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Voltage-based protection of microgrids using decision tree algorithms

机译：使用决策树算法的基于电压的微电网保护

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摘要

This paper presents a protection method for microgrids by data mining of voltage disturbances. The occurrence of a fault on the system is associated with a sudden voltage depression, which is fast detected by the adaptive cumulative sum (ACUSUM) algorithm. There are other operational (no-fault) events causing voltage depression such as motor starting, transformer energizing, and capacitor or heavy load switching. In order to discriminate between the fault and no-fault events, one cycle of the voltage waveform is preprocessed by the short-time Fourier transform (STFT) to extract and construct effective features of the disturbance. The features are then used in the decision trees (DTs) for the discrimination. The proposed protection method is tested for fault or no-fault conditions of grid-connected or islanded mode of the microgrid operation, as well as radial or meshed topology. The proposed method also identifies the fault type and faulted phase(s) for selective phase tripping. The immunity of the method against different noise levels is investigated. It is shown by the simulation study that by using only two features for symmetrical events and six features for asymmetrical events, any fault can be detected accurately.
机译：本文提出了一种通过电压扰动数据挖掘的微电网保护方法。系统故障的发生与突然的电压下降有关，可以通过自适应累积和（ACUSUM）算法快速检测到该电压下降。还有其他操作（无故障）事件会导致电压下降，例如电动机启动，变压器通电以及电容器或重载开关。为了区分故障事件和无故障事件，通过短时傅立叶变换（STFT）对电压波形的一个周期进行预处理，以提取和构建扰动的有效特征。然后将特征用于决策树（DT）中。针对微电网运行的并网或孤岛模式的故障或无故障情况，以及径向或网状拓扑，对提出的保护方法进行了测试。所提出的方法还识别故障类型和故障相，以进行选择性相跳闸。研究了该方法对不同噪声水平的抗扰性。仿真研究表明，通过仅将两个特征用于对称事件，将六个特征用于非对称事件，就可以准确地检测出任何故障。

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