Modified Finite Time Sliding Mode Controller for Automatic Voltage Regulation under Fast-Changing Atmospheric Conditions in Grid-Connected Solar Energy Systems

The lack of control in voltage overshoots, transient response, and steady-state error are common issues that frequently occur in a grid-connected photovoltaic (PV) system which can degrade the battery storage and negatively impact other grid components. It may result in damage to equipment and reduce the efficiency of the overall power system. To improve the efficiency of the overall power system, an artificial intelligence (AI) optimization technique is used to determine the optimal sliding mode controller (SMC) gain. The present work proposes the accomplishment of a control strategy for designing a finite-time sliding mode maximum power point controller for a grid-connected photovoltaic (PV) system under fast-changing atmospheric conditions. A particle swarm optimization algorithm (PSO) is used to determine the optimal sliding mode controller (SMC) gains used in perturb and observe (P & O) algorithms. Two modes of operation are available: offline mode for testing different sets of SMC gains leading to optimum values, and online mode for driving the variable step of the P & O MPPT using the SMC optimum gains. The Simscape-power system toolbox (Version 2020A) has been used successfully to study the effectiveness of MPPT. An evaluation of the proposed MPPT compared to the fixed-step P & O is presented. The proposed AI algorithm performs significantly better under fast-changing atmospheric conditions, particularly in transient, steady-state, and dynamic responses. In addition to tuning SMC parameters using PSO, our main contribution is improving the performance of the proposed algorithm to effectively track the maximum power point (MPP) at low oscillation, low ripple, low overshoot, and good rapidity in both slow and fast-changing atmospheric conditions. A three-phase grid-connected PV system with an inverter is described in the present work. The proposed strategy is centered around optimizing the controller of a three-phase grid-connected inverter system in order to improve the power quality.

Automated summary

The lack of control in voltage overshoots, transient response, and steady-state error in a grid-connected photovoltaic (PV) system can have negative impacts on battery storage and other grid components. An AI optimization technique is proposed to improve the efficiency of the overall power system by determining the optimal sliding mode controller (SMC) gain. The proposed control strategy utilizes a particle swarm optimization algorithm (PSO) to optimize the SMC gains used in perturb and observe (P & O) algorithms.