Adaptive neuro-fuzzy inference system based representative quality power factor for power quality assessment

Under sinusoidal operating conditions of electric power system, the classical definitions of apparent power and power factor work well as long as the loads are linear and the source voltage waveform is sinusoidal. Increase in use of power electronic devices, adjustable speed drives and other nonlinear loads cause the voltage and current waveforms to become non-sinusoidal and highly distorted. A new adaptive neuro-fuzzy inference system based representative quality power factor (ANFIS RQPF) is proposed in this paper to represent the existing different power factors—displacement power factor, transmission efficiency power factor and oscillation power factor. The ANFIS RQPF can represent an essential module for evaluating and amalgamating the three power factors. The ANFIS RQPF was applied to different cases—linear, nonlinear, sinusoidal and non-sinusoidal considering lagging and leading power factors. It is shown that the ANFIS RQPF is expressive and accurately represents the existing power factors in all cases and in all situations. Taking into consideration the advantages of the ANFIS such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties, this factor can be useful for power quality assessment, cost-effective analysis of power quality mitigation techniques, as well as billing purposes, in these situations.