A new approach to predict the excitation current and parameter weightings of synchronous machines based on genetic algorithm-based k-NN estimator

This paper presents a novel and efficient solution to overcome difficulties in excitation current estimation and parameter weighting of synchronous motors. Weighting the parameters or searching the best coefficients of problems is commonly accomplished through intuitive/heuristic approaches. For this reason, in this study, a genetic algorithm-based k-nearest neighbor estimator (also called intuitive k-NN estimator, IKE) is adapted to explore the optimum parameters and this algorithm estimates the excitation current of a synchronous motor with having small prediction errors. The motor parameters such as load current, power factor, error and excitation current changes are weighted depending on the effects on the excitation current. The experimental results are compared with the estimation results in consideration with standard deviations of the well-known Artificial Neural Network-based (ANN) method and k-NN-based estimator with that of the proposed IKE method. The results have shown that the proposed IKE estimator achieves the tasks in high accuracies, stabilities, robustness and low error rates other two well-known methods presented in the literature.