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Power electronic converters find application in diverse fields due to their high power conversion efficiency. Converters are often characterized by time response specifications, robustness and stability. Conventionally, converters employ the classic PID controller. The state space average linear time invariant model of a boost converter is known to be a non-minimum phase system. This paper demonstrates that the boost converter with a PID controller using the Queen Bee assisted Genetic Algorithm (QBGA) optimization is not robust to plant parameter variations. A fractional order PID controller based on QBGA optimization proposed here is shown to have improved robustness. The controller proposed here is applicable across converters, viz., buck, boost and buck-boost, equally.

WOS:000575284400001 Traditional approaches towards proportional-integral (PI) controller tuning often fail to provide optimum gain values in situations where shunt active power filter (SAPF) is connected to systems containing complex, dynamic, and nonlinear loads. Optimum gain values are, however, crucial in the generation of compensating currents with less transient and steady-state error, that would nullify the harmonic currents in a short time. This work proposes two soft computing techniques, genetic algorithm (GA) and Queen Bee assisted GA (QBGA) for better controller tuning to obtain optimum gain values to switch SAPF. These algorithms are used in local search technique mode to arrive at the optimal solutions based on the desired characteristics. The PI controller controls the voltage of the DC capacitor to generate the required compensating current. The proposed algorithms are practical since reliable solutions are obtained with a limited number of iterations. Implementation of the suggested algorithm reduces the THD of supply current to less than 5%, in compliance with IEEE-519 standards. The system performance is evaluated through MATLAB simulation tool. Suitable hardware model is also developed and tested for validating the simulation results. The hardware results are found in close agreement with simulation results. The highlight of this work is the introduction of QBGA algorithm as a novel technique for tuning of PI controller for SAPF.