Grid component outage probability model considering weather and. aging factors

Transcription

1 Advanced Materials Research Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland Grid component outage probability model considering weather and aging factors Feng Yao 1,a, Zhonghui Zhang 2,b, Ye He 3,c 1 Electric power company Of Henan, Zhengzhou, China, ; 2 Nanchang University, Nanchang, China, ; 3 Room 818, Building 28, Qianhu Campus, Nanchang University, No.999 Xuefu Road, Honggutan New District, Nanchang City, Jiangxi Province, China, a @163.com, b yaofeng-1981@163.com, c @qq.com Keywords: Risk assessment;component outage model;the weather dependent;aging failure Abstract: Component outage model plays an important role in risk assessment of the power grid safety. In this paper, three aspects resulting in failures were taken into consideration, which are: repairable failure, aging factor, and the weather dependent factor. Repairable failure model can be obtained based on statistical date in the past, while aging failure model were simulated according to normal distributions. Moreover, the weather dependent characteristics of both models were combined, and ultimately, it was possible to derive the components outage probability model. Besides, the feasibility of this model was proved by an actual line in the IEEE-RTS96. Introduction During the process of power grid safety risk assessment, it s indispensable to build up the component outage model [1]. The model changes constantly with operating environment and its own circumstances, and thus there is a need to build up the component outage model to describe these characteristics along with the factors. Fundamental elements of the model consist of weather information, component outage statistics and component lifecycle. Therefore, in order to take the weather and aging factors into consideration for component outage model, it was required to conduct a long-term data collection between different departments in power companies and to record down the detailed factors causing the component outage. Meanwhile, it s also a must for meteorological department to be involved closely. At present, there are mainly two approaches for the research of the component outage model: one is based on the statistical data to calculate the probability of the component outage model [1-4], the other is based on the current operating condition to establish the model [5-6]. The Former method which is only based on statistical data doesn t count in the factor caused by actual operation, and thus the accuracy of the calculations of the component outage rate is not high enough; whereas the latter one depends on the current operating condition contains factors of environment temperature, wind speed, sunshine heat, load levels, service time, the current and so on, of which the data will be complex and hard to achieve. This paper considers the operability of the component outage model when applied to engineering field and establishes a model counting weather and aging factors in. Finally it uses a circuit in IEEE-96 to prove the validity, the probability of the component outage model. Using the model, we will be able to know more about the changing patterns of component outage model under different factors and as a result, it provides a certain guide line to the repair schedule component and the operation management of power grid. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (# , Pennsylvania State University, University Park, USA-18/09/16,14:51:46)

2 Advanced Materials Research Vols Component outage probability classification modeling Component outage probability classification model should reflect the outage probability change influenced by certain conditions. Currently, the constructions of the Power Grid component outage probability models have several categories as listed below: 1 Repairable outage model Literature [2] put forward the independent and related outage model, which gives out the algorithm of the model based on historical statistics. Among those outage models, the repairable 2-state outage model is an important one as it can directly reflect the component failure rate of the power grid, the mathematics form can be: λ 8760 * MTTF P = λ λ 1 ( λ + µ ) = λ 1 = ( + ) ( λ* MTTF ) 8760 MTTF (1) Where, λ is failure rate(failure times/year); u is repair rate(repair times/year); MTTF is mean time to failure(h); f is mean failure frequency(failure frequency/year); P1 is probability that can be repaired. 2 Aging outage model The outage probability of the component is very much related to the service time, as the aging outage is an event relevant to the limited serving time. It obeys the laws of Valley curve of the operating life [7]. When the component operates after certain years, it s very likely to have a sudden aging failure. Using the failure rate model based on the states of the equipment as the research object, Literature [8] put forward a failure rate model of the equipment with a complete health process. Component aging outage model can use the normal distribution to simulate, the component aging failure probability of service time T can be [1] : T + ( i 1) x µ T + i x µ Q( ) Q( ) P σ σ 2 = T µ Q( ) σ Where, µ and σ respectively is the normal distribution of mean and standard deviation, function Q approximate as standard deviation. 3 Weather dependent random failure model In addition to aging failure models, power grid equipment is also affected by weather. Literature [9] first proposed a 2-state weather model with both the normal state of climate and the heavy climate. In literature [10], the author expanded the 2-state model and obtained a 3-state weather evaluation model. Literature [11] came up with a model that considers multiple factors. For simplicity, we always use 2-state weather model to describe the random failure probability. Literature [3] divided the power system equipment into 2 categories: Exposed and Enclosed, and also brought up the idea of applying different models according to different conditions. Random failure model of transformers and circuit is as follows [6] : Random failure probability in t for the transformer: (2) Ptc = tc( ) 1 e λ w t (3) Where, λ tc( w ) is the random failure probability for the transformer failure model.

3 824 Energy and Power Technology For transmission circuit, at one point of time, different parts of the same transmission line may be subjected to different weather conditions. Random failure probability in t for the transmission circuit: Plc lc( 1, 2,, k) 1 w w w t = < e λ (4) lc( 1, 2,, k) Where, λ ww w < is the random failure probability for the transmission circuit failure model. k lc 1, 2 < k = lci i i i= 1 λ ( w w,, w ) λ ( w) l (5) Where, k is the meteorological region number that transmission circuit through; l in the length of i the transmission circuit in the I-th region; transmission circuit in the I-th region. λ ( w) lci i is the failure probability of the unit length of the Component outage probability model counting the weather and aging factors in The reliability evaluation or risk assessment of Power Grid both domestic and abroad were mainly conducted basing on past year statistics to calculate the probability of failure of a component, and most of them only considered repairable failure in the traditional risk evaluation of the power system, while ignoring the failure of aging and so on. During the power grid operation, aging issue of equipment has become one of the critical factors causing electrical component failure, which, however, is ignored in this case, and thus the risk of aging failure will be underestimated certainly. In addition to aging failure model, the probability of having component failure will increase during heavy weather [12]. Compared with other influencing factors, adverse weather conditions lead to 33% of all line faults and failure [13]. Moreover, in bad weather conditions, the failure rate of the components greatly increased. Therefore, this paper proposes to build up a component outage model which considers all the three factors: repairable failure, the failure of aging, weather dependent. Repairable outage model Element outage model Aging outage model Weather dependent random failure model Figure 1 Component outage model block diagram When involving multi-factor independent outage, apply concepts of union set: if there are two independent outages, you can apply the following formula to calculate the equivalent outage probability [1] : P = P P = P + P PP e (6) This group of formula can be repeated for conditions with more than two outages, the first two outages can be combined into an equivalent outage, then the third outage with a previous outage combination equivalent into the equivalent of a second outage. The three outages model mentioned

4 Advanced Materials Research Vols in this paper are independent of each other. Hence, applying the equations above, the power grid components outage probability with weather and aging issues considered, will be: P = P P P = P + P + P PP PP PP + PPP (7) Where, P1 is the repairable failure rate based on the statistical data; P2 is component aging failure rate of line failure probability by the impact of the component service time; P3 is weather-dependent component failure probability. Numerical example To apply the component outage probability model proposed in this paper, a single line in the system IEEE RTS-96 [14] was calculated. Assume that an overhead line has a service age of 10 years, in which the meteorological area is counted as 1, line length is 16km, and current weather is sunny, while the weather forecast predicts a storm in the region after 1 hour. The failure rate of the line considering historical statistics is λ= 0.3 times/year; repair time is 10h; duration of normal weather N is 100h; duration of inclement weather S is 1h; the probability of failures happening in the inclement weather F is 0.3. Example 1: considering only repairable failure model p( t) = times/ h 0h t 2h Example 2: taking into account the weather dependent component outage probability model p( t) = times/ h 0h t 1h p( t) = times/ h 1h t 2h Example 3: taking into account the weather and aging factors component outage probability model p( t) = times/ h 0h t 1h p( t) = times/ h 1h t 2h Within the inspection period [0h, 2h], the above three examples has a failure rate curve of line outage probability as shown in Figure 2. p As can be seen in the figures: for example 1 the resulting probability is a constant, when only the historical statistical data is considered; for example 2, after 1h of weather changing from normal to heavy, the line outage probability increases, which implies the impact of weather changing on the line outage probability; for example 3, it takes both weather and aging factors into account in the calculation of the component outage probability model, and thus the result is much closer to the actual situation.

5 826 Energy and Power Technology Conclusion This Paper analyzes the factors that affect the component outage, and considers repairable failure, aging and weather dependent, in total three factors, to build up a component outage probability model. Besides, it also analyzes the results of considering different factors of component failure probability analysis. Furthermore, it points out the focus on the statistics of the historical data, and establishes a corresponding model in accordance with the different components, and to better establish the component outage probability model. By an actual calculation example, we have the following conclusions: (1) The impact of bad weather on the component outage probability is great. The results show that the outage probability is four times more than the normal weather of the same components in the inclement weather. Therefore, it s constructive for power grid evaluation to have the weather factors counted in and establish the component outage probability model. (2) In the case of considering the component service time, there are differences between different component outage probabilities, according to their serving years. Therefore, in establishing the component outage probability model, it s necessary to consider the impact of service time. References [1] Wenyuan LI, Risk assessment of power systems--models, Methods, and Applications (Science Press, Beijing 2006).(In Chinese) [2] Xiaoxin Zhou, Qiang Lu, Qixun Yang, Qili Huang, China electrical engineering canon (China Electric Power Publications, Beijing 2010). (In Chinese) [3] Liaoyi NING, Wenchuan WU, Boming ZHANG. Analysis of a Time-varying Power Component Outage Model for Operation Risk Assessment[J].Automation of Electric Power Systems, 2009, 33(16): (In Chinese) [4] Billinton R, Allan R N. Reliability evaluation of power systems[m]. New York and London: Plenum Press, 1996: [5] Haitao Liu, Lin Cheng, Yuanzhang Sun, Peng Wang. Outage Factors Analysis and Outage Rate Model of Components Based on Operating Conditions[J].Automation of Electric Power Systems,2007, 31(7): (In Chinese) [6] Lin Cheng, Jian He, Yuanzhang Sun. Impact of Transmission Line s Real-Time Reliability Model Parameter upon Power System Operational Reliability Evaluation. Power System Technology, 2006, 30(13): (In Chinese) [7] Li Zhang, Bo Zhang. An algorithm for optimal parameter estimation of the failure rate of electrical equipment[j]. Relay, 2005, 33(17): (In Chinese) [8] Wanfang Zhao, Huifang Wang. Research of Failure Rate Model Parameters Based on Equipment Integral Health Process[J].East China Electric Power, 2012, 40(8): (In Chinese) [9] GAVER D P, MONTMEAT F E, PATTON A D. Power system reliability: PartⅠmeasures of reliability and methods of calculation. IEEE Trans on Power Apparatus and Systems, 1964, 83(7): [10] Zhimin Lin, Han Lin, Buying Wen. Power system reliability evaluation based on weather dependent failure models [J].East China Electric Power, 2008, 36(1): (In Chinese) [11] Xiaofu Xiong, Weijun Wang, Yang Yu, Zhijian Shen, Renli Cheng, Zhiyong Dai. Risk Analysis Method for Transmission Line Combining of Various Meteorological Factors [J].Proceedings of the Chinese Society of Universities for Electric Power System and its Automation, 2011, 29(6): (In Chinese)

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