摘要： This study presents a novel Leaky Least Mean Logarithmic Fourth (LLMLF) based control technique and Learning based Perturb and Observe (LPO) maximum power point tracking (MPPT) algorithm, for optimal control of grid-tied solar photovoltaic (PV) system. Here, a novel LLMLF algorithm is developed for active component extraction from load current, and a novel LPO MPPT algorithm is developed for optimal MPPT operation. Proposed LPO is the improved form of Perturb and Observe (P&O) algorithm, where inherent problems of traditional P&O algorithm like steady-state oscillation, slow dynamic responses and fixed step size issues, are successfully mitigated. The prime objective of proposed LLMLF control is to, fulfill the active power requirement of the loads from generated solar PV power, and excess power is fed to the grid. However, when generated PV power is less than the required load power, then LLMLF fulfills the load by taking extra required power from the grid. During this process, power quality is improved at the grid. The controller action provides reactive power compensation, power factor correction, harmonics filtering and mitigation of other power quality issues. Moreover, when the solar irradiation is zero, than the DC link capacitor and VSC act as DSTATCOM (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 condition, imbalance loading condition for linear/nonlinear loads, as well as in different grid disturbances such as over-voltage, under-voltage, phase imbalance, harmonics distortion in the grid voltage etc, where it has shown a very good performance.