Coyote optimization algorithm based on mutation opposition-based learning in terminal optimization

As a crucial component of the power system, the distribution network undertakes the core tasks of power transmission and distribution, and plays an indispensable role in the operation and stable power supply of the power system. Optimizing the installation location of distribution automation terminals aims to balance the reliability and economy of power supply in the distribution network. It is a key strategy to improve power supply quality and ensure the safe and stable operation of the distribution network. It is also an important issue that urgently needs to be addressed in the construction of the distribution network. A mutation opposition-based learning based coyote optimization algorithm is proposed to solve the optimization layout problem of distribution automation terminals. Firstly, taking the minimum annual comprehensive cost as the objective function, considering reliability constraints and other conditions, the optimal number of terminal arrangements is determined; then, based on the mutation opposition-based learning based coyote optimization algorithm, the optimal layout position of the terminal is calculated. This paper takes the actual distribution network as an example to verify that the proposed method can effectively reduce costs and further determine the optimal layout of terminals.