Abstract
Fault location is one of the most essential techniques to maintain the stable operation of power systems. A fast and accurate fault location allows operators to restore power grids faster and avoid economic losses. Conventional methods rely on expert knowledge to extract the necessary features (e.g. DWT, DFT). For large systems, more coupling effects of transmission lines require more complex feature engineering, and incomplete features can easily introduce large errors. To overcome this, a deep learning approach without manual feature extraction is introduced to the fault location model under big data application. Towards this end, in the proposed method, the attention mechanism, the Bi-GRU and a dual structure network are applied to analyze the current data from different perspectives. Complete information for the fault features is extracted for the fault location. Based on a time series model and benefit from the ability to internally acquire the information architecture of faulty line, the established model is adaptive to the power grids with very complex topologies. Simulation results indicate that the proposed double-structure model reduces the maximum error and is less affected by noise. In comparison with different structures and different models, the proposed method shows better performance in IEEE 39-bus system.
Highlights
The power system is one of the most complex man-made systems in the world
The fault location technology developed for distribution networks can be divided mainly into three types: (1) methods based on impedance, (2)methods based on traveling wave, and (3) methods based on training [1]
The proposed method is tested on the IEEE39 bus system, and different fault types and fault locations are set to prove the robustness of the system
Summary
The power system is one of the most complex man-made systems in the world. Due to the aging of transmission lines, the limits of their operation are approaching. The regression tree has been used to determine the fault position, but the proposed method has been identified that the results under some experimental samples have large error In this mode, the learning algorithm is responsible for discovering the complicated relationship between the hidden rules and the pattern features [16]. Using traditional machine learning algorithms for high-precision regression tasks, especially in large power grids, is conventionally difficult to meet the low error of fault location. The method can determine fault position and the fault line by extracting and analyzing the line’s data for current measurements. In order to locate and classify faults, the proposed algorithm learns the time series of the current, the fault position and type by mapping their relationships. The proposed method is tested on the IEEE39 bus system, and different fault types and fault locations are set to prove the robustness of the system
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