In response to the significant fluctuations in PV power generation due to weather changes, this paper proposes a time-series modeling approach. The proposed method integrates random weather sampling with fluctuation characteristic simulation techniques, effectively characterizing the uncertainty of PV power. Initially, the characteristics and the fluctuation of PV power under different weather conditions are analyzed, and the impact of weather factors on PV power fluctuations is quantified using the “no-shade coefficient”. Subsequently, based on statistical analysis of historical weather data, a Markov chain-based weather type transition model is constructed to accurately capture the transition patterns between weather types. On this foundation, combined with the Gaussian Mixture Model (GMM) and the Normal Distribution, mean value and fluctuation value models of the PV power no-shade coefficient are established, respectively. These two models collectively provide a detailed depiction of PV power fluctuations. Finally, the validity and accuracy of the proposed modeling method are verified using actual operational data from a PV power station in Wuqing, Tianjin. This method provides a modeling basis and algorithmic support for the new electricity system planning and production process simulation.

High-voltage switchgear plays a crucial role in modern industrial and electrical systems, used for controlling and safeguarding electrical equipment from overloads, short circuits, and other electrical faults. However, these switchgears generate significant heat during operation, making accurate prediction and timely alerting of abnormal temperature changes essential for preventing equipment overheating and extending its lifespan. To establish an efficient temperature warning system, real-time temperature data from optic fiber temperature sensing system was studied in this work. Initially, multi-type machine learning algorithms including Lasso Regression, Random Forest, AdaBoost, SVM, KNN, and GradientBoost were tested and compared. Experimental results revealed that the Random Forest algorithm performed the best in predicting high-voltage switchgear temperatures. By combining predictions from multiple decision trees, this algorithm effectively captures complex temperature variations, providing highly precise forecasts. Leveraging the predictive capabilities of the Random Forest model, temperature warnings were generated for different time intervals. Experimental findings demonstrate that the Random Forest algorithm could effectively forecast temperature trends for 10 minutes, 2, 4, and 8 hours ahead, thereby enabling timely detection of potential overheating risks and facilitating necessary maintenance measures.

The existing evaluation methods mainly focus on the basic measurement accuracy of electric energy meters, without fully considering the anti-interference ability of electric energy meters under different electromagnetic interference intensities. In order to comprehensively evaluate the metrological performance of electric energy meters in complex electromagnetic field environments, this article adopted a comprehensive and systematic AHP-EWM (Analytical Hierarchy Process-Entropy Weight Method) comprehensive evaluation algorithm. This article used the Finite Element Method (FEM) to simulate complex electromagnetic environments, designed a multidimensional performance index system, and combined AHP and FEM to construct a comprehensive evaluation model for systematically evaluating the metrological performance of Intelligent Energy Meters (IEM) in complex electromagnetic environments. The experimental results show that the measurement error of smart energy meters significantly increases with the increase of interference intensity. AHP-EWM has shown high consistency and reliability under different electromagnetic interference intensity conditions and multiple repeated experimental tests. The weight allocation of various indicators remains relatively stable in different testing environments, and the comprehensive evaluation score fluctuates slightly, with a range of only 0.01 to 0.03. The AHP-EWM model can overcome electromagnetic interference of different intensities in practical applications and conduct comprehensive IEM metrological performance evaluation.