Clustering Analysis of Black-start Decision-making witha Large Group of Decision-makers

The optimization of black-start decision-making plays an important role in the rapid restoration of a powersystem after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks，the black-start decision-makers are no longer limited to only one or a few powersystem experts such as dispatchers，but rather a large group of professional people in practice.The overall behaviorsof a large decision-making group of decision-makers/experts are more complicated and unpredictable.However，theexisting methods for black-start decision-making cannot handle the situations with a large group of decision-makers. Given this background，a clustering algorithm is presented to optimize the black-start decision-making problem witha large group of decision-makers.Group decision-making preferences are obtained by clustering analysis，and thefinal black-start decision-making results are achieved by combining the weights of black-start indexes and thepreferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case. This work extends the black-start decision-making problem to situations with a large group of decision-makers