Performance evaluation of SEIG employing certain optimization tools in MATLAB under various operating conditions

This paper illustrates the usage of certain optimization toolboxes available in MATLAB, for predicting the performance of self-excited induction generators (SEIGs). An objective function is formulated by taking the exact equivalent circuit of SEIG and adopting the loop impedance method. The formulated objective function comprises of absolute value of this loop impedance, along with a constraint for the core loss resistance of the generator. The more recently used algorithms, namely, whale optimization algorithm (WOA) and grey-wolf optimizer (GWO) algorithms are straightway used to minimize this function to zero, which results in the evaluation of unknown parameters such as per unit (pu) frequency (a), magnetizing reactance (X
m
) and core loss resistance (R
m
) of SEIG for the given pu speed (b), excitation capacitance (C), machine and load parameters. Then, to justify the efficacy of these algorithms, results obtained from these, are compared with genetic algorithm (GA) solver. Results reveal that WOA converges faster in minimizing the objective function. Experiments were also conducted on a 3-phase, 3.7 kW, 230 V, delta-connected SEIG, and these results closely match with the calculated values, which further confirms the applicability of the proposed optimization tools.