Abstract：In wind farms, collecting lines are short with a large number of wind turbines connected to it, and fault characteristics of wind turbines are different from those of traditional power sources, so dense connection of special power sources is the typical characteristic of collecting lines. The elimination of influence of wind turbines, applicability of collecting lines with line branches and reduction of measurement points are key challenges faced by the fault localization for collecting lines. Given these above challenges, it is difficult for the fault localization method in transmission and distribution networks to apply in wind farms, and the existing fault localization method of collecting lines cannot effectively address all these challenges. Therefore, a new hierarchical fault localization scheme for single-phase-to-ground faults on collecting lines combining graph learning and zero-sequence components is proposed, which can achieve precise fault localization across wind turbines with the applicability of collecting lines containing line branches considered and a few measurement points used. Firstly, the graph data is constructed according to electrical information at measurement points and collecting line topology, and GCN (graph convolution neural network) is used as base-models for graph classification modeling to obtain the identification model of faulty areas which consists of a primary classifier and a secondary classifier; in the process of identification model construction, in order to overcome the impact of sample imbalance, multiple classifier models are connected in series with transfer learning combined, and cost sensitive learning based on Bayes minimum risk is also introduced to post-process classification results. Then, considering the neutral-point grounding type of wind farms and the winding connection characteristic of box-type transformers, a wind-turbine-independent fault distance calculation formula is derived based on zero-sequence components, and thus the fault point in faulty areas can be obtained by zero-sequence components. Besides, a misidentification processing mechanism for faulty areas is also proposed to correct the misidentification which occurs rarely. This above proposed fault localization scheme does not require strict data synchronization, can take into account both of structural and attribute information of collecting lines to identify the faulty area with limited fault data provided by a few measurement points, and can overcome influence of wind turbines to achieve the fault distance calculation. Verification results show that with the graph data constructed, faulty areas can be effectively identified by GCN, and the identification accuracy of all faulty areas reaches over 99%, which is higher than traditional DNN (deep neural network); besides, in the process of dealing with sample imbalance, model series connection increases the total average accuracy of branch faults by 12%, and further this above accuracy achieves another 6.1% improvement with cost sensitive learning introduced, which verifies the feasibility of the sample imbalance treatment strategy in this paper. The fault distance calculation method proposed for faulty areas can overcome the influence of fault locations, fault resistance, wind speed and data asynchronization, and errors of fault distance is no more than 150 m. In addition, the proposed misidentification processing mechanism is also proved to be effective and efficient in correcting misidentification of faulty areas.
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2023, Vol. 38 
