Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

Dissolved gas analysis (DGA) is a widely used method for diagnosing internal transformer defects. The traditional single intelligent diagnostic method cannot efficiently process large amounts of incomplete defect information with DGA, which affects the accuracy of fault diagnosis. To this end, this paper proposes a transformer fault diagnosis method based on the optimization of kernel parameters and weight parameters of a kernel extreme learning machine (KELM). First, based on Mercer's theorem, we combine the radial basis kernel function with the polynomial kernel function to construct a new hybrid kernel function. Then, the gray wolf optimization (GWO) algorithm and the differential evolution (DE) algorithm are combined to improve the diversity of the gray wolf population, enhance the searchability of GWO, and prevent GWO from falling into a local optimum during the iterative process. Finally, the kernel parameters and weight parameters of the hybrid kernel function are optimized by using the modified grey wolf optimization (MGWO) algorithm. The International Electrotechnical Commission Technical Committee (IEC TC) 10 transformer fault data and constructed hybrid feature set is used as the input set of the model, the model is simulated and analyzed, and the transformer fault data collected at a site are used for training and verification. The simulation results on the two sets of data show that the method can accurately and effectively diagnose transformer faults, and has a higher fault diagnosis accuracy rate than traditional methods.

This research aims to optimize the capacity and location of distributed energy resources in the distribution network using a metaheuristic algorithm that uses the Improved Beluga Whale Optimization Algorithm(IBWO). This algorithm incorporates the elite reverse learning strategy and cyclone foraging strategy while adjusting the balance factor to enhance the diversity of the algorithm population and further balance local search capability with global search capability. This study optimizes the configuration of distributed energy resources considering the uncertainty and correlation of wind power, photovoltaic power, and load. The optimization objective is to reduce active power loss, improve voltage stability, and minimize investment and operating costs. By conducting simulations on IEEE 33-bus and IEEE 118-bus test cases, the active power network losses are enhanced by 55.49% and 45.39% respectively, and the algorithm outperforms other methods regarding other data, demonstrating its superiority and effectiveness.

Distributed generation equipment improves renewable energy utilization and economic benefits through an energy storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on the dynamic change of load, which leads to a large deviation from the expected return. Considering the system characteristics of lack of data and less information, after introducing the grey theory, we propose a new long-term capacity configuration method for ESS and establish the long-term grey forecasting model (GFM) of user load, improving the basic forecasting model to improve the accuracy of the long-term forecasting model. Then, the scheduling model is established with the maximum economic and social benefits as the optimization objective. Based on the forecast data of the improved grey forecasting model (IGFM), the hierarchical solution method is used to solve the scheduling model. Finally, the parameters are configured based on the service life of the equipment and the expected rate of return. The simulation results show that higher accuracy is realized in the improved prediction model, and the improved algorithm gets higher convergence speed and precision. Apart from that, the nonlinear correlation trend of the EES return rate between the capacity and life cycle is revealed. Compared with the ESS configuration in a short period, this study provides more comprehensive and accurate data support for the capacity configuration of the ESS, reducing the error between the actual return and the expected return significantly.

In order to solve the problems of difficulty in extracting effective features from dissolved gases in transformer oil and limited recognition accuracy of the fault diagnosis model, a feature selection and improved black-winged kite algorithm (IBKA) optimized support vector machine (SVM) transformer fault diagnosis method based on dissolved gas analysis (DGA) in oil is proposed. Firstly, a hybrid feature selection method is used to perform quantitative analysis on the constructed 20-dimensional fault candidate feature set, thereby achieving the selection of feature variables. Then, the Tent chaotic mapping, the Gompertz growth model, and the Morlet wavelet variation strategy are introduced to improve the Black-Winged Kite Algorithm (BKA) to enhance its optimization searching performance; then, the IBKA is used to optimize the hyperparameters such as kernel function and penalty factor of SVM to improve the accuracy of model diagnosis results. Finally, case analysis based on 410 sets of IEC TC10 transformer fault data shows that the fault diagnosis accuracy of the proposed method reaches 98.37%, which verifies the effectiveness of the proposed method for classifying faults according to the IEC TC10 method.

Online partial discharge (PD) detection for power cables is one reliable means of monitoring their health. However, strong interference by white noise poses a major challenge in the process of collecting information on partial discharge signals. To solve the problem whereby the wavelet threshold estimation based on sample entropy falls into the local optimal and the wavelet noise reduction makes it difficult to process detailed information, we propose a partial discharge signal noise reduction method based on a combination of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and discrete wavelet transform (DWT) with multiscale sample entropy (MSE). Firstly, the ICEEMDAN method was used to decompose the original sequence into multiple intrinsic mode components. The intrinsic mode function (IMF) components were grouped using the mutual information method, and high-frequency noise was eliminated using the kurtosis criterion. Next, an MSE model was established to optimize the wavelet threshold, and wavelet noise reduction was applied to the effective component. The ICEEMDAN-MSE-DWT method can retain effective information while achieving complete denoising, which alleviates the problem of information loss that occurs after denoising using the wavelet method. Lastly, as shown by our simulation and experimental results, the proposed method can effectively realize noise reduction for power cable partial discharge signals, thus providing an effective method.