Automatic Power Quality Disturbance Research Articles

Automatic Power Quality Disturbance Diagnosis Based on Residual Denoising Convolutional Auto-Encoder

With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection

Automatic power quality disturbance (PQD) classification is a challenging task for industry and utility. This work presents an algorithm for classifying the PQDs based on the combination of novel feature selection with machine learning (ML) techniques. In this work, discrete wavelet transform (DWT) is utilized to decompose the PQD signals and hence minimize the size of input vectors with multi-resolution analysis. Novel optimal feature selection-based classification is performed using probabilistic neural network and adaptive Arrhenius artificial bee colony (PNN- adaptive AABC) algorithm. The selection of optimal features may discard the redundant features and retain the useful features. Also, this adaptive AABC algorithm provides a higher convergence rate, and this is used for accurate feature selection since this work is applied for 16 PQD events. In other case, features extracted with DWT are processed and trained with various ML algorithms such as Decision Tree, support vector machine (SVM), and K-nearest neighbor (KNN). Finally, the comparison accuracy of the proposed classifier is compared with the existing classifiers. The proposed technique found to give improved results in most of the cases like accuracy, prediction speed, and training time, and the outcomes acquired as 99.98%, ~1260 obs/seconds, and 13.113 seconds, respectively.

Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation

Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform. It is the purpose of this paper to bring to bear advances, especially in wavelet transforms, artificial neural networks, and the mathematical theory of evidence, to the problem of automatic power quality disturbance waveform recognition. Unlike past attempts to automatically identify disturbance waveforms where the identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using a set of multiple neural networks. The outcomes of the networks are then integrated using decision making schemes such as a simple voting scheme or the Dempster-Shafer theory of evidence. With such a configuration, the classifier is capable of providing a degree of belief for the identified disturbance waveform.