Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
TL;DR: A critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration is presented, to provide various concepts utilized for extraction of the features to detect and classify the P Q disturbances even in the noisy environment.
Abstract: The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.
Citations
More filters
TL;DR: In this article , the authors provide an up-to-date review of the most recent global trend of various renewable energy integrations into the power sector and discuss the role of RE integration in sustainable development.
55 citations
TL;DR: In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, which contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid.
Abstract: Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources’ ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.
52 citations
TL;DR: In this paper, the authors present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons, with the viewpoint of the types of power input signal (synthetic/real/noisy), preprocessing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline).
Abstract: Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.
37 citations
TL;DR: The proposed hybrid convolutional neural network method is a novel approach that covers the steps of an expert examining a signal and its classification performance is relatively high compared to other methods, the computational complexity is almost the same.
Abstract: As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.
34 citations
27 Jul 2014
TL;DR: In this paper, the authors provided an improved power quality (PQ) disturbances classification, which were associated with load changes and environmental factors, by employing support vector machines (SVM) and decision tree classifiers.
Abstract: Penetration of distributed generation (DG) systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which are associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch and harmonics, are taken into account. Several features are obtained through HS-transform, out of which optimal features are selected using a genetic algorithm (GA). These optimal features are used for PQ disturbances classification by employing support vector machines (SVM) and decision tree (DT) classifiers. The study is supported on three different case studies, considering experimental set-up prototypes for wind energy and photovoltaic (PV) systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM is performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.
31 citations
References
More filters
01 Oct 2017
TL;DR: A method based on Stockwell's transform and Fuzzy C-means clustering initialized by decision tree has been proposed for detection and classification of power quality (PQ) disturbances and is established effectively by results of high accuracy.
Abstract: Display Omitted The S-transform based decision tree initialized Fuzzy C-means clustering technique is proposed for recognition of PQ disturbances.Sum absolute values curve is introduced to increase efficiency of algorithm.Results of FCM technique are more efficient compared with rule based decision tree.Validation of results is carried out with 100 data sets of each PQ disturbance with and without noise and comparing with real time results.Classification accuracy more than 99% is achieved even in the noisy environment. A method based on Stockwell's transform and Fuzzy C-means (FCM) clustering initialized by decision tree has been proposed in this paper for detection and classification of power quality (PQ) disturbances. Performance of this method is compared with S-transform based ruled decision tree. PQ disturbances are simulated in conformity with standard IEEE-1159 using MATLAB software. Different statistical features of PQ disturbance signals are obtained using Stockwell's transform based multi-resolution analysis of signals. These features are given as input to the proposed techniques such as rule-based decision tree and FCM clustering initialized by ruled decision tree for classification of various PQ disturbances. The PQ disturbances investigated in this study include voltage swell, voltage sag, interruption, notch, harmonics, spike, flicker, impulsive transient and oscillatory transient. It has been observed that the efficiency of classification based on ruled decision tree deteriorates in the presence of noise. However, the classification based on Fuzzy C-means clustering initialized by decision tree gives results with high accuracy even in the noisy environment. Validity of simulation results has been verified through comparisons with results in real time obtained using the Real Time Digital Simulator (RTDS) in hardware synchronization mode. The proposed algorithm is established effectively by results of high accuracy to detect and classify various electrical power quality disturbances.
88 citations
TL;DR: In this article, an integrated rule based approach of discrete wavelet transform (DFT) is proposed for the detection of power quality disturbance present in the input signal, the input waveform is processed by DFT and DFT coefficients are used to calculate average energy entropy of squared detailed coefficients feature.
84 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...The other WT based detection techniques include, interpolated DFT [58], actual data based noise-suppression method usingWT and un-decimated WT [59], integrated rule-based approach of DWT-FFT [60], DTCWT and sparse presentation classifier (SRC) [61], combine wavelet packet and t-sallis entropy [62], empirical-WT based time-frequency technique [63], rank wavelet support vector machine (rank-WSVM) [64], wavelet packet decomposition (WPD) [65], combination of WT and SVM [66], WPE and MIST [67], hybridization of daubechies wavelets db2 and db8 [68], multi-flicker source power network using WT [69], variants of WT, namely the maximum overlapping DWT and the second-generationWT [70], threshold selection using WT [71], maximal overlap discrete wavelet transform [72], DB4 wavelet [73], dual-tree complex wavelet-based algorithm [74] and harmonic evolution [75]....
[...]
...tion techniques include, interpolated DFT [58], actual data based noise-suppression method usingWT and un-decimated WT [59], integrated rule-based approach of DWT-FFT [60], DTCWT and sparse presentation classifier (SRC) [61], combine wavelet packet and t-sallis entropy [62], empirical-WT based time-frequency technique [63], rank wavelet support...
[...]
TL;DR: In this paper, the authors presented a technique to recognize the power quality disturbances associated with solar energy penetration in distribution network using a standard IEEE-13 bus test system modified by incorporating the solar PV system.
83 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...Classification of the PQDs with wind energy penetration in the utility grid using fuzzy c-means clustering has been presented in [22]....
[...]
TL;DR: In this article, the authors presented fractional Fourier transform (FRFT) based feature extraction as a new technique for classification of power quality disturbances (PQDs) for mitigation of PQDs.
Abstract: Proper mitigation of power quality disturbances (PQDs) requires a fast, accurate and highly noise immune classification technique. This study, therefore, presents fractional Fourier transform (FRFT) based feature extraction as a new technique for classification of PQDs. FRFT is a generalised version of Fourier transform (FT) with an additional order control and can give time, frequency and intermediate time-frequency representations for a signal. The order control offers multi-domain feature extraction, such that most robust feature matrix is utilised for classification under any condition. An expression is derived for the optimal classification order corresponding to maximum overall accuracy. Based on IEEE-1159 standards, 15 PQDs are simulated and a database of pure and noisy signals is prepared. Features extracted from FRFT processed signals are tested with decision trees (DTs) and bagging predictors (BPs). The proposed technique shows better performance in most of the cases, when compared with Stockwell transform based classification under similar conditions. The classification accuracies of FRFT-DT and FRFT-BP are impressive even with significant reduction in training and features. Further, a validation using real PQDs obtained from an experimental setup is shown. The corresponding results closely resemble the simulation outcomes.
83 citations
"Comprehensive Review on Detection a..." refers background in this paper
...Also, fractional Fourier transform as a generalized version of FT has been presented in [48]....
[...]
TL;DR: This paper proposes a novel detection framework for complex PQ disturbances based on multifusion convolutional neural network (MFCNN), and focuses on automatic extraction and fusion of features from multiple sources.
Abstract: Intelligent identification of multiple power quality (PQ) disturbances is very useful for pollution control of power systems. In this paper, we propose a novel detection framework for complex PQ disturbances based on multifusion convolutional neural network (MFCNN). Our contributions focus on automatic extraction and fusion of features from multiple sources. First, an information fusion structure is introduced in which the time domain and frequency domain information of the PQ disturbance signal are used as inputs. Additionally, the one-dimensional composite convolution is proposed to improve the diversity of network features based on the standard convolution and dilated convolution. Then, to speed up the training and prevent overfitting, batch normalization is used to adjust the distribution of features. Second, we use several visualization methods to resolve the internal mode of MFCNN, and demonstrate the working mechanism of the proposed method. Finally, we conduct various experiments to verify the effectiveness of the MFCNN. Compared with the handcrafted feature design methods and the general convolutional neural network models, the simulation under different noises and hardware platform-based experiments verify the effectiveness of noise immunity, higher training speed, and better accuracy of the method.
81 citations
"Comprehensive Review on Detection a..." refers background in this paper
...Multi-fusion convolutional neural network for complex PQDs in the noisy environment has been presented in [214]....
[...]