Imran Zulquifal Mamade

Transcription

1 A METHODOLOGY TOWARDS CONDITION-BASED ASSET MANAGEMENT Imran Zulquifal Mamade Instituto Superior Técnico, Lisboa, Portugal, 2016 Abstract: Back on the 1970s, electric utilities were vertically integrated monopolies responsible for the process of generation, transmission and distribution. Load rates were increasing annually and utilities were expanding their grids to achieve high levels of performance. However, today s power systems in developed countries are characterized by a plateau in load growth (due to global economic slowdown and energy efficiency measures) and an already mature stage of electricity decarbonization. As a result, grid companies such as transmission system operators (TSO) are witnessing lower levels of investment in grid expansion and start to focus their effort in asset management (due to grid ageing). The goal of the current research work is to develop an asset condition index to be applied in power transmission grids, and an innovative condition forecasting methodology that enhances the decisionmaking process in maintenance and asset replacement. This condition index is based on a weighted sum model that takes into account state-of-the-art methodologies regarding the physical asset chosen power transformers (PT). The condition of power transformers has a significant impact on the operation reliability of the electrical power grid. It is also well known that the PT have the single highest value of the equipment installed in high-voltage substation. A condition index for the PT is proposed taking into consideration four factors. Firstly, the PT condition is explored through a series of tests performed to the physical asset. These tests may be from chemical, electrical and thermal nature. Age is also considered since it s an important factor for several physical assets, in particular for the Power Transformer. Performance and operational context of the PT examined and allow a whole different range of analysis on the physical asset. The PT condition forecasting methodology is also proposed considering the prediction of the abovementioned factors. The forecasting method varies between the factors considered. A tool for learning dynamic Bayesian networks is presented to forecast the state while another approach is suggested for the performance particular. Keywords: Asset Management, Power Transformer, Condition Index, Condition Forecasting, Dissolved Gas Analysis, Dynamic Bayesian Networks. 1 INTRODUCTION Power transformers are one of the most important physical asset classes in substations and have a meaningful role in the reliability of electrical systems. This leads to a special attention by asset managers to the operational behavior of power transformers as its repair or replacement is costly and time-consuming [1]. The development of approaches that allow the condition evaluation of the power transformer in terms of condition variables (e.g. age, furfuraldehyde concentration in the insulation oil, power factor, mineral oil breakdown voltage) is essential for an optimized management and operation of the Power Transformers in the electrical Transmission Network and, consequently, the network itself. For instance, the furfuraldehyde concentration in the insulating oil is important to analyze the deterioration of the insulation paper of the PT; the power factor is important to estimate the power losses in the bushings which are directly related to the performance of this component that has a considerable cut in the failure 1

2 statistics of different transformer components reported by CIGRE. Figure 1 shows the results previously mentioned. Figure 1 Failure statistics of different transformer components reported by CIGRE [2] The regulatory authorities for the electric utilities throughout the world are becoming stricter when it comes to the performance, availability and investment on the electric grids. Therefore, building a condition index that helps electric utilities to evaluate the current and forecasted condition of its PT taking into account the result of several tests performed as well as the age, performance and operational context of this physical asset certainly represents a helpful tool for a wiser decisionmaking when considering maintenance and replacement actions [3]. 2 Maintenance Policies In the course of this work, several maintenance policies were analyzed and briefly described in order to understand the current practices and specific knowledge under this topic. Two different maintenance natures can be highlighted: reactive and preventive maintenance. Reactive maintenance concerns the response to faults occurred in a physical asset or equipment independently of its gravity or detection mode. As suggested by the term reactive, no action is taken to prevent the occurrence of failures and this maintenance s goal is to restore the operational functions performed by a certain physical asset. On the other hand, preventive maintenance concerns failure prevention mechanisms resulting from the deterioration of the physical asset. Two major divisions are suggested: systematic and conditioned preventive maintenance. Under systematic preventive maintenance, time based maintenance is highlighted and in terms of condition preventive maintenance the three major topics are introduced: condition based, reliability centered and risk based maintenance. As a result of the maintenance policies study, one has become aware of the advantages and disadvantages of each maintenance policy, as well as the principles related to each policy. 3 Condition assessment 3.1 General methodology For condition assessment, important particulars that generally account the condition of PT as a physical asset need to be identified and quantified. Therefore, calculating an index or a set of indicators that reflect, with good approximation, the current state of the PT if of upmost importance. 2

3 Four main attributes should be considered when assessing the PT s condition: Health indicator a " Age a # Performance indicator a $ Operational indicator a % 3.2 Health Indicator a s The asset s health concerns an approximate measure of the internal and individual condition of the PT itself obtained by analyzing a set of inspections and tests performed on the PT. Thereby, several tests of electrical, chemical and thermal nature were analyzed in order to acknowledge the behavior of the PT from different outlooks that influence the overall asset s health. Eight important tests following the referred premise were and identified and consequently analyzed: 2-Furfural measurement on oil Dissolved gas analysis (DGA) Bushing power factor Oil temperature difference Static resistance measurement Polarization index Oil quality analysis On-load tap changer DGA For all the above mentioned tests an evaluation mechanism was developed taking into account the determining state variables and their valuebased meaning individually. The developed work under the health index is partially presented next Furfural measurement on oil The power transformer condition is strongly influenced by the state of the mechanical strength of the insulating paper. This insulating paper is present in the transformer windings and its resistance can be estimated by analyzing the degree of polymerization (DP) of the paper. Studies revealed a strong and stable correlation between DP and 2-Furfural (2-FAL). Consequently, the 2-FAL concentration method has been broadly adopted by several utilities in order to infer the strength of insulating paper of their PT. Chendong, Burton and several other investigators proposed a direct correlation between DP and the 2-FAL concentration in the insulating oil. In this work, we consider the Chendong relation since lower DP levels are reached sooner as the 2-FAL concentration rise, and therefore it presents a more conservative way of evaluating the DP. Follows the Chendong correlation: DP *+,-$%-. = 1.51 log 89 2FAL (1) where 2FAL denotes the concentration of 2-FAL (ppm) in the insulating oil. According to [4], it is estimated that the remaining life of a certain PT values 100% for an 800 DP value and 200 DP value presents null remaining life. Even more the 200 DP value it is widely accepted as the bottom threshold. By considering [4], it was possible to gain 3

4 awareness on this matter and propose the evaluation mechanism. By considering the concentration of the seven DGA gases (ppm), the total concentration of noncombustible gases in DGA (ppm), the Be s AB and c AB respectively the evaluation parameter of the insulating paper condition and the 2-FAL concentration (ppm) in the insulation oil. The following evaluation method is proposed: respective scoring of each previous concentration referred and overall evaluation of DGA method be respectively represented by β L, β MA*N, γ L and s ANP, where x denotes the several gases scientific notation. s AB = 5, c AB < 0,05 4, 0,05 c AB < 0,17 3, 0,17 c AB < 0,86 2, 0,86 c AB < 2,88 1, 2,88 c AB < 6,45 (2) γ RS = 5, β RS 100 4, 100 < β RS 400 3, 400 < β RS 700 2, 700 < β RS , β RS > 1800 (3) Dissolved gas analysis Dissolved gas analysis consists in the concentration analysis of seven different gases γ *RU = 5, β *RU 125 4, 125 < β *RU 275 3, 275 < β *RU 400 2, 400 < β *RU , β *RU > 1000 (4) in the transformer mineral oil allowing the detection of internal faults in the PT such as V\{Y Z[S } (5) partial discharges, overheating and arching. β MA*N = β # # Methods like Duval Triangle, Rogers, Dornenburg among several others were proposed to analyze and acknowledge internal faults of electric and thermal nature taking into account the concentration of DGA gases. γ MA*N = = 5, β MA*N , 2500 < β MA*N , 4000 < β MA*N , < β MA*N , β MA*N > (6) In this work, beyond the identification of several proposed methods by different scientist and reputable electrotechnical organizations a comparative study between [5], [6] and [7] was performed. The comparative study and the knowledge on other proposed methods in this topic allowed to reach a final proposal regarding this test widely adopted by several utilities. the proposed evaluation of DGA is presented as Since it is not possible to present the whole evaluation criteria of the different gases in this short document only two of the DGA gases and the total concentration of noncombustible gases are shown. As for the overall evaluation on this method a common weighted sum was considered as follow. s ANP N s ANP = 1,2,3,4,5 (7) follows. 4

5 s ANP = int γ # q # # (8) where U and I denote the complex voltage and current. Power factor is given by: where q # denotes the relative weight (%) of each gas individual scoring. p g = 1 cos θ 100 (10) (10) Bushing power factor The following scoring criteria is proposed: Bushings are power transformer components responsible for providing a safe path for electrical conductors between the interior and exterior of PT. In fact, the reliability of PT is significantly influenced by the performance and s e = 5, p g < 0,5 4, 0,5 p g < 0,7 3, 0,7 p g < 1,0 2, 1,0 p g < 2,0 1, p g 2,0 (11) condition of its bushings. [8] The deterioration process of the bushings is normally reflected on dielectric parameters change and incidents of partial discharge. Such behavioral change is possible to anticipate by monitoring the power factor or dissipation factor in the electrical conductors passing through bushings Considerations It is not possible to present the whole work developed under the scoring criteria for the several inspection and tests performed on the PT for the health index. Therefore, three important tests are presented herein with the respective evaluation method. The insights provided on [6] and [9] allowed gaining sensibility on acceptable values for the power factor used in the electrical field. Ultimately a scoring criteria for the overall condition of power transformer bushings has been reached stated below. Be the scoring criteria regarding the condition of bushings and the power factor respectively represented by s e and p g. Considering the angle difference between the complex voltage and As for the asset health index an evaluation mechanism, taking into account all tests, was developed considering a respective relative weight to each test, so that all tests are accounted by their relative significance. Considering the asset health index, ranging from 1 to 5 (best), the scoring criteria for each of the tests proposed and its respective relative weight represented by a ", s L and w L, where x denotes the prefix chosen for each test. current in the electric conductors passing through bushings represented by θ. a " = V s r w r (12) r θ = arg U I (9) 5

6 3.3 Age a i Age criteria is a common and important attribute when it comes to assessing any physical asset, and power transformers are no exception on that matter. There are always issues on physical assets, particularly on PT, which are difficult to be accounted for condition assessment. Thus, due to lack of information, age equipment is commonly adopted. Research under this topic has shown two important conclusions. Firstly, it is broadly accepted to consider PT lifetime either as 30 or 40 years. Secondly, it is commonly assumed that age vs condition relationship in power transformers is in fact linear. Taking into account the research conclusions it was decided to consider the lifetime age of PT to be 40 years and for the age vs condition relationship to be linear, since it was accordingly to international good practices and 30 years was considered too conservative. Considering the actual age of the power transformer and the age indicator ranging from 9 1 to 5 respectively represented by i MB and a #. Follows the evaluation criteria developed. their physical assets. It is in fact base on the above and other aspects that good practices of asset management are recognized. Once the PT performance is related to its availability by evaluating the last one it is possible to account for the performance of PT. In order to understand availability of a given physical asset, it is first necessary to become aware of unavailability meaning. Unavailability is considered to be the ratio between the total time that the physical asset stops its operation and its lifetime. Nevertheless, unavailability can be measured for other time slots such as a year, five years, among other. Therefore, availability is simply calculated as: Availability = 1 Unavailability (15) The Portuguese Regulator of Energetic Services (ERSE) defines as a benchmark for combined rate of physical assets availability in the Portuguese Transmission Network the value of 97,5%. Taking into account the benchmark stated and [10], a scoring criteria for power transformer performance is proposed. 9 f i MB = i MB 9, 0 < i 9 MB 40 1, i 9 MB > 40 (13) a # = int f(i 9 MB ) (14) The indicator developed is presented below. d = t {V t M{M (16) 3.4 Performance Indicator a d f d = 0,8x 75, 95,0 d 100,0 1, d < 95,0 (17) Transmission system operators pay particular attention to the performance and availability of where a $, d, t {V and t M{M refers respectively to the performance indicator, the availability of a 6

7 given PT, the operational time (hours) and the total time considered. The performance indicator is then given by: a $ = int f d (18) transformer s rated power S. The formulation is presented below: P = S S (19) where P denotes the mentioned ratio. 3.5 Operational Indicator a o The operational indicator aims at analyzing the operational context of a given TP and its operating history. It is certainly important to account for the operational context of the PT since different scenarios might influence the regular operation of a given PT and therefore its overall condition. In view of the research carried out in this subject, with the goal of formulating an operational indicator for PT, it was possible to highlight some important variable such as the duration of intensive load periods and the analysis of both repair and load history. In order to evaluate the operational context of a given power transformer it was decided to examine the PT s load history. Thus, the evaluation methodology proposed has as principle the classification of each time period belonging to the power transformer s load history, this is, each operating period will be classified based on a set of rules. Firstly, it is considered that an operating time period data can be classified according to five different levels i taking into account the ratio between the maximum output power S i = 5, P 0.2 4, 0.2 < P 0.4 3, 0.4 < P 0.6 2, 0.6 < P 0.8 1, P > 0.8 (20) It is important to mention that the above ranking established is related to transmission network power transformers where good practices of no-overloading are considered. By considering a set of different time periods, it is necessary to count the periods that fall under each of the categories. By considering the count of time periods that fall under each of the categories and a load factor variable respectively represented by N # and LF. The load factor variable is calculated as follows: LF = # ƒ (i 1) N # # 8 # ƒ # 8 N # where LF 0,0; 4,0. [11] (21) As for the operational indicator it is represented by a % and it goes from 1 to 5 (best). The proposed operational context evaluation criteria is as follows. registered in a given time period and the 7

8 a % = 5, LF 3,6 4, 3,6 > LF 3,2 3, 3,2 > LF 2,8 2, 2,8 > LF 2,4 1, 2,4 > LF 2,0 (22) It is proposed that each of the main attributes of the condition index is studied in isolation so it becomes easier to understand the various factors that influence its evolution. Therefore, a 3.6 Condition Index Taking into account the four indicators concerning the condition assessment of the power transformer. Considering the overall condition index value ranging from 1 to 5, where 5 indicates the best condition, condition index is represented by IE # : different approach is proposed for each main attribute. Let us consider a moment in the future and its forecasted condition index respectively represented by t 8 and IE # ˆ. We consider the following forecasted overall evaluation method taking into account the four main attributes V IE # = a p (23) where a and p respectively denotes the previously defined: V IE # ˆ = a ˆ p (24) evaluation of each of the four condition assessment attributes described and its relative weight. 4 Condition forecasting Condition forecasting is an important aspect to analyze mid-term power transformer future state since it allows planning maintenance actions with a wider time frame and in fact estimate the behavior of the physical asset. In this work a maximum five-year forecasting is considered. A review of state evaluation and forecasting methodologies was held and some important methods were briefly introduced, such as Artificial Neural Networks, Dynamic Bayesian Networks, Fuzzy Logic, among others. In the course of this review, some physical asset related applications were presented. ˆ where a and p respectively represents the attributes forecasted evaluation and its relative weight. 4.1 Health Indicator a s - forecast As previously introduced, health index is an approximate measure of the internal and individual condition of the the PT obtained through the analysis of inspections and test performed. In order to forecast a s we need to study the behavior of the state variables that integrate each of the proposed tests and take in to account the current information on the asset s health. Once the information concerning the evolution of the condition variables is analyzed, an estimated value for each state variable can be obtained, and therefore through the health 8

9 indicator previously proposed we can reach a forecasted health indicator. Figure 2 shows the proposed reasoning. Figure 2 Proposed methodology for the prediction of the asset s health condition In order to estimate state variables future value, a learning Dynamic Bayesian Networks tool developed in Objected Oriented Programming course is presented. Figure 2 shows the developed tool in Windows environment. 4.3 Performance Indicator a d - forecast Based on the premise that the proposed forecasting condition index aims to plan maintenance activities and investment in the medium term (maximum 5 years) it is assumed that the current evaluation carried out to the physical asset should remain constant over the period to carry out the forecast. a $ ˆ = a $ (25) 4.4 Operational Indicator a o - forecast The operational indicator is intrinsically based on the relative value of the maximum power load of a given time period against the rated power of the transformer considered. Some actions related to investment in structural changes in the transmission network may influence the variation of this indicator. In the present work, the worst scenario is considered where no investment is performed on the network. In order to obtain the forecasted operational indicator value, the estimated Figure 3 Learning Dynamic Bayesian Network tool in Windows OS 4.2 Age a i - forecast Age attribute was previously introduced with its respective evaluation. The same evaluation method for forecasting the impact of future age in the condition of the PT is considered, therefore the only parameter that needs update is in fact the age of the physical asset in the future time considered. synchronous peak load growth is considered. Assuming that the growth will be reflected uniformly follows the proposed update to P this indicator: P = = S L, Š 1 + tc B Ž S S, t t Š S, t > t Š r (26) where tc B, S L, Š, t and t Š respectively represent estimated synchronous power annual 9

10 growth, maximum registered power in the time period that comprehends the actual time instance, the instance considered and the actual time instance. 5 Conclusions From an overall perspective, the novel condition-based index proposed in the current work is a good instrument to help utilities asset managers perform informed decisions as far as maintenance and replacement actions are concerned. The innovative forecasting methodology proposed is particularly valuable for predicting power transformer s future performance in the mid-term perspective. Furthermore, the present work allowed to deepen the knowledge on the electrical transmission network, particularly in power transformers, circuit breakers, high voltage lines and other fundamental physical assets on the network. References [4] K. Baburao, N. Bhangre e a. et, The experience of DP and furan in remnant life assessment of power transformer, Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008, pp , [5] M. Soares, Elementos para a Gestão do Ciclo de Vida de Transformadores Eléctricos de Potência, Master Thesis in Electrical and Computer Engineering, FEUP, [6] A. Naderian, S. Cress e a. et, An Approach to Determine the Health Index of Power, International Symposoium on Electrical Insulation (ISEI 2008), pp , [7] IEEE, IEEE Std C , IEEE Guide for the Interpretation of Gases Generated in Silicone- Immersed Transformers, Committee of the IEEE Power Engineering Society, Transformers, [8] M. Allahbakhshi e M. Akbari, Heat analysis of the power transformer bushings using the finite element method, Applied Thermal Engineering, [9] Y. Li, M. Tang e a. et, Aging Assessment of Power Transformer Using Multi-parameters, vol. 5, nº 1, pp , [10] REN - Rede Eléctrica Nacional, Relatório da Qualidade de Serviço 2014, [11] A. Jahromi, R. Piercy e e. al., An approach to power transformer asset management using health index, IEEE Electrical insulation magazine, vol. 25, nº 2, p. 2, [1] M. Martins, Condition and risk assessment of power transformers: a general approach to calculate a Health Index, Ciência & Tecnologia dos Materiais, vol. 26, nº 1, pp. 9-16, [2] A. Bossi, J. Dind e a. et., An International Survey of Failures in Large Power Transformers in Service, Final Report of the Cigre Working Group 12.05, nº 88, pp , [3] R. Brown e B. Humphrey, Asset management for transmission and distribution, Power and Energy Magazine, IEEE, vol. 3, pp , June