Guoqing An

This paper presents a Caputo-type fractional calculus sliding mode control method for attitude detection in spherical motor wireless power transfer (WPT) systems. It introduces a rapid rotor attitude estimation method using inductance estimation, avoiding complex finite element analysis (FEA), and integrates this with magnetic field analysis to ensure accurate detection. Additionally, a Caputo-type fractional calculus sliding mode control method is proposed for the trajectory tracking problem of the spherical motor motion system, combining fractional calculus with sliding mode control by constructing fractional sliding surfaces and fractional reaching laws, with stability of the method proven by the Lyapunov theorem. Subsequently, the method is compared with traditional sliding mode control simulations, demonstrating a significant reduction in input signal oscillation and improved trajectory tracking performance of the spherical motor. A comparison of WPT attitude detection methods with visual improved fast discriminative scale space tracking (IFDSST) algorithm and micro electro mechanical system (MEMS) fusion algorithms highlights the system's real-time capabilities. The results indicate that the angular error of the WPT attitude detection method is consistently below 2%. Finally, the effectiveness of this method was validated through closed-loop control experiments, providing experimental evidence for its performance.

Partial discharge signals are often interfered by white noise and narrowband noise, which makes it difficult to locate the partial discharge source. To tackle this intricate issue, this paper proposes an improved variational Mode Decomposition (VMD) noise reduction method. Initially, the method optimizes the VMD by employing the Crested Porcupine Optimizer (CPO). Subsequently, modal screening is carried out in accordance with a specific criterion. Finally, noise reduction is implemented through an improved 3σ method. Via noise - reduction simulation experiments, the superiority of this method is validated, and comparisons are made with several other methods, wherein this method is capable of boosting the Signal - to - Noise Ratio (SNR) by up to 50, elevating the correlation coefficient to over 0.99, and reducing the Mean Squared Error (MSE) by 99%. Meanwhile, the practicality of this method is confirmed through localization experiments.

In order to locate the transformer partial discharge position, the time reversal method is proposed. However, a single sensor is highly susceptible to noise during positioning. Therefore, this paper proposes the PSO-VMD-FastICA algorithm for denoising. The algorithm makes full use of the advantages of VMD variable parameters, optimizes through PSO, and further decomposes by using FastICA. The effectiveness of PSO-VMD-FastICA is verified by comparing the positioning results with those of the time inversion method without noise interference and the ordinary VMD algorithm.

Partial discharge pattern recognition has been established as a standard diagnostic tool for monitoring the operation of electrical equipment. Intelligent state recognition is the development trend of transformer state recognition, but the existing intelligent state recognition has the disadvantages of single model and low recognition accuracy. In order to overcome this shortcoming, a multi-dimensional information source transformer partial discharge fault identification method based on D-S evidence theory is proposed. Firstly, wavelet packet decomposition is used to extract the energy features of high frequency partial discharge signal and ultrasonic signal. Then, according to the selected feature set, the convolutional neural network (CNN) model and CNN-SVM model are established respectively. Finally, the Dempster-Shafer(D-S) evidence theory is used to combine the output results of the two signal recognition models. The results show that using the proposed wavelet packet decomposition energy feature set as input vector, the recognition rates of the two signals CNN-SVM models reach 95% and 81.67%, which are 3.33% and 8.34% higher than CNN respectively. The overall performance of D-S evidence theory fusion method is better than that of CNN and CNN-SVM, and the accuracy and consistency are improved by 3.33% and 16.66% respectively. The effectiveness and feasibility of this method are verified.

Aiming at the problem of unsatisfactory diagnosis performance of conventional fault diagnosis methods for transformer, a novel method based on maximally collapsing metric learning algorithm (MCML) and parameter optimization kernel extreme learning machine (KELM) is proposed in this study. First, a new set of dissolved gas analysis (DGA) features combination, which can reflect the transformer fault information, is used to form the input feature space. Then, the MCML is employed to reduce the feature space dimension to extract a set of optimal DGA features combination. Finally, the salp swarm algorithm (SSA) is utilized to optimize the parameters in KELM to establish an SSA‐KELM model, which is adopted to diagnose and identify transformer faults. The proposed method is applied to the International Electrotechnical Commission (IEC) TC 10 database, and the results show that the feature extraction effect of MCML is superior than that of linear discriminant analysis, neighborhood preserving embedding, and Laplacian eigenmaps. The optimal DGA feature set is more advantageous than the frequent‐used DGA data, IEC ratios, Rogers ratios, and Doernenburg Ratios. The diagnosis accuracy of SSA‐KELM is better than that of KELM, particle swarm optimization‐KELM, genetic algorithm‐KELM, and loin swarm optimization‐KELM. Furthermore, the generalization and robustness ability of the MCML and SSA‐KELM is confirmed by the China DGA samples, the obtained results verify the reliability and validity of the proposed method again. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Dissolved gas analysis (DGA) method is widely used to detect the incipient fault of power transformers. This paper presents a novel DGA method for power transformer fault diagnosis based on Harris-Hawks-optimization (HHO) algorithm optimized kernel extreme learning machine (KELM). The non-code ratios of the gases are used as the characterizing vector for the KELM model, and the Harris-Hawks-optimization (HHO) algorithm is introduced to optimize the KELM parameters, which promotes the fault diagnostic performance of KELM. Based on dataset collected from IEC TC 10, the fault diagnosis capability of the proposed method is validated by different characterizing vectors and is compared with conventional KELM and other optimized KELM. Moreover, the generalization ability of the proposed method is confirmed by China DGA data. The results demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy.

Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.