Abstract
The excessive use of power semiconductor devices in a grid utility increases the malfunction of the control system, produces power quality disturbances (PQDs) and reduces the electrical component life. The present work proposes a novel algorithm based on Improved Principal Component Analysis (IPCA) and 1-Dimensional Convolution Neural Network (1-D-CNN) for detection and classification of PQDs. Firstly, IPCA is used to extract the statistical features of PQDs such as Root Mean Square, Skewness, Range, Kurtosis, Crest Factor, Form Factor. IPCA is decomposed into four levels. The principal component (PC) is obtained by IPCA, and it contains a maximum amount of original data as compare to PCA. 1-D-CNN is also used to extract features such as mean, energy, standard deviation, Shannon entropy, and log-energy entropy. The statistical analysis is employed for optimal feature selection. Secondly, these improved features of the PQDs are fed to the 1-D-CNN-based classifier to gain maximum classification accuracy. The proposed IPCA-1-D-CNN is utilized for classification of 12 types of synthetic and simulated single and multiple PQDs. The simulated PQDs are generated from a modified IEEE bus system with wind energy penetration in the balanced distribution system. Finally, the proposed IPCA-1-D-CNN algorithm has been tested with noise (50 dB to 20 dB) and noiseless environment. The obtained results are compared with SVM and other existing techniques. The comparative results show that the proposed method gives significantly higher classification accuracy.
Highlights
Power quality (PQ) is becoming the primary concern as serious issues affecting sustainable energy, energy security, and the environmen tend to arise
The results of the advanced Improved Principal Component Analysis (IPCA)-1-D Convolutional Neural Network (CNN) technique for classification of PQ disturbances are discussed
Dataset 1 is based on synthetic PQ disturbances produced in MATLAB, while dataset 2 contains simulated PQ disturbances generated from the modified IEEE 13 bus distribution system
Summary
Power quality (PQ) is becoming the primary concern as serious issues affecting sustainable energy, energy security, and the environmen tend to arise. Distributed generation (DG) based on renewable energy sources and conventional grid is a concern as it uses modern power electronics devices for control, heavy non-linear loads, microprocessor and computer solutions [1,2,3]. Non-stationary PQ disturbances occur due to fluctuations and loads, which change the capability of the signals. The sudden change in frequency, magnitude, current and phase angle can cause PQ disturbances. Automatic classification and detection of PQ disturbances with appropriate methods have solved this issue [5,6,7,8]
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