Integration of Solar PV With Low-Voltage Weak Grid System: Using Normalized Laplacian Kernel Adaptive Kalman Filter and Learning Based InC Algorithm

This paper proposes a novel normalized Laplacian kernel adaptive Kalman filter (NLKAKF) based control technique and learning based incremental conductance (LIC) maximum power point tracking (MPPT) algorithm, for low-voltage weak grid-integrated solar photovoltaic (PV) system. Here, a two-stage topology of three-phase grid integrated solar PV system is implemented, where the loads are connected at the point of common coupling. Proposed LIC is the improved form of incremental conductance (InC) algorithm, where inherent problems of traditional InC technique, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed NLKAKF control is to meet the active power requirement of the loads from generated solar PV power, and after feeding load, excess power is fed to the grid. However, when generated PV power is less than the required load power, then NLKAKF control meets 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, and harmonics filtering and mitigation of other power quality issues. Moreover, when the solar irradiation is zero than voltage source converter acts as a distribution static compensator (DSTATCOM), 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, as well as in different grid disturbances such as overvoltage, undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc.