A convolutional neural network-based approach to composite power system reliability evaluation

Abstract This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development.