Most of the low-voltage distribution networks are three-phase four wire systems. Because of the quantity and dispersion of single-phase power users, the three-phase load unbalanced problem exists in a large number of low-voltage stations. With the continuous increase of power consumption level of the majority of power customers, the three-phase load unbalanced has become increasingly prominent in low-voltage station area. In this paper, the mechanism of three-phase unbalance generation and the defects of traditional treatment methods are introduced. On this basis, a three-phase unbalanced treatment scheme for low-pressure station area is proposed. The scheme can test out three-phase imbalance of low-voltage substation area quickly and accurately, and adjust the load phase sequence in real time without power failure through the intelligent commutation switch system, so that three-phase load in the station area lies a comparatively balanced condition. The scheme can cut down the loss of transformer and line caused through unbalanced three-phase load e in nicely. And it can restrain single-phase overcurrent, and solve the problem of terminal low voltage treatment.
s. Since the current substation robot inspection process exists in the high-voltage switchgear status recognition is highly susceptible to the influence of complex environments such as low image contrast, light clutter interference, and blurred reading status details, this paper proposes an image enhancement and deep learning based substation switchgear state recognition method. A multi-scale Retinex-based image enhancement is proposed to enhance the adaptability of outdoor switchgear images to light changes; improve the YOLOx target detection network to introduces a lightweight ECA attention mechanism without dimensionality reduction based on the original YOLOx model’s backbone network CSPDarknet, allowing the model to learn classification features while also focusing on learning spatial features. The experimental results show that the improved network can accurately identify the boundary information of anomalies, and the quality of its prediction results will not be reduced for noise-containing data, and the network shows strong generalization, robustness, accuracy and rapidity, providing certain conditions for realizing substation equipment condition monitoring.
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