Polarization is a universal phenomenon that occurs inside lithium-ion batteries especially during operation, and whether it can be accurately characterized affects the accuracy of the battery management system. Model-based approaches are commonly adopted in studies of the characterization of polarization. Towards the application of the battery management system, a lumped diffusion model with three parameters was adopted. In addition, a joint algorithm composed of the Particle Swarm Optimization algorithm and the Levenberg-Marquardt method is proposed to identify model parameters. Verification experiments showed that this proposed algorithm can significantly improve the accuracy of model output voltages compared to the Particle Swarm Optimization algorithm alone and the Levenberg-Marquardt method alone. Furthermore, to verify the real-time performance of the proposed method, a hardware implementation platform was built, and this system’s performance was tested under actual operating conditions. Results show that the hardware platform is capable of realizing the basic function of quantitative polarization voltage characterization, and the updating frequency of relevant parameters can reach 1 Hz, showing good real-time performance.

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this article proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attention-directed mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features, and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method.

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