Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system

This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard.