Recognition Of Power Quality Events Research Articles

Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine

In this paper, a novel approach based on Empirical Mode Decomposition (EMD) and an Extreme Learning Machine (ELM) for the detection and classification of Power Quality Events (PQEs) is proposed. The EMD technique is used for computing the prominent features required to characterize the PQE signals. A down-sampled Kriging Interpolation (KI) based EMD is suggested to enhance the performance of the EMD operation in terms of accuracy and speed. The ELM is applied for the classification of Power Quality Disturbances (PQDs), considering all the derived features through the KI-EMD approach. Symbiotic Organism Search (SOS) optimization technique is applied to enhance the performance and robustness of ELM by optimally computing the values of the system parameters. The performance of the proposed approach is justified with test cases under diverse noise conditions. Comparative results and analysis are provided to show an improvement of 2-5% in terms of accuracy, speed, and robustness compared to other conventional methods. Experimental results validate the efficacy of the proposed approach under real-time conditions.

Multiple power quality events recognition and classification based on adaptive Eigen-based type-2 fuzzy logic algorithm

Multiple power quality events recognition and classification based on adaptive Eigen-based type-2 fuzzy logic algorithm

FPGA-Based Semisupervised Multifusion RDCNN of Process Robust VMD Data With Online Kernel RVFLN for Power Quality Events Recognition

The improved particle swarm optimization algorithm is integrated with variational mode decomposition (VMD) to extract the efficient band-limited intrinsic mode function (BLIMF) of the single and combined power quality events (PQEs). The selected BLIMF of the robust VMD (RVMD) and the privileged Fourier magnitude spectrum (FMS) information are fed to the proposed reduced deep convolutional neural network (RDCNN) for the extraction of the most discriminative unsupervised features. The RVMD-FMS-RDCNN method shows minimum feature overlapping compared with RDCNN and RVMD-RDCNN methods. The feature vector is imported to the novel supervised online kernel random vector functional link network (OKRVFLN) for quick and accurate categorization of complex PQEs. The proposed RVMD-FMS-RDCNN-OKRVFLN method produces excellent recognition capability over RDCNN, RVMD-RDCNN, and RVMD-RDCNN-OKRVFLN methods in noise-free and noisy environments. The unique BLIMF selection, clear detection, descriptive feature extraction, higher learning speed, superior classification accuracy, and robust antinoise performances are considerable importance of the proposed RVMD-FMS-RDCNN-OKRVFLN method. Finally, the proposed method architecture is developed and implemented in a very-high-speed ML506 Virtex-5 FPGA to text, examine, and validate the feasibility, performances, and practicability for online monitoring of the PQEs.

FPGA-Based Deep Convolutional Neural Network of Process Adaptive VMD Data With Online Sequential RVFLN for Power Quality Events Recognition

In this article, self-adaptive variational mode decomposition (SAVMD), deep convolutional neural networks (DCNN), and online-sequential random vector functional link networks (OSRVFLN) are integrated to categorize the single as well as combined power quality events (PQEs) in real time. The SAVMD method is proposed to optimize both the number of decomposition and data-fidelity factor to extract the most efficient band-limited mode (BLM) based on entropy and Kurtosis index. The most discriminative unsupervised features are extracted automatically using a DCNN from the BLM of SAVMD. The extracted distinct feature vector is fed to the proposed supervised OSRVFLN classifier to train accurately by minimizing the training cross-entropy loss with an increment in the number of hidden nodes for obtaining the maximum classification accuracy of the complex PQE patterns in noisy and noise-free environments. The automatic efficacious feature extraction, superior classification accuracy, noise immunity, and short event detection time are the major advantages of the proposed SAVMD-DCNN-OSRVFLN method. Finally, the novel methodology is implemented in a fast digital Xilinx Virtex-5 field-programmable gate array embedded processor to validate the practicability and feasibility of the proposed method in real-time.

A real-time power quality events recognition using variational mode decomposition and online-sequential extreme learning machine

In this paper, variational mode decomposition (VMD) and online-sequential extreme learning machine (OSELM) are integrated to detect and classify power quality events (PQEs) in real-time. Empirical Wavelet transform (EWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to decompose the non-stationary power quality (PQ) signals into intrinsic mode functions (IMFs) or band-limited mode of oscillations. Four felicitous features are extracted by applying the Hilbert transform (HT) on the decomposed PQE signals. The synthetic, as well as practical PQE signals, are considered to test and examine the overall performance of the proposed method. OSELM is an efficient and advanced classifier which is implemented to recognize the single as well as multiple PQEs. The robust anti-noise performance, faster learning speed, lesser computational complexity, superior classification accuracy and short event detection time prove that the proposed VMD-OSELM method can be implemented in the electrical power system. Finally, a PC interface based hardware prototype is developed to verify the cogency of the proposed method in real-time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.

Power Quality Events Recognition Using S-Transform and Wild Goat Optimization-Based Extreme Learning Machine

This paper presents a novel approach for automatic power quality (PQ) event detection and classification based on Stockwell transform (S-transform) and wild goat optimization (WGO)-tuned extreme learning machine (ELM). The distinctive features associated with PQ event signals have been extracted by S-transform to obtain the feature vectors characterizing the signal nature. Considering these feature vectors as input, a classifier based on ELM optimally tuned with modified WGO technique is proposed. The WGO technique originated from the social hierarchy and strategic planning to reach at peak by the wild goats in nature is adapted to formulate an effective ELM model by parameter tuning for better classification. To justify the enhanced performance of the proposed approach, it is tested on a wide range of extracted synthetic PQ event data by MATLAB simulation. To ensure the real-time implementation, the PQ event data with the addition of 20, 30, and 50 dB to the synthetic signals are considered. The analysis of results presented reveals a very high performance for both PQ event recognition and classification, ensuring the efficiency of the proposed approach.

Automatic Power Quality Events Recognition Using Modes Decomposition Based Online P-Norm Adaptive Extreme Learning Machine

This article presents an automatic recognition of power quality events (PQEs) by integrating variational mode decomposition (VMD) with Hilbert transform (HT) and the proposed online P-norm adaptive extreme learning machine (OPAELM). The robust parameters estimation capability from the highly nonstationary PQE patterns is presented using VMDHT method and a novel mode selection scheme is introduced based on the correlation coefficient. Three most efficient power quality indices are extracted and fed as an input to train and test the OPAELM classifier with a few existing advanced classifiers. The distinctive modes extraction, low computational burden, robust antinoise performance, short event recognition time, and outstanding recognition capability are the prime superiority expediencies of the VMDHT-OPAELM method. Finally, the proposed method is developed in Xilinx integrated synthesis environment (ISE) Design Suite 14.5 configured with MATLAB/Simulink software environment and implemented in a high-speed field-programmable gate array digital circuitry hardware platform to validate the cogency in real time.

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real time. Empirical mode decomposition is used to decompose the nonstationary PQEs into the monocomponent mode of oscillation, known as intrinsic mode functions (IMFs). The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs. An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine, least-square support vector machine, extreme learning machine, and bidirectional extreme learning machine. The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform associated with HT and WBELM method, and tunable-Q wavelet transform along with HT and WBELM method. The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system. Finally, a hardware prototype is developed based on the digital signal processor to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.

A hybrid method based on time frequency analysis and artificial intelligence for classification of power quality events

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This paper presents a hybrid intelligent scheme for the classification of power quality disturbances. The proposed algorithm is realized through three main steps: feature extraction, feature selection and feature classification. The feature vectors are extracted using S-transform ST and Wavelet transform WT which are very powerful time-frequency analysis tools. In order to avoid large dimension of feature vector, three different approaches are applied for feature selection step, namely Sequential Forward Selection SFS, Sequential Backward Selection SBS and Genetic Algorithm GA. In the next step, the most meaningful features are applied to Probabilistic Neural Network PNN as classifier core. Various transient events, such as voltage sag, swell, interruption, harmonics, transient, sag with harmonics, swell with harmonics, and flicker, are tested. Sensitivity of the proposed algorithm under different noisy conditions is investigated in this article. Results show that the classifier can detect and classify different power quality signals, even under noisy conditions, with high accuracy.

Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy.

Recognition of Power Quality Events Using Artificial Neural Networks

Recognition of Power Quality Events Using Artificial Neural Networks

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

Recognition of power quality events by analyzing the voltage waveform disturbances is a very important task for the power system monitoring. This paper presents an integrated model for recognizing power quality disturbances (PQDs) using a new wavelet multi-class relevance vector machines (WMRVMs). The so-called relevance vector machines (RVMs) is an effective classification tool. The proposed method employs the wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained feature vectors are used for training the RVMs to classify the PQDs. Various transient events are tested, the result shows that the classifier can detect and classify different power quality signal correctly. Finally, sensitivity of the proposed algorithm under noise condition is investigated in this paper. Copyright © 2010 John Wiley & Sons, Ltd.

Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This article presents a novel approach for the recognition and classification of power quality disturbances using wavelet transform and wavelet-support vector machines. The proposed method employs wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained severable feature vectors are used for training the support vector machines to classify the power quality disturbances. Various transient events, such as voltage sag, swell, interruption, harmonic, transient, sag with harmonic, swell with harmonic, and flicker, are tested. Sensitivity of the proposed algorithm under different noise conditions is investigated in this article. The results show that the classifier can detect and classify different power quality signals, even under noisy conditions, correctly.

Power quality events recognition using a SVM-based method

In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost.

Recognition of power quality events by using multiwavelet-based neural networks

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.

Wavelet-based neural network for power quality disturbance recognition and classification

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, voltage swell, interruption, notching, impulsive transient, and harmonic distortion. The results show that the classifier can efficiently detect and classify different types of power quality disturbance.

Hybrid S-Transform and Kalman Filtering Approach for Detection and Measurement of Short Duration Disturbances in Power Networks

This paper presents a new approach in the detection, localization, and classification of short duration disturbances in the power networks using a phase-corrected wavelet transform known as S-transform (ST) and an extended Kalman filter (EKF). The ST has excellent time-frequency resolution characteristics and provides detection, localization, and visual patterns suitable for automatic recognition of power quality events. The EKF, on the other hand, provides automatic classification and measurements of the frequently occurring power frequency short duration disturbances on the power networks. Thus, by combining both the ST and EKF, it is possible to completely classify and measure the short duration power quality disturbances. The proposed technique is applied to both simulated and experimentally obtained short duration power network disturbances in the presence of additive noise, and the results reveal significant accuracy in completely characterizing the power quality events.