Internal Overvoltage Identification of Distribution Network via Time-Frequency Atomic Decomposition

Internal overvoltage accidents in the distribution network are likely to cause an equipment insulation breakdown and result in system power outages and economic losses. Therefore, an internal overvoltage identification method based on the time-frequency atomic decomposition is investigated in this study. Firstly, the overvoltage waveforms are divided into four time periods. Then, the waveforms during these four time periods are decomposed by the atomic decomposition algorithm to obtain the effective atoms from the waveforms. Moreover, the root-mean-square (RMS) value of the zero-sequence voltage, the dominant atom frequency, the total relative matching degree, and the effective atom frequency are extracted as major features. In addition, the layered identification of the overvoltage types can be realized by combining the corresponding identification criteria. The salient advantage is the features with low dimension and a high degree of discrimination. Various overvoltage types can be identified just by the corresponding thresholds, and it is easier to deploy in the field than conventional methods based on classifier training. The effectiveness of the proposed method is verified by experimental results, and it concludes that the proposed algorithm has high accuracy and strong adaptability.