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电能质量信号在采集的过程中通常会被噪声污染,而暂态扰动检测的主要挑战就是噪声干扰。传统的扰动检测算法要么只适用于稳态扰动的分析,要么运算量太大,要么易受噪声影响致使检测的准确性急剧下降。为提高强噪声环境下暂态扰动检测的准确性,文中提出了一种扰动检测的新方法。该方法非常简单，无需前置滤波单元，仅有两个参数且对检测结果的影响不灵敏。仿真中，将文中算法与广义形态滤波与差分熵的扰动检测方法和基于奇异值分解的扰动检测方法进行暂态扰动检测对比分析，结果表明文中算法不仅具有较强的抗噪性，且对过零时刻发生的扰动也具有较好的检测效果，同时还能指示扰动突变的极性。最后通过对实际暂态扰动数据的检测，进一步验证了算法的有效性。
The power quality signal is usually contaminated by noise during the acquisition. The main challenge of transient disturbance detection is noise interference. Traditional disturbance detection algorithms are either only suitable for the analysis of steady-state disturbances, or they are too computationally intensive or susceptible to noise, causing a drastic drop in the accuracy of the detection. In order to improve the accuracy of transient disturbance detection in strong noise environment, a new method of disturbance detection is proposed in this paper. This method is very simple, without prefiltering unit, only two parameters and the impact on the test results insensitive. Simulation results show that the proposed algorithm not only has strong anti-noise performance, but also has better performance than the traditional one. In the simulation, the proposed method is compared with the disturbance detection method based on singular value decomposition and the generalized morphological filtering and differential entropy. The disturbance occurring at zero moment also has better detection effect, meanwhile it can also indicate the polarity of disturbance mutation. Finally, the detection of the actual transient disturbance data further verifies the effectiveness of the algorithm.