Research on GA weight determination and SOM comparison strategy in black-start evaluation

Purpose Different black-start initial plans may be required for specific power systems, so it is necessary to make decisions and select reasonable black-start plans to ensure that the power system can be quickly and safely restored in the event of a power outage. To address this issue, this study aims to propose a novel black-start schemes evaluation method called genetic algorithm(GA)–self-organizing feature mapping neural network (SOM), which combines the GA and SOM comparison strategy. Design/methodology/approach First, the index weights are considered as variables, and a fitness function with minimum scheme similarity is established. The GA is used to obtain the optimal index weights when it terminates. Then, a comparison strategy for black-start evaluation methods based on SOM clustering is designed. The SOM clustering algorithm is used to cluster the black-start schemes, and the consistency between the ranking vector and the clustering results is analyzed to measure the accuracy of black-start evaluation methods. Findings Experimental results indicate that the GA weight determination method enhances the accuracy of the black-start evaluation, and the SOM comparison strategy effectively realizes the quantitative comparison among different evaluation methods. Originality/value The proposed method is novel in using GA to determine the index weights and for verifying the accuracy of black-start evaluation methods based on SOM comparison strategy.