Lightning is one of the most common transient interferences on overhead transmission lines of high-voltage direct current (HVDC) systems. Accurate and effective recognition of faults and disturbances caused by lightning strokes is crucial in transient protections such as traveling wave protection. Traditional recognition methods which adopt feature extraction and classification models rely heavily on the performance of signal processing and practical operation experiences. Misjudgments occur due to the poor generalization performance of recognition models. To improve the recognition rates and reliability of transient protection, this paper proposes a transient recognition method based on the deep belief network. The normalized line-mode components of transient currents on HVDC transmission lines are analyzed by a deep belief network which is properly designed. The feature learning process of the deep belief network can discover the inherent characteristics and improve recognition accuracy. Simulations are carried out to verify the effectiveness of the proposed method. Results demonstrate that the proposed method performs well in various scenarios and shows higher potential in practical applications than traditional machine learning based ones.

中文翻译：

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基于深信度网络的VSC-HVDC端到端瞬态识别方法

雷电是高压直流（HVDC）系统的架空输电线路上最常见的瞬态干扰之一。准确有效地识别由雷击引起的故障和干扰对于瞬态保护（例如行波保护）至关重要。采用特征提取和分类模型的传统识别方法在很大程度上取决于信号处理的性能和实际操作经验。由于识别模型的泛化性能较差，因此会发生误判。为了提高暂态保护的识别率和可靠性，提出了一种基于深度置信网络的暂态识别方法。高压直流输电线路上的瞬态电流的归一化线模式分量由经过适当设计的深度置信网络进行分析。深度信念网络的特征学习过程可以发现内在特征并提高识别精度。通过仿真验证了所提方法的有效性。结果表明，与传统的基于机器学习的方法相比，该方法在各种情况下均具有良好的性能，并且在实际应用中显示出更高的潜力。