Reliability Assessment Of Power Systems Based On Bayesian Networks

At present, Monte Carlo method and Analytical method are widely used in power system reliability assessment. However, they can only calculate the reliability indices and make sensitivity analysis and can't effectively identify the weak elements. Furthermore, it is difficult to apply importance analysis method, which is usually used in general reliability engineering, to power system reliability assessment. Bayesian networks provide a method to represent knowledge in a graphical mode and can be used to do directed graphical description for causal probability relation between random variables. They are mainly used for uncertainty knowledge representation, casual inference and diagnosis inference. By means of various inference modes, the weak elements of power system can be readily identified. Taking the advantages of Bayesian networks, the author makes a deep study on applying Bayesian networks to power system reliability assessment. The main contributions are : (1) By analyzing and comparing the implication of several kinds of importance approaches used in the reliability analysis of general engineering, the implication of the sensitivity analysis approach for power system reliability assessment and the implication of various conditional probabilities inferred from Bayesian networks, it can be concluded that Bayesian networks based approach is more suitable to identify the weak components in a power system. (2) By combining the Bayesian network's stochastic simulating inference algorithm based on forward sampling and time-sequence simulating algorithm of power system reliability assessment, approximate Bayesian network inference algorithm for time-sequence simulation (ABNIATS) is proposed. Numerical simulating results show that it is effective. (3) Based on minimal state cutsets, Bayesian network modes to represent electrical main systems composed of multi-state components are built. Moreover, they have been applied to two typical electrical main systems reliability assessment, 3/2 breakers scheme and double bus bar scheme. The simulation results have shown the advantages of this method. ABNIATS method and exact inference method are respectively used to evaluate the reliability of the two electrical main system schemes mentioned above. The results show that the calculation error of the former is smaller. (4) The Bayesian network models for distribution systems reliability evaluation are presented and have been applied to IEEE reliability test system (RTS) to test their feasibility. The results show that the Bayesian network models are correct and effective. (5) Bayesian network models for the reliability evaluation of generation systems and multi-area interconnected generation systems are built. The proposed ABNIATS approach and exact inference approach are respectively applied to test IEEE reliability test system (IEEE-RTS). The results have proven the superiority of the ABNIATS approach. (6) A novel and fast transmission system fault diagnosis method based on Bayesian networks is proposed. The method is based on component oriented Bayesian network models, which have stronger fault-tolerance ability.