Abstract:

Objectives Photovoltaic arrays operating under complex outdoor conditions encounter various fault types with varying degrees of severity. To accurately assess the working status of photovoltaic arrays, a fault diagnosis method based on an improved grey wolf optimized extreme learning machine (IGWO-ELM) is proposed. Methods Firstly, nine fault simulation output characteristics are analyzed, and a five-dimensional fault feature vector is established, consisting of short-circuit current, open-circuit voltage, maximum power point current, maximum power point voltage, and fill factor. Secondly, to address the limitations of the grey wolf algorithm, such as uneven distribution of initial position and imbalance between global search and local exploitation, Circle mapping and nonlinear convergence factors are incorporated. An improved grey wolf optimization algorithm is then proposed, which optimizes the input layer weights and hidden layer node biases of the extreme learning machine to improve performance. Finally, simulation models and experimental platforms are developed to collect fault data, which are divided using K-fold cross validation. The data are input into the IGWO-ELM model for accuracy verification and compared with other algorithms. Results The IGWO-ELM model demonstrates high recognition rates for various fault types in photovoltaic arrays, achieving classification accuracy of 98.32% and 95.48% for simulation and experimental data, respectively. Conclusions The fault diagnosis method based on IGWO-ELM offers high accuracy, requires fewer iterations, and achieves fast convergence speed, effectively judging the working state of photovoltaic arrays.

Key words: solar power generation, photovoltaic arrays, fault diagnosis, improved grey wolf optimization (IGWO) algorithm, extreme learning machine (ELM), K-fold cross validation, feature extraction, simulation