With the rapid growth of photovoltaic power generation systems, fire incidents within the system have progressively increased. The lack of thorough studies on the temperature properties of direct current (DC) arc faults has resulted in an unclear ignition mechanism, significantly increasing the fire risk associated with such faults. Hence, this work presents a proposed experimental scheme for detecting photovoltaic DC series arc faults (SAFs) and the corresponding detection standards. Additionally, the temperature characteristics of the DC arc fault are further analyzed. The magnetohydrodynamic (MHD) arc fault simulation model is developed to investigate the temperature-related aspects of photovoltaic DC arc faults. Finally, our experimental validation confirms the precision of the model in simulating arc temperature. It is verified that the research presented in this paper can provide a good explanation for the rise time of DC arc temperature and the characteristic distribution of arc distance. This study elucidates the impact mechanism of line current, power supply voltage, and arc gap size on arc temperature in a photovoltaic system. Additionally, it proposes an evaluation method for assessing the arc fault ignition risk level. This method is essential for safeguarding against arc fault ignition risk in photovoltaic DC series cells.

In light of the global energy shortage, the development of renewable energy has become increasingly vital. With China’s commitment to achieving “carbon peak and carbon neutrality,” photovoltaic power generation has emerged as a focal point in new energy development. However, DC arc faults in photovoltaic systems pose significant safety hazards, potentially leading to electrical fires. While new detection technologies for DC arc faults in photovoltaic power generation systems have advanced rapidly, the diversity of international standards—such as UL 1699 B, GB/T 39750, IEC 63027, and CGC/GF 175—limits both the construction of experimental platforms and the universality of detection technologies. Current research often relies on a single standard to establish experimental platforms, resulting in detection methods with limited applicability and an inability to validate technological effectiveness fully. To address this issue, this paper conducts an in-depth study of four international and national standards (IEC 63027; UL 1699 B, GB/T 39750, and CGC/GF 175), focusing on the discrepancies in decoupling methods, impedance parameter settings, and experimental circuit topologies, including series and parallel arc scenarios. Through comprehensive comparative analysis of multiple standards, this study integrates major international and domestic specifications to develop a multi-standard compatible experimental platform. The platform is designed to accommodate diverse topologies and parameter requirements, enabling efficient collection of arc test data and performance evaluation of arc fault detection devices. It also provides a standardized foundation for the performance testing and classification of DC arc circuit breakers in photovoltaic power generation systems. Through a comprehensive multi-standard comparative analysis, we systematically analyze the technical differences in photovoltaic DC arc detection. We construct a multi-standard compatible experimental platform by integrating mainstream international and domestic standards. This platform is designed to accommodate various topological structures and parameter requirements, facilitating the collection of arcing experimental data and assessment of the performance of arc fault detection devices. The findings from this research provide both theoretical and experimental foundations for developing unified technical guidelines for photovoltaic DC arc protection. This will aid in standardizing the development of detection devices and enhancing the electrical safety of photovoltaic systems. Ultimately, this work is significant for promoting the safe utilization of new energy within the framework of the dual carbon goals. Moving forward, it is crucial to enhance the generalization abilities of detection algorithms further and foster the integration of standards and industrial applications.

AC arc faults detection aims to detect the early arc faults to prevent potential accidents. To develop a small and low-cost testing device for arc fault detection devises (AFDDs), an AC arc faults simulation method is vital. Generally, physical arc models arc used to generate arc fault signals. However, signals can hardly address the high frequency component, such as the transient spikes. From a data-driven perspective, we propose an electric AC arc faults simulation method using measured data based on Principal Component Analysis (PCA) to simulate more accurate and more realistic arc current signals. Without extra physical models, this method can adaptively simulate actual AC arc fault current signals in different application scenarios using a few measured data. Based on a set of standard orthogonal basis derived from PCA, the simulated AC arc current signals can maintain the time and frequency features more effectively than classical models. Compared with other simulated arc current signals, real-world experimental results indicate that the proposed method can simulate more accurate and more actual AC arc current signals.

As the global energy transition moves towards the goal of low-carbon sustainability, it is crucial to build a new energy power system. The performance and reliability of Smart Circuit Breakers are the key to ensuring safe operation. The control circuit is the key to the reliability of Smart Circuit Breakers, so studying its performance-degradation process is of great significance. This study centers on the development of a degradation model and the performance-degradation-assessment method for the control circuit of Smart Circuit Breakers and proposes a novel approach for lifetime prediction. Firstly, a test platform is established to collect necessary data for developing a performance-degradation model based on the two-stage Wiener process. According to the theory of maximum likelihood estimation and Schwarz information criterion, the estimation method of model distribution parameters in each degradation stage and the degradation ‘turning point’ method are studied. Then, reliability along with residual life serve as evaluation criteria for analyzing the control circuit’s performance deterioration. Taking the degradation characteristic data into the degradation model, for example, analysis, combined with the Arrhenius empirical formula, the reliability function at room temperature and the curve of the residual life probability density function is obtained. Ultimately, the average service life of the Smart Circuit Breaker control circuit at room temperature is 178,100 h (20.3 years), with a degradation turning point at 155,000 h (17.7 years), providing a basis for the lifetime evaluation of low-voltage circuit breakers.

With the development of intelligent grid systems, smart circuit breakers are widely used. The control circuit is the core component of the smart circuit breaker, making its performance degradation characteristics highly significant. This paper focuses on the control circuit’s accelerated degradation test and performance degradation characteristics. First, an accelerated degradation test is designed, and a test platform is established. By analyzing the degradation mechanism of the intelligent circuit breaker control loop, the key weak links in the control loop are determined, and then the monitoring quantity is determined. Then, degradation data are preprocessed to extract features from the time, frequency, and wavelet domains. The multidimensional evaluation index model is applied to select the optimal features, fit the degradation trend, and use the fixed segmentation algorithm to divide the degradation stages and analyze the performance degradation characteristics of the control circuit. The experimental results show that the turning points of the two-stage degradation process at 85 °C, 95 °C, and 105 °C are 78.8%, 77.6%, and 77.0%, respectively. The position of the turning point is relatively fixed. The key circuit’s PSpice simulation model is built to verify the two-stage nonlinear characteristics observed in the experimental results. Finally, the results are verified by the Pearson correlation coefficient. The results show that the Pearson correlation coefficient between the simulation and accelerated life test results is above 0.9158, and the consistency between the two is high.

In the operation of a power distribution system, miniature circuit breakers (MCBs) are subjected to the synergistic effect of electrical and mechanical stresses in service, and their operational performance is progressively degraded, which is prone to bring significant losses to the users after failures occur. In order to accurately predict the remaining electrical life of MCBs in service, MCB mechanical characterization and dynamic simulation are carried out, and the initial closing angle of MCBs is selected as the degradation characteristic quantity, so as to deeply analyze the evolutionary characteristics of the initial closing angle in the degradation of MCBs and to construct the electrical degradation model of the one-dimensional linear Wiener process in the present study. With the help of the Monte Carlo method, we carry out the electric life simulation analysis to investigate the intrinsic correlation between the degradation of electric performance and the initial closing angle, and we implement the electric life experiment under the 63 A working condition to analyze the dynamic change in the stiffening angle of the test samples. The parameters of the electrical performance degradation model are identified through the synergistic driving of the electrical life simulation data and the experimental data, the remaining electrical life prediction is realized based on the degradation data of the same batch of products, and the maximum prediction error of the proposed method is controlled within 15%.