Identification Of Weak Point In Probabilistic Risk Assessment Of Bulk Power System

This dissertation is supported by National Natural Science Foundation of China(50977094) and Chinese Universities Scientific Fund (CDJZR11150012).As the fast growth of the economy, and the steady progress of the technology inelectric power industry, modern power system and its performance become more com-plex. The interconnection of power grid suffers more risks while it brings great eco-nomic benefits, which leads challenges to power system in planning, operating, main-tenance and management procedures. The using of new types of equipment, weatherand region conditions and even the human’s factor put impacts on the safe operation ofthe power system. The needs of electrical energy draw higher demand in the powersupply ability. Power system reliability assessment, which can provides quantitative in-formation about system reliability level and identify the weak points through powersystem, is a useful method to obtain theory evidence for power system planning and op-erating procedures, has an important practical significance in engineering and becomesan important subject in power system researches.This paper presents related works with the impact of uncertainty of reliability pa-rameters on risk assessment of bulk power system and the method of identifying theweak generation buses in bulk power system.The main contents of this paper are as follows:①Stochastic response surface method (SRSM) is introduced. The uncertainty ofthe statistical parameters, such as the failure rate and the mean time to repair of thecomponent, has been described with random number. The reliability indices can berepresented as a polynomial function of independent random variables by using Her-mite-chaos expansion. Equations of calculating expectation and standard deviation arederived, that the statistic information of indices can be obtained quickly. The conver-gence rate of SRSM has been improved by using generalized polynomial chaos expan-sion, with the non-Gaussian distribution of component reliability parameters. The weakpoint of the systems has been identified through the polynomials function of reliabilityindices involving the uncertainty of component reliability parameters. In case studies ofRBTS and IEEE-RTS79test systems, using both2order and3order SRSM, polynomialequation is obtained, expectation and standard deviation of reliability indices of thesystem are calculated, the probability density distributions of indices are obtained, the weak component reliability parameters are identified. Compared with the result of re-peating reliability evaluation, SRSM is verified corrective and effective.②The uncertainty of the statistical parameters, such as the expected failure rateand the repair rate of the component, has been described with fuzzy number. The fuzzyimportance index of the component has been calculated. In order to identify the com-ponent that impacts the most on the reliability indices of the power system while theparameters uncertain, defuzzification techniques, including gravity center method, meanvalue method and α-cut method, are applied to these fuzzy importance indices to nor-malize the values for comparison. In the case studies on the RBTS and IEEE-RTS79testsystems, the fuzzy importance indices of the components are calculated, the results ofwhich verifies the correctness and effectiveness of above-mentioned method.③A model of capacity increment utilization rate based on DC load flow is built.The capacity increment average utilized rate and the expectation of capacity incrementare defined. According to the result of capacity increment utilized rate, the weak genera-tion bus and line could be identified in terms of capacity. In case studies of RBTS andIEEE-RTS79test systems, the effective identification and correctness of the method areillustrated.