support the CBM, see termination table 3 6.2. examples of data mining approach and norms Statistical evaluations of diagnostic parameters e.g. PD level at the service voltage or PD inception voltage (PDIV) are powerful means to determine the component typical values and the statistical significance limits. Based on these parameters norms can be estimated. Based on a large population of measuring data as obtained on one type of cable accessory the average PD level and the 95% significant limits can be calculated. In figure 5 for two different types of splice insulation the population of PD level at U0 is shown. It follows from these examples that for resin insulated splices the average PD level is 2.4 nC and for oil filed splices is 3.6 nC. Respectively, the significance limits are 4.7nC for resin-insulated splices and 8.2nC for oil filed splices. As a result of the above mentioned examples it can be concluded that using large population of measuring data, database selection of different insulation types result in sensitive distinction in threshold values for decision support. 6.3. examples of separation of construction effects and aging Using large populations of PILC power cables and PD diagnosis provides support in separating the aging processes and effects of different power cable insulation types. As known, a PILC power cable is not always PD free and a certain level in the range of nC’s of PD activity is accepted. Figure 6 shows an example of PD amplitudes as detected at U0 on a large population of PILC cables. It follows from this figure that at U0 (service voltage) the typical (average) 5 -PD amplitude in PILC power cable insulation is in the range of 4.7 nC. As a result, cable section with PD larger as 12.9 nC are significantly different from the cable population. It has been observed that in contrast to mineral oil impregnated insulation in the case of synthetic oil impregnated cables (manufactured after 1980) due to temperature/pressure effects (T/P) (load changes or switching off the cable section) result in additional PD activities [3]. Large discharge amplitudes characterize these PD processes and they are of temporary art and they do not indicate insulation degradation. These situation forces to take during data evaluating and condition assessment into account the construction aspects, e.g. type of insulation impregnation. As a result, selecting in the database the cables influenced by T/P effects shows that the typical parameters like the average PD and the significance levels are changing. (a) (b ) Figure 5: PD levels as observed at U0 for PILC database population of two different type of cable splices; (a) splice type resin insulated with the norm 4.7nC; (b) splice type oil filed with the norm 8.2 nC (a) (b) Figure 6: Database selection of all PILC power cables showing PD activity at U0 level: (a) definition of the database filter; (b) population of cable section and the PD levels; norm 12.9nC Figure 7 shows an example of the database selection of PD amplitudes as detected at U0 on a large population of PILC cable only where the T/P effects have been observed. It follows from this analysis that at U0 (service voltage) the typical (average) PD amplitude in PILC power cable insulation is in the range of 9,3 nC. As a result, cable section with PD larger as 18 nC are significantly different from the cable population. In order to obtain typical PD amplitudes in relation to insulation degradation, those cables where no TP effect is measured should be obtained. Figure 8 shows an example of the database selection of PD amplitudes as detected at U0 on a large population of PILC cable, where the cable sections the T/P effects are left out the observation. It follows from this analysis that at U0 (service voltage) the typical (average) PD amplitude in a PILC power cable insulation is in the range of 3,7 nC. As a result, cable section with PD larger as 11 nC are significantly different from the cable population. From these examples, it follows that as a result of T/P effects, the norm level is influenced. If all PILC insulation is used for norm calculations, the norm would be 12nC. The right norm level should be 11nC, obtained by filtering only the degradation effects. This example shows that results of statistical analysis are sensitive to different construction parameters. As a result, condition assessment and decision support need data-mining support, see figure 4. 6 (a) (a ) (b) Figure 7: Database selection of those PILC power cables showing PD activity at U0 level, where the temperature/pressure effects have been observed; (a) definition of the database filter; (b) population of cable sections and the PD levels; norm 18nC. 7. (b ) Figure 8: Database selection of those power cables showing PD activity at U0 level where the temperature/pressure effects are not observed; (a) definition of the database filter; (b) population of cable sections and the PD levels; norm 11 nC EXAMPLE OF DECISION SUPPORT Figure 9: Example of using knowledge rules and database to support the decision process of asset managers in making distinction between cable networks with different insulation conditions; the reed marked cable section shows increased insulation degradation as defined by the knowledge rules. Combining the component data with the condition data as obtained using diagnostic inspections can be used to develop decision support for asset management. In figure 9 two examples are shown of such decision support. Based on the following information: PD parameters as detected for the particular cable type, significance limits (norms) of those cable section can be selected (marked in red) from the whole cable population where statistical deviating condition is observed. Conform the figures 1 and 2 of this paper this technical information now can be further combined to economic and strategic decision processes as defined by the asset management of the power utility. 7 8. CONCLUSIONS Cost pressure on utilities’ will force in the near future the asset managers to apply risk strategies, often resulting in more or less sophisticated condition based maintenance activities. Data resulting from these condition measurements would be far better applicable for decision purposes if analysed against a larger asset data population through an overall data mining analysis process. Risk management processes are based upon the relation between risk acceptance estimation at system level, being a decision fully depending on “subjective” local circumstances, and “objectified” knowledge about (expected) asset performance. A possible approach to find this relation between network risk and asset directed activities is set by applying a matrix approach. Stakeholders’ values are assessed against the susceptibility for failures at circuit/system level and transposed to the relation where the asset condition is investigated. The latter judgement is strongly influenced by asset type based knowledge that can be generated if fed by condition information from a maximum number of users of the specific asset type. A generic applicable software system should be used to gather and store a maximum of condition data providing there would be an independent operating organisation that offers the facilities for such a platform. Such an independent platform does exist nowadays supporting real knowledge base approach for risk management of distribution and transmission assets. The application of data mining and analysis techniques on large amounts of data that this platform is able to generate offers many possibilities for a better judgement of maintenance and investment priorities and actions related to real practical risk management processes. References [1] [2] [3] [4] [5] [6] [7] [8] [8] [9] Cigre JTF 23/12/13/21/22-16 “General overview on experience feedback methods in the field of electrical equipment, Cigre Brochure 211 Cigre JWG B3/C2 “Maintenance & Reliability” TF03, “Information strategy to support utility asset management”, Ekectrano.207, April 2003 E. Gulski, F.J. Wester, W. Boone, N. van Schaik, E.F. Steennis, E.R.S. Groot, J. Pellis, B.J. Grotenhuis, ‘’Knowledge Rules Support for CBM of Power Cable Circuits’’ Cigre Paris 2002, SC 15 paper 104 J. McGrail, , E. Gulski, E. R. S. Groot, D. Allan, D. Birtwhistle, T.R. Blackburn, “Datamining techniques to assess the condition of high voltage electrical plant” Cigré Paris, WG15.11 paper, 2002 J.J. Smit, J.A.W. de Croon, E.R.S. Groot, Y.F. Fu, E. Gulski, W.R. Rutgers, H.F.A. Verhaart and Ph. Wester “Decision making experience with maintenance diagnosis of high voltage equipment”, 37th Cigre session, Paris 1998, paper 15-105. B. Quak, E. Gulski, J.J. Smit, ‘’Knowledge generation aspects to support AM decisions’’, ERA Conference on Engineering Asset Management, 5-6 Nov. 2002 London, UK F. J. Wester, E. Gulski, J.J. Smit, ‘’CBM of MV power cable systems on the base of advanced PD diagnosis’’, presented at the 16th Int. Conf. on Electricity Distribution, CIRED 2001, Amsterdam, The Netherlands, paper No. 1.29 Ph Wester, J.J. Smit, J.J. Oestergaard, J. Corbett “Development of Asset Management Services”, Cigre session, Paris 2002. E.Gulski, R.Wester, Ph.Wester, E.Groot, survey based “sharing asset condition data and performance knowledge”, ISH/Cigre colloquium, Delft, Aug.2003 B. Quak, E. Gulski, J.J. Smit, ‘’Knowledge generation aspects to support AM decisions’’, ERA Conference on Engineering Asset Management, 5-6 Nov. 2002 London, UK 8