Series arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detection method based on time–frequency composite recurrence plots (TFCRPs). Initially, variational mode decomposition (VMD) is employed to decompose the current into distinct modes. Subsequently, the proposed TFCRP transforms these modes into two-dimensional matrices, enabling the measurement of composite similarity between different phase states. Lastly, extra tree (ET) is utilized to fuse the fractional recurrence entropy (FRE) and the singular values extracted from the matrices, thereby achieving SAF detection. Experimental results indicate that the proposed method achieves a detection accuracy of 98.75% and can accurately detect SAFs under various operating conditions. Comparisons with different methods further highlight the advancement of the proposed method. Furthermore, the detection time of the proposed method (209 ms) meets the requirements of standard UL1699B.

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation.

Accurate magnetic core loss prediction is important for efficiency evaluation and thermal design in high-frequency power electronic converters. Under varying temperature and frequency conditions, core loss exhibits strong nonlinear behavior, making conventional fixed-parameter Steinmetz-type models difficult to maintain stable accuracy. To address this problem, this work proposes a temperature–frequency-dependent core loss prediction method under triangular-wave excitation by combining the improved Generalized Steinmetz Equation (iGSE), dynamic Fitness–Distance Balance Teaching-Learning-Studying-Based Artificial Bee Colony (dFDB-TLSABC), and a gated recurrent unit (GRU) network. The dFDB-TLSABC algorithm identifies iGSE parameters under different operating conditions, and GRU maps temperature and frequency to the corresponding parameters for dynamic loss calculation. Based on triangular-wave data for N87, 3C90, and 77 ferrites from the MagNet database, the proposed model reduces the average prediction error by at least 61.87% and 61.60% relative to the conventional iGSE model and the Improved Steinmetz Equation (ISE) model, respectively. Compared with Long Short-Term Memory Network (LSTM), Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and polynomial regression (PR) models, the reductions are 5.91%, 28.50%, 32.26%, 3.80%, and 2.64%, respectively. These results indicate improved prediction accuracy under temperature–frequency variations.