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The flux-corrected transport (FCT) algorithm is used to simulate the two-dimensional planar needle-plate structure of the positive streamer model and the partial discharge process of tip defects. The variations in electron density, net charge density and electric field during the movement of the positive streamer are obtained. By deducing the relationship between photon flux and photoionization term, the photon distribution function is analyzed, and the variation law of the photon flux (the number density of photons generated per second) of the streamer moving in the air is obtained. The results show that the maximum luminous point concentrates on the fast-moving head of the positive streamer in the air. The photon flux (the number of photons produced per second) is approximately exponential with the distance from the cathode, with a maximum of approximately 1.4×1028 s-1·m-3.

Optical detection of partial discharge (PD) is an important way to reflect the insulation status of equipment. C4F7N/CO2 gas mixture is one of the most potential substitutes for SF6 at present, but there is a lack of research on its optical PD characteristics and diagnostic methods. In this paper, a PD multispectral array detection platform that can collect 7 characteristic bands is constructed, and 4 kinds of PD defects are produced. The similarities and differences of the PD multispectral characteristics in phase distribution, energy distribution, and feature stacking map under the conditions of 5 different ratios of C4F7N/CO2 gas mixture and pure SF6 gas are analyzed. Finally, a novel method of PD diagnosis based on multispectral features (MF) and k-nearest neighbors (KNN) is proposed. The experimental results show that the fault recognition accuracy in pure SF6 can reach 96.2%. The recognition rate of C4F7N/CO2 gas mixture is above 88%, and the highest accuracy rate is 91.1%. This method has a guiding significance for the PD diagnosis of environmentally friendly gas-insulated equipment, and provides a new route for traditional PD detection and diagnosis.

Introduction: Machine learning-based power transformer fault diagnosis methods often grapple with the challenge of imbalanced fault case distributions across different categories, potentially degrading diagnostic accuracy. To address this issue and enhance the accuracy and operational efficiency of power transformer fault diagnosis models, this paper presents a novel fault diagnosis model that integrates Neighborhood Component Analysis (NCA) and k-Nearest Neighbor (KNN) learning, with the incorporation of correction factors. Methods: The methodology begins by introducing a correction factor into the objective function of the NCA algorithm to reduce the impact of sample imbalance on model training. We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm's training metric matrix. The metric matrix obtained from training is then applied to perform a mapping transformation on the input data for the KNN classifier, thereby reducing the distance between similar samples and enhancing KNN classification performance. Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy. Results: Analysis of the transformer fault case library reveals that the model proposed in this paper reduces diagnostic time by nearly half compared to traditional machine learning diagnosis models. Additionally, the accuracy for minority sample classes is improved by at least 15% compared to other models. Discussion: The integration of NCA and KNN with correction factors not only mitigates the effects of sample imbalance but also significantly enhances the operational efficiency and diagnostic accuracy of power transformer fault diagnosis. The proposed model's performance improvements highlight the potential of this approach for practical applications in the field of power transformer maintenance and diagnostics. [ABSTRACT FROM AUTHOR]

Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, China, 2Guangdong Power Grid Co., Ltd., Guangzhou, China, 3China Southern Power Grid Co., Ltd., Guangzhou, China To achieve load management optimization and timely failure warning for power transformers, as well as improve the reliability of the power network, this paper proposes a multiple time scale prediction method for top oil temperature (TOT) based on an adaptive extended Kalman filter (AEKF) algorithm. This method combines the Kalman filter (KF) algorithm and the D. Susa thermal model. The TOT, oil exponent and oil time constant are taken as state variables, while the ambient temperature and load current are used as input variables. The iterative optimization of the oil exponent and oil time constant is realized by comparing the estimated and observed TOT values. Moreover, the proposed method utilizes an adaptive noise estimator to correct the noise statistics parameters, which simplifies the initial noise setting and thus further improves the TOT prediction accuracy. A case study is conducted with two 110 kV transformers. The results show that comparing the thermal equivalent circuit model and the extended KF algorithm, the proposed method has a higher accuracy in the intraday ultra-short-term prediction on a 15-min time scale and day-ahead short-term prediction on a 24-h time scale for the TOT. [ABSTRACT FROM AUTHOR]

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. [ABSTRACT FROM AUTHOR]