基于分段改进S变换的复合电能质量扰动识别

针对存在多种单一电能质量扰动的复合扰动分类识别问题,提出了一种基于分段改进S变换和RBF神经网络相结合的复合电能质量扰动识别新方法。首先对离散S变换进行了分段改进,将时域分辨率和频域分辨率进行分段处理,通过分析改进S变换得到的模时频矩阵,绘制了能够反映扰动信号不同突变参数的特性曲线。其次利用统计方法优化计算提取了10种用于模式识别的特征量,并用局部逼近的RBF神经网络设计了分类器对提取的特征样本进行训练和分类,最后在不同噪声环境下对5种单一扰动及谐波+电压暂降、电压暂降+闪变等6类复合电能质量扰动的分类识别进行了仿真验证。仿真结果表明,该方案时频处理、分类能力和学习速度等方面均优于普通改进S变换+全局逼近网络的方法,且鲁棒性强,能准确识别多种单一扰动及两种扰动同时存在的复合电能质量扰动。

Classification of composite power quality disturbances based on piecewise-modified S transform

Aiming at the classification and recognition problem of composite power quality disturbances, a composite power quality disturbance recognition algorithm based on piecewise-modified S transform and RBF neural network is proposed. Firstly, the S transform is modified by segmenting the time resolution and the frequency resolution. By analyzing the obtained mode time-frequency matrix, the characteristic curve that can reflect different mutation parameters of the disturbance signal is drawn. Secondly, 10 types of characteristic parameters for pattern recognition are extracted by using statistical methods and optimization. The RBF neural network classifier is designed to classify the extracted feature samples by training and classification. Finally, six types of composite power quality disturbance classification including five single disturbances and harmonic and voltage sag, voltage sag and flicker, etc. are simulated under different noise environment. The simulation results show that the proposed scheme is superior to S transform and global approximation networks in terms of time-frequency processing ability, classification ability and learning speed, and is robust and can accurately identify multiple kinds of single disturbances and two kinds of disturbances simultaneously composite power quality disturbance. This work is supported by National Natural Science Foundation of China (No. 61863023).