Aiming to the problem of large computation amount and high redundancy of traditional S‐transform methods available in the detection and classification of transient power quality disturbances, an efficient and accurate method for the detection and classification is proposed. First, the maximum power spectrum dynamics method is used to extract the main frequency points. Then, a called modified incomplete S‐transform proposed in the paper is performed on the main frequency points so as to obtain a modulus time‐frequency matrix involving the characteristic information of disturbances. The modified incomplete S‐transform is implemented by introducing a bi‐Gaussian window function with two parameters to replace the Gaussian window function of the traditional S‐transform. And then, the characteristic quantities for detecting power quality disturbance and identifying the classification of the disturbances are extracted based on the matrix. Finally, the disturbance parameters such as the amplitude, start time, and stop time of the disturbances are calculated based on the relevant theories and rules, and also the classification of the disturbances is identified by extracting some important characteristic quantities. A great number of simulation experiments are conducted. The results obtained show that by the method, the start time, stop time, and amplitude changes of power quality disturbance signals can be fast and accurately detected, and the classification of the disturbances can be fast and accurately identified, and that the method has the characteristics of less operation time and strong anti‐interference ability. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

---

一种高效，准确的电能质量扰动分类检测方法

针对暂态电能质量扰动检测和分类中传统S变换方法计算量大，冗余度高的问题，提出了一种高效，准确的检测和分类方法。首先，使用最大功率谱动力学方法提取主频点。然后，在主频点上进行了本文提出的被称为修正的不完全S变换，以获得包含扰动特征信息的模量时频矩阵。通过引入带有两个参数的双高斯窗函数来代替传统S变换的高斯窗函数，可以实现修改后的不完整S变换。然后，基于矩阵提取用于检测电能质量扰动和识别扰动分类的特征量。最后，根据相关的理论和规则，计算出扰动的幅度，起止时间等扰动参数，并通过提取一些重要的特征量来确定扰动的分类。进行了大量的模拟实验。结果表明，该方法可以快速，准确地检测出电能质量扰动信号的开始时间，停止时间和幅度变化，可以快速，准确地识别出扰动的类别，并且该方法具有如下特点：具有操作时间短，抗干扰能力强的特点。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。