Research on MPPT control strategy based on LSTM and IHOA algorithm

To obtain a more efficient photovoltaic system, it is inevitable to adopt optimum maximum power point tracking (MPPT) algorithm. Swarm intelligence optimization algorithm is one of the most commonly used MPPT algorithms. However, they are prone to getting trapped in local optima, with slow convergence speed. This paper proposes a novel hybrid MPPT strategy (LSTM-IHOA) that integrates a Long Short-Term Memory (LSTM) network with an Improved Hiking Optimization Algorithm (IHOA). First, the proposed MPPT utilizes the LSTM to predict real-time duty cycle references using environmental parameters (irradiance and temperature), guiding IHOA with accurate initial search directions. Then, IHOA is applied to accurately find the duty cycle corresponding to the maximum power point. Especially, IHOA employs a Cauchy mutation strategy to escape local optima, incorporates a boundary particle recovery mechanism based on the duty cycle reference to constrain search space, and adopts a leader-following mechanism to accelerate local convergence. Quantitative validations under six operating scenarios demonstrate its superiority: the proposed MPPT strategy (LSTM-IHOA) can reduce the tracking time (reduced by 30 %), with power tracking achieving higher accuracy, smaller power oscillations, and greater efficiency (no <99.96 % tracking efficiency), outperforming four compared algorithms in both static and dynamic scenarios.