scholarly journals Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 174
Author(s):  
Sihan Chen ◽  
Ziche Li ◽  
Guobing Pan ◽  
Fang Xu
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Power Quality ◽  
Electrical Equipment ◽  
Support Vector ◽  
Analysis Tool ◽  
Electrical Systems ◽  
Empirical Wavelet Transform ◽  
Power Quality Disturbance ◽  
System Calculation

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

scholarly journals Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine

2019 ◽  
Vol 9 (11) ◽  
pp. 2315 ◽  
Author(s):  
Jidong Wang ◽  
Zhilin Xu ◽  
Yanbo Che
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Fine Tuning ◽  
Current Layer ◽  
Data Imbalance ◽  
Empirical Wavelet Transform ◽  
Power Quality Disturbance ◽  
Learning Machine ◽  
The Impact

In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data.

Transient Power Quality Disturbances Identification and Classification Using Wavelet and Support Vector Machines

2012 ◽  
Vol 433-440 ◽  
pp. 1071-1077
Author(s):  
Wen Sheng Sun ◽  
Xiang Ning Xiao ◽  
Shun Tao ◽  
Jian Wang
Keyword(s):  
Wavelet Transform ◽  
Support Vector Machines ◽  
Power Quality ◽  
Support Vector ◽  
Time Frequency ◽  
Vector Machines ◽  
Power Quality Disturbance ◽  
Time Frequency Localization ◽  
Power Quality Disturbances ◽  
Transient Power Quality

Based on wavelet transform and support vector machines, a method of recognition and classification of transient power quality disturbance is presented. Using wavelet transform time-frequency localization characteristics, according to the principle of modulus maxima, realize the automatic detection positioning. After multi-resolution signal decomposition of PQ disturbances, multi-scale information in frequency domain and time domain of the signal can be extracted as the characteristic vectors. After choose and optimization of the eigenvectors based on the method of F-score, support vector machines are used to classify these eigenvectors of power quality disturbances. Effectiveness of the proposed method is verified through Matlab simulation.

scholarly journals Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenjing Zhao ◽  
Liqun Shang ◽  
Jinfan Sun
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Frequency Domain ◽  
Power Quality ◽  
Classification Accuracy ◽  
Support Vector ◽  
Classification Rules ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbance

AbstractAccurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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