A Novel Three-Step Classification Approach Based on Time-Dependent Spectral Features for Complex Power Quality Disturbances
- 21 January 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 70 (00189456), 1-14
- https://doi.org/10.1109/tim.2021.3050187
Abstract
Power quality events caused by renewable-energy integration are usually associated with complex disturbances; therefore, their type identification is the primary task of subsequent pollution control. This study proposes a novel three-step classification approach based on time-dependent spectral features (TDSFs) for the classification of complex power quality disturbances (PQDs). First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is adopted to decompose the PQDs into several intrinsic mode functions (IMFs). The related IMFs are selected by correlation coefficient and kurtosis. Then the eight eigenvalues of each IMF are extracted, including TDSFs. Finally, the eigenvalue dimension of each IMF is reduced by linear discriminant analysis (LDA). Moreover, the classifier of the adaptive $k$ -nearest neighbor with excluding outliers (AdaKNNEO) can confirm the PQDs type. To verify the effectiveness of the proposed approach, a series of simulations and hardware experiments are conducted. The overall result shows robustness and high accuracy of the proposed method, and especially for the complex PQDs, it possesses the highest entirely correct of 96% compared to other advanced methods.Keywords
Funding Information
- Chinese National Natural Science Foundation (51977039)
- Research Fund for International Young Scientists of the National Natural Science Foundation of China (51950410593)
This publication has 24 references indexed in Scilit:
- Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation SystemIEEE Transactions on Smart Grid, 2018
- A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern RecognitionIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017
- A fusion of time-domain descriptors for improved myoelectric hand controlPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural networkMeasurement, 2017
- Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMsIEEE Transactions on Instrumentation and Measurement, 2016
- Feature Extraction and Power Quality Disturbances Classification Using Smart Meters SignalsIEEE Transactions on Industrial Informatics, 2015
- A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVMIEEE Transactions on Smart Grid, 2015
- Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial AmputeesIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015
- Improved complete ensemble EMD: A suitable tool for biomedical signal processingBiomedical Signal Processing and Control, 2014
- Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision TreeIEEE Transactions on Industry Applications, 2014