基于深度置信网络的电网故障类型辨识

高效可靠的电网故障分类有利于指导调控人员快速排查和消除故障、恢复系统供电,对保障系统安全可靠运行具有重要意义。为了克服浅层智能方法对信号处理技术和人工经验的依赖以及对复杂电力系统特征提取和表达的不足,文中基于故障录波信息,提出一种基于深度置信网络的电网故障类型辨识方法。直接以故障发生后的各相电流/电压以及零序电流/电压作为网络输入,从原始时域信号中自动学习和提取故障状态特征,从而实现故障类型的辨识。IEEE 39节点仿真系统案例和电网实际故障案例均表明该方法具有良好的故障特征提取能力,在数据降维过程中能保持数据原本的特征,且辨识结果不受过渡电阻、故障时刻、故障位置、负荷大小等因素的影响,与传统人工神经网络模型相比其识别准确率更高。

Fault types identification of power grid based on deep belief network

Efficient and reliable fault classification is beneficial to guide the dispatchers in finding and removing the fault quickly, thus restoring system power supply promptly, so the classification is of great significance for ensuring the safe and reliable operation of the system. A fault classification method based on deep belief network is proposed to overcome deficiencies of traditional fault classification, such as shallow intelligent methods'' dependence on signal processing technology and artificial experience, and the lack of feature extraction and expression for complex power system. The raw data of each phase current- voltage and zero sequence current-voltage are taken as the network input, and the features of fault state are automatically learned and extracted from the original time-domain signals to realize the fault type identification. The simulation results of the IEEE 39-bus system and real fault cases of power grid show that the proposed fault type identification method has good capability of fault feature extraction. Besides, the proposed method keeps the original characteristics of data in the process of dimensionality reduction, and it is not affected by factors including transition resistance, fault time, fault location and load size. Therefore, it identifies fault types more accurately than other traditional artificial neural networks do.