Research on MPPT Control Based on Novel Intelligent Fusion Algorithm in Photovoltaic-Storage DC Grid-Connected Systems

In light of the low efficiency and unstable power transmission capacity of grid-connected energy storage in photovoltaic (PV) systems, this paper proposes a maximum power point tracking (MPPT) control strategy based on a novel intelligent fusion algorithm. We begin by highlighting that improving the power generation efficiency of PV systems under non-ideal conditions—such as partial shading—is a key challenge for increasing the utilization rate of renewable energy and promoting the sustainability of energy systems. The proposed strategy integrates two complementary search algorithms: the Whale Optimization Algorithm (WOA) for global exploration and the Sparrow Search Algorithm (SSA) for local exploitation. These are combined through a weighted superposition mechanism to enhance the overall search balance. First, a Chaotic Map is used to initialize the populations of both WOA and SSA, while population weights are restructured to improve diversity. Subsequently, a weighted superposition mechanism reorganizes the initialized populations to generate a fused WOSSA population, enabling a global search that produces and evolves a set of optimal solutions across the entire search space, further enhancing search diversity. Then, a local search is applied to selected high-quality individuals to prevent premature convergence and rapidly exploit promising regions. Finally, boundary-handling functions and a power restart mechanism are introduced during the population position update phase to refine position updates in the WOSSA algorithm. This optimizes the iterative process, accelerates convergence, and strengthens the algorithm’s ability to escape local optima. The proposed algorithm is simulated in MATLAB. Simulation results demonstrate that, compared with the SSA algorithm, the convergence speed is improved by approximately 55%, the maximum power tracking performance is enhanced by about 70%, and the voltage of the energy storage unit remains above 380 V. Experimental validation further shows that the PV system achieves an average daily output of 425.5 V and 4.3 A, a grid frequency of 49.9 Hz, a daily energy yield of 0.5 kWh, and a power generation efficiency per unit installation area of 2 kW. The method also exhibits good performance in improving the quality of grid-connected power.