The converter transformer is an important equipment in the bulk power and long-distance power transmission projects, the oil-pressboard insulation at the rectifying valve side, which mainly deals with combined AC-DC voltage, requires more study of discharge and its influence to ensure equipment safety. In this research, an oil-pressboard insulated experimental platform with needle and plate electrodes, which is similar to the sharp defects of components such as the corner ring and the gasket of converter transformer, is established, and photos of PD are used to analyze the influence of PD under the above experimental conditions, then the generation and evolution of various PDs and their by-products can be researched in detail, which can influence the electrical aging process of oil-pressboard insulation. Results indicate that the main by-products brought by PD are bubbles and paper fibers, which can affect the proportion and development of subsequent PDs. These results can provide experimental support to research the internal mechanism of the electrical aging phenomenon of converter transformer, especially at its rectifying valve side.

With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets.