An Improved Bat Algorithm for Load Model Parameter Identification of Power System

Aiming at the problem that the traditional bat algorithm is easily trapped in the local optimal solution, this paper presents an improved bat optimization algorithm, and uses this algorithm to identify the parameters of the power system load model. The algorithm flow is as follows: Firstly, chaotic mapping traverses the whole solution space and initializes the solution. Secondly, the fitness of each solution is calculated in the application process of the algorithm, and the fitness of the solution near the peak value in the solution space is shared. The more solutions exist in a certain range, the lower the fitness. Moreover, after the algorithm runs a certain number of cycles, the simplex method is used for the second search. This method solves the problem that the traditional bat algorithm is easily limited to the local optimal solution, and makes the parameter identification method of the load model have higher identification accuracy. The simulation results show that the proposed algorithm has better performance in convergence speed than the traditional bat optimization algorithm, and can be applied to the parameter optimization of load model.