Research on the Inverse Problem of Reliability Evaluation–Model and Algorithm

• Main Authors: Tao Niu, Fan Li, Bo Hu, Hui Lu, Lvbin Peng, Kaigui Xie, Kan Cao, Kunpeng Zhou
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Inverse problem of reliability evaluation; nonsequential Monte Carlo method; high-order polynomial approximation; continuation method
• Online Access: https://ieeexplore.ieee.org/document/9317857/

Due to the high dependence of economic and social development on power systems, the demand for reliable operation of power systems is increasing. Considering the popularity and widespread installation of smart meters, accurate system/node reliability indexes can be obtained. The inverse problem of reliability evaluation (IPRE) refers to the use of known system/node reliability indexes to obtain component reliability parameters. In this paper, a novel method of solving the IPRE is proposed. First, based on a nonsequential Monte Carlo (NSMC) method, analytical expressions for system reliability indexes in terms of component reliability parameters are derived, and then, the nonlinear equations of the IPRE are constructed. Second, a high-order polynomial approximation based on the conjugate gradient algorithm is used to calculate the unknown component reliability parameters, and the results are compared with those obtained using traditional neural networks method. Finally, a continuation method is used to correct the errors of the obtained component reliability parameters. Three cases, namely, the IEEE 1979 Reliability Test System (IEEE RTS-79), the Roy Billinton Test System (RBTS) and the Chuanyu power system in Southwest China, are used to test the method proposed in this paper to verify its feasibility and accuracy.