PurposeThis paper aims to propose an improved multifunctional control strategy for achieving real, reactive power flow control and the mitigation of power quality issues in grid integrated photovoltaic (GIPV) systems.Design/methodology/approachThe paper proposes a dual stage, three phase, multifunctional GIPV system with modified instantaneous reactive power (IRP) theory-based and modified synchronous reference frame (SRF) theory-based control algorithms for reference template generation with continuous load power requirement tracking. The control structure is designed so as to impart virtual distribution static compensator functionality to the photovoltaic inverter. The dual mode operation in active filter and renewable power injection modes provides enhanced capability to the GIPV system. A comprehensive evaluation of the dynamic behaviour of the GIPV system is carried out for various conditions of irradiance and load under MATLAB/Simulink platform. The performance comparison is done considering an uncompensated system and the GIPV system with both proposed control algorithms.FindingsThe extensive simulation results demonstrate that the proposed modified SRF theory-based multifunctional control strategy shows superior performance in real and reactive power flow control; reduction in real and reactive burden of the utility grid; and regulation of dc bus voltage under varying scenarios of irradiance and load. Furthermore, there is improvement of grid power factor and reduction in total harmonic distortion of grid currents in compliance with the IEEE 519 standard even with highly non-linear loads at the point of common coupling.Originality/valueThe proposed modified SRF theory-based multifunctional controller offers a viable solution for power quality enhancement as well as the realization of effective real and reactive power flow control in GIPV systems. Thus, the penetration level of distributed generation can be increased in this era of global energy crisis.
The changing landscape of power grids with distributed energy sources and power electronic devices has led to increasing relay maloperations. Wide area backup protection is necessary for the resolution of faults and for a reliable power grid. This paper presents detecting and classifying faults in transmission lines for wide-area backup protection using phasor measurement units (PMU) data. The faults are detected and classified using a Weighted Extreme Learning Machine (WELM) algorithm, which considers the variable distribution of data among the different classes using a weighted approach. The PMU signal data used were generated by the simulation of an IEEE 39 bus test system in the PowerWorld/OpenPDC/MATLAB environment. For classification, the input features data were derived using a wavelet transform-based ensemble feature extraction technique, and the WELM classifier was optimized using Particle Swarm Optimization (PSO). The PSO optimized WELM (PSO-WELM) model trained on PMU data detected faults with 100% accuracy and classified them into different types with an accuracy of 99.85%. It is validated that the PSO-WELM outperforms other known classifiers on performance comparison. The model also classified noisy data with a signal-to-noise ratio (SNR) as low as 10 dB and with an accuracy of 97%.
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