Wavelet-fuzzy power quality diagnosis system with inference method based on overlap functions: Case study in an AC microgrid

Abstract This work proposes a wavelet-fuzzy power quality (PQ) diagnosis method able to evaluate the PQ impact of steady-state (stationary) PQ events in alternating current (AC) microgrids considering the influence of the power level penetration. The proposed method is composed by a wavelet packet-based signal processing to compute the root mean square (RMS) and steady-state PQ indices of measured voltages and currents, providing accurate results even if transient disturbances take place. Thereafter, a cascade-type hierarchical fuzzy system receives the PQ indices and performs the power quality diagnosis to evaluate the impacts of disturbances on electrical system power quality. The proposed method considers subjectivities of several PQ standards simultaneously and applies an adaptive algorithm that allows the evaluation of the PQ diagnosis from the total harmonic distortion of currents considering different levels of power penetration of microgrids. Experimental results obtained from an ac microgrid laboratory setup evaluates the proposed PQ diagnosis method. In addition, the fuzzy system uses a new inference concept based on an extended n-dimensional overlap function.