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.
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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
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TL;DR: In this paper, the performance of a wavelet-based, on-line (real-time) voltage detection scheme for power quality applications is evaluated, and the authors demonstrate suitability of the proposed method in detecting faults/disturbances in a power system and compare its performance with that of a conventional scheme.
Abstract: This article evaluates the performance of a wavelet-based, on-line (real-time) voltage detection scheme for power quality applications. The objectives are (1) to demonstrate suitability of the proposed method in detecting faults/disturbances in a power system and (2) to compare its performance with that of a conventional scheme. Two static transfer switch (STS) systems are chosen as frameworks for comparison; a low-voltage laboratory STS set-up for which measured results are provided, and a medium-voltage STS system for which detection times are derived based on simulation, using the EMTDC/PSCAD.
65 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...and experimental [43] framework for the selection of small window size with the help of FT to provide automated detection of PQDs....
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TL;DR: The short event detection, lesser computational complexity, superior classification accuracy, and robust antinoise performance are the major advantages of the proposed RSHHT-CSWRVFLN method.
Abstract: This paper proposes a new signal segmentation method, reduced-sample empirical mode decomposition, to extract the highly correlated monocomponent mode of oscillations. The two efficacious power quality indices are extracted from the Hilbert transformed (HT) array of the first three intrinsic mode functions. A novel class-specific weighted random vector functional link network (CSWRVFLN) classifier is proposed to recognize the complex power quality disturbances (PQDs). The performance of reduced sample Hilbert–Huang transform (RSHHT) combined with CSWRVFLN (RSHHT-CSWRVFLN) method is tested and compared with tunable-Q Wavelet transform associated with HT and CSWRVFLN and empirical wavelet transform along with HT and CSWRVFLN methods. The short event detection, lesser computational complexity, superior classification accuracy, and robust antinoise performance are the major advantages of the proposed RSHHT-CSWRVFLN method. Furthermore, a field-programmable gate array embedded processor is used to test and validate the feasibility of the proposed method for online monitoring the PQDs.
61 citations
"Comprehensive Review on Detection a..." refers background or methods in this paper
...technique [133], double resolution ST (DRST) [134], DWT, multi-resolution analysis, and the concept of signal energy [135], phase-locked loop (PLL) and symmetrical components [136], Reduced sample Hilbert–Huang transform (RSHHT) [137]....
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...These includes, hardware and software architecture of expert system [197], rule-based model [198], improved generalized adaptive resonance theory (IGART) [199], recurrence quantification analysis [200], stochastic ordering theory with coded quickest classification [201], variety of supervised NN with online learning capabilities [202], attribute weighted artificial immune evolutionary classifier (AWAIEC) [203], spectral kurtosis to separate hybrid PQ disturbances [204], DT initialized fuzzy C-means clustering system based on ST [205], variational mode decomposition (VMD) [206], real-time calculation of the spectral kurtosis [207], online PQDs detection and classification using DWT, MM and SVD [208], curvelet transform and deep learning [209], rule-based ST and adaboost with decision stump as weak classifier [87], random forests based PQ assessment framework [82], deep learningbased method and stacked auto-encoder, as a deep learning framework [210], ICA with a sparse autoencoder (SAE) for gaining automatically training features [211] and a new classspecific weighted random vector functional link network (CSWRVFLN) [137]....
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...These includes, advancedDSP techniques [121], [122], slant-transform (SLT) [123], improved chirplet transform (ICT) [124], amplitude and frequency demodulation (AFD) technique [125], higherorder statistics (HOS) [126] and HOS with case-based reasoning [127], time–time transform (TTT) [128], principal curves (PC) [129], DWT and IDWT [130], sequence components of voltages are measured in presence of solar PV using FFT [131], sparse signal decomposition on hybrid dictionaries reduced [132], kernel extreme learning machine technique [133], double resolution ST (DRST) [134], DWT, multi-resolution analysis, and the concept of signal energy [135], phase-locked loop (PLL) and symmetrical components [136], Reduced sample Hilbert–Huang transform (RSHHT) [137]....
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...specific weighted random vector functional link network (CSWRVFLN) [137]....
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TL;DR: Experimental results indicate that the proposed approach to classification of power quality disturbances is robust against noise and reaches a desirable classification accuracy rate.
Abstract: In this paper, a deep learning-based method is introduced into the classification of power quality disturbances (PQDs). Stacked autoencoder, as a deep learning framework, is employed to extract high-level features of PQDs for classification. In this context, a previously unsolved issue regarding optimal features’ selection for PQDs can be addressed. Besides, variances of signals and a particle swarm optimization algorithm are applied to assist classification of the PQDs. To sufficiently validate the effectiveness of the proposed classification scheme, a 10-fold cross-validation has been done. Experimental results indicate that the proposed approach is robust against noise and reaches a desirable classification accuracy rate.
61 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...transform and deep learning [209], rule-based ST and adaboost with decision stump as weak classifier [87], random forests based PQ assessment framework [82], deep learningbased method and stacked auto-encoder, as a deep learning framework [210], ICA with a sparse autoencoder (SAE) for gaining automatically training features [211] and a new class-...
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TL;DR: A closed-loop detection platform based on real-time digital simulator (RTDS) for the converter controller of a permanent magnet synchronous generator (PWSG) set is introduced, to investigate the LVRT performance of the WT system under grid voltage sag conditions.
Abstract: The large-scale application of wind power and photovoltaic power solves the energy crisis and alleviates the environmental problems caused by the use of conventional energy. However, they are at risk of being randomly tripped from the network when faced to voltage sag and severe fault events, which will lead to a sudden reduction of active power output and also complicates fault recovery process of the whole system. Moreover, it may also aggravate failures and lead to large-scale power outages, which stimulates a growing interest in analyzing the low-voltage ride-through (LVRT) capabilities of the renewable energy systems (RES) and improving the performance through developing various mathematical models and analysis tools. In this paper, a systematical overview of cause, classification of voltage sag phenomena and voltage sag emulating techniques is presented, and four voltage sag generators (VSGs) are discussed and compared, which include generator based-VSG, shunt impedance based-VSG, transformer based-VSG and full converter based-VSG. Furthermore, a closed-loop detection platform based on real-time digital simulator (RTDS) for the converter controller of a permanent magnet synchronous generator (PWSG) set is introduced, to investigate the LVRT performance of the WT system under grid voltage sag conditions. Finally, the application of VSG in RES are presented and the future research directions are also discussed.
60 citations
TL;DR: In this paper, a new method for real-time detection and classification of voltage events in power systems is proposed, which simultaneously uses the results obtained in the application of the discrete wavelet transform and an extended Kalman filtering to the voltage waveform.
Abstract: This paper presents a new method for online real-time detection and classification of voltage events in power systems. The method proposed simultaneously uses the results obtained in the application of the discrete wavelet transform and an extended Kalman filtering to the voltage waveform. Wavelet analysis is used for detection and estimation of the time-related parameters of an event and the extended Kalman filtering is used for confirmation of the event and for computation of the voltage magnitude during the event. The paper presents different experimental results showing the improved performance of this combined approach.
60 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...These includes, advancedDSP techniques [121], [122], slant-transform (SLT) [123], improved chirplet transform (ICT) [124], amplitude and frequency demodulation (AFD) technique [125], higher-...
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