Improved sinh–cosh optimizer for photovoltaic parameter identification

To address the challenge of complex nonlinear optimization problems in photovoltaic (PV) system parameter identification, an improved sinh–cosh optimizer (ISCHO) is proposed. The ISCHO enhances global search capability through tent mapping initialization and generalized quadratic interpolation (GQI) strategies. Evaluated against 10 benchmark functions and six optimization algorithms, the ISCHO demonstrates superior performance in exploration, convergence, accuracy, and robustness, achieving optimal results in 38 out of 40 statistical indicators. The algorithm outperforms the original sinh–cosh optimizer (SCHO) algorithm in both benchmark tests and PV parameter identification tasks, showing improved accuracy, convergence speed and stability. The ISCHO reduces standard deviation by two orders of magnitude compared to the original SCHO and one order of magnitude compared to the sine-memory arithmetic optimization algorithm. This work advances the application of meta-heuristic optimization in renewable energy systems.