Interval Reliability Assessment of Power System under Epistemic Uncertainty Based on Belief UGF Method

2017 2nd Asia Conference on Power and Electrical Engineering

能源学;电工学

Wang, Bolun^1 ; Wang, Yong^1 ; Ding, Ying^2 ; Li, Ming^3 ; Zhang, Cong^4

Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan

250061, China^1

State Grid of China Technology College, Jinan

250002, China^2

State Grid Shan Dong Electric Power Research Institute, Shandong Province, Jinan

250002, China^3

Shandong Zibo Power Supply Company, Shandong Province, Zibo

255032, China^4

关键词: Epistemic uncertainties;    Loss of load expectation;    Loss of load probabilities (LOLP);    Lower and upper bounds;    Plausibility function;    Power generation systems;    Reliability assessments;    Universal generating functions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/199/1/012070/pdf
DOI  :  10.1088/1757-899X/199/1/012070

来源: IOP

【 摘 要 】

At present, reliability assessment plays an important role in power systems. When the component failure probabilities are interval valued, common methods fail to achieve reasonable interval reliability assessment result of power systems. In this paper a novel approach based on the belief universal generating function (BUGF) is proposed to calculate the reliability indexes of power systems. Instead of giving a single-valued assessment result, a belief function and a plausibility function are exploited to calculate the lower and upper bounds of the loss of load probability (LOLP), the loss of load expectation (LOLE), the expected unsupplied load (EUL) and the expected unsupplied energy (EUE) in UGF, respectively. The proposed approach can track the correlation of the original data well and keep it to the end of the calculation. By using BUGF compared to other common methods to calculate the interval LOLP, LOLE, EUL, and EUE of IEEE-RTS 79, the results show the BUGF method can track the correlation of the original data well, and can get narrower and more accurate interval reliability indexes of the power generation system, which illustrates the effectiveness of the proposed approach.