A novel case adaptation method based on an improved integrated genetic algorithm for power grid wind disaster emergencies

Case adaptation is a challenging and crucial process of Case-Based Reasoning (CBR) for power grid wind disaster emergencies. The statistical adaptation method is a traditional method that is independent of domain knowledge, is easy to implement, but is not proper for the complex system problem. Therefore, the aim of this paper is to propose a novel case adaptation method to address this problem by integrating the multi-objective genetic algorithm with gray relational analysis, called the grey relational analysis-multi-objective genetic algorithms method (GRAMOGA). Compared with the traditional method, GRAMOGA is performed in terms of corresponding relations between the case similarity and emergency plan, indicating a new idea for case adaptation. To improve adaptation accuracy, this paper improved the multi-objective genetic algorithm by using a selection method based on the fitness function. Furthermore, the frame theory is expanded by combining it with the D/S evidence theory, providing a novel method for case description and retrieval with incomplete information. A practical example from the south of Jiangsu demonstrates that GRAMOGA achieves better adaptation performance for power grid wind disaster emergencies. In addition to the practical applications in case adaptation, GRAMOGA can be used as a novel method for expanding the case base.