Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Abstract

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

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Real-Time Detection and Classification of Power Quality Disturbances

“…Many existing PQD detection and classification methods rely on frequency-domain or time-frequency domain analyses of the signals to extract informative features for further analysis to identify the type of disturbances, e.g., wavelet transform [ 2 ], Fourier transform, short-time Fourier transform (STFT), S-transform [ 3 ], etc. These methods are usually assisted with machine learning (ML)-based classification methods, including decision tree (DT) [ 3 , 4 ], support vector machine (SVM) [ 5 , 6 ], k -nearest-neighbor-based methods ( k NN) [ 7 , 8 , 9 ], and neural networks [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”