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
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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: Empirical results show that the proposed structures can yield an automatic online/offline monitoring of PQ with sparser structures and less computational execution time, both in the training and recognition phases, without sacrificing generality of performance.
42 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...ing [145], [146] to extract the best subset of features using extreme learning machine (ELM) [147] and obtaining transient events using ICA [148]....
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01 Jan 2016
TL;DR: A way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform (DWT) using Daubechies family filter banks, allowing for the segmentation of signals and, as a consequence, the analysis of disturbances related to Power Quality (PQ).
Abstract: Graphical abstractDisplay Omitted HighlightsAn adaptive threshold is determined for segmentation of power quality signals.The power quality signals are based on mathematical models and acquired in field.Wavelet transforms are used to decompose the signals.The intersections between the adaptive threshold and the wavelet transform detail curves determine the start and the end of the segments.The adaptive threshold was accurate in more than 96 percent of the signals. Detecting discontinuities in electrical signals from recorded oscillograms makes it possible to segment them. This is the first step in implementing automated methods which will ensure disturbances in electrical power systems are detected, classified and stored. In this context, this paper presents a way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform (DWT) using Daubechies family filter banks, allowing for the segmentation of signals and, as a consequence, the analysis of disturbances related to Power Quality (PQ). Considering this, the proposed approach was initially evaluated for signals originating from mathematical models representing short-term voltage fluctuations, transients (impulsive and oscillatory) and harmonic distortions. In the synthetic signal database, either single or combined occurrences of more than one disturbance were considered. By applying the DWT, the amount of energy and entropy of energy were then calculated for the leaves of the second level of decomposition. Based on these calculations, a unique adaptive threshold could be determined for each analyzed signal. Afterwards, the amount of existing intersections between the threshold and the curve of details obtained for the second level of decomposition was then defined. Thus, the intersections determine the beginning and end of the segments. In order to validate the approach, the performance of the proposed methodology was analyzed considering the signals obtained from oscillograms provided by IEEE 1159.3 Task Force, as well as real oscillograms obtained from a regional distribution utility. After these analyses, it was observed that the proposed approach is efficient and applicable to automatic segmentation of events related to PQ.
42 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...using WT [71], maximal overlap discrete wavelet transform [72], DB4 wavelet [73], dual-tree complex wavelet-based algorithm [74] and harmonic evolution [75]....
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TL;DR: The experimental results indicate that the proposed method has not only achieved a significant improvement for detecting single PZ disturbances but is also effective for detecting and classifying multiple PQ disturbances.
Abstract: This paper introduces a method to detect multiple power quality (PQ) disturbances of power system features based on an independent component analysis (ICA) and a sparse auto encoder (SAE). Seven basic single PQ disturbances are extracted as typical features from numerous training samples of PQ disturbances by the SAE method, which can automatically gain the training features rather than manually selecting features as in the conventional approaches. An ICA is adapted to conduct basic separate signals from the blind original disturbance sources. The experimental results indicate that the proposed method has not only achieved a significant improvement for detecting single PQ disturbances but is also effective for detecting and classifying multiple PQ disturbances.
42 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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27 Mar 2015
TL;DR: In this paper, the authors presented a technique for the detection and localization of the power quality events associated with outage and grid synchronization of solar photovoltaic plant in distribution network.
Abstract: This paper presents a technique for the detection and localization of the power quality events associated with outage and grid synchronization of solar photovoltaic plant in distribution network. A standard IEEE 13 bus distribution network has been modified by adding 100kW solar photovoltaic plant. The proposed test system is simulated in MATLAB/simulink environment. Voltage signals captured at nodes of the test system are used for power quality analysis. The harmonic detection has been carried out using fast Fourier transform and the voltage sag, swell has been detected by voltage index calculated based on wavelet analysis. The unbalance in the voltage has been detected by sequence components of voltages. The power quality events associated with sudden change in the solar insolation have also been investigated. Detection and discrimination of synchronization, outage and sudden changes in solar insolation has been proposed using sequence components of voltages measured.
40 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], 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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...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]....
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...[60] S. A. Deokar and L. M. Waghmare, ‘‘Integrated DWT–FFT approach for detection and classification of power quality disturbances,’’ Int....
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...[203] H. Y. Li, Y. Fu, and D. Zhao, ‘‘Identification of power quality disturbances based on FFT and attribute weighted artificial immune evolutionary classifier,’’ Appl....
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...order 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...
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TL;DR: In this article, a fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system.
Abstract: SUMMARY
In this paper, a new fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system. The proposed algorithm is based on the generalized Fourier algorithm that is used to obtain the time-localized spectral characteristics of the time-varying voltage and current signals belonging to PQ events. The fast ST algorithm is realized with different types of frequency scaling, band pass filtering, and interpolation techniques based on Heisenberg's uncertainty principle resulting in a reduced computation cost. In the conventional ST, the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Therefore, the time-varying PQ disturbance signal is down sampled at low frequencies and cropped at high frequencies resulting in the evaluation of a fewer samples. From the time–frequency matrix output, important features are extracted and used with a binary decision tree for an accurate classification of single and simultaneous PQ events. Further, a unified approach is presented to track the time-varying PQ disturbance waveforms like voltage sag, swell, harmonics, and oscillatory transients and produce estimation of their amplitudes and phase angles. Copyright © 2013 John Wiley & Sons, Ltd.
40 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...random forest tree [82], ST and DT [83], Multi-resolution S transform (MST) [84], discrete ST [85], ST-extreme learning machine (ELM) [86], rule-based ST [87] and experimental validation of non-stationary signal parameters under the spectrum leakage using nonergodic S-transform (NEST) [88]....
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