Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method

It is well‐known that the reliability evaluation of composite power systems is computationally demanding. This work introduces a state space classification (SSC) technique that classifies a system's state space into failure, success, and unclassified subspaces without performing power flow analysis. The SSC technique was developed based on calculating the maximum capacity flow of the transmission lines and the available generation. An algorithm, which is developed based on a directed binary particle swarm optimisation, was developed to search for failure states in the unclassified subspaces. The key element in controlling the particle swarm optimisation (PSO) search method to search for failure states in the unclassified subspaces is the selection of the weighting factors of the velocity update rule. The work presented in this study proposes an intelligent PSO based search method to adjust these weighting factors in a dynamic fashion. The effectiveness of the proposed method was demonstrated on three test systems, the Institute of Electrical and Electronics Engineers reliability test system (IEEE RTS), the modified IEEE RTS and the Saskatchewan Power Corporation in Canada. The results have shown that the reliability indices obtained using the proposed method correspond closely with those obtained using Monte Carlo simulation with less computation burden.