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: A modified ACO model is proposed which is applied for network routing problem and compared with existing traditional routing algorithms.
Abstract: Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order to mark the route for identification of their routes from the nest to food that should be followed by other members of the colony. This ACO exploits an optimization mechanism for solving discrete optimization problems in various engineering domain. From the early nineties, when the first Ant Colony Optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. This paper review varies recent research and implementation of ACO, and proposed a modified ACO model which is applied for network routing problem and compared with existing traditional routing algorithms.
330 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...This includes, swarm intelligence technique [159], honey bee swarms [160], bacteria foraging technique [161], honey bee mating optimization SVM (HBMOSVM) [162] and multi-objective optimization [163]....
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TL;DR: A comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances is presented in this paper.
Abstract: Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference
326 citations
"Comprehensive Review on Detection a..." refers background in this paper
...[10] presented various detection and classification techniques and the effect of noise on PQ events diagnosis....
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TL;DR: A new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices.
Abstract: The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics-interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology.
266 citations
"Comprehensive Review on Detection a..." refers background or methods in this paper
...Also, DSP and FPGA processors with data acquisition equipment have been found capable of handling the computational burden in both noisy and non-noisy conditions of real-time operation [84], [169]....
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...propagation based ANN [168], NN structure [169] and DT hardware framework [170] have been reported for better understanding of PQDs classification....
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TL;DR: In this paper, a wavelet transform based online voltage disturbance detection approach is proposed to identify voltage disturbances and discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor switching incident.
Abstract: This paper introduces a new online voltage disturbance detection approach based on the wavelet transform. The proposed approach: (1) identifies voltage disturbances; and (2) discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor-switching incident. The proposed approach is: (1) significantly faster; and (2) more precise in discriminating the type of transient event than conventional voltage-based disturbance detection approaches. The feasibility of the proposed disturbance detection approach is demonstrated based on digital time-domain simulation of a power distribution system using the PSCAD/EMTDC software package.
223 citations
Additional excerpts
...This includes online [41], real-time [42]...
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TL;DR: Comparison study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system demonstrates the advantages of S -transform over WT in detection of Islanding and different disturbances under noise-free as well as noisy scenarios.
Abstract: In this paper, comparative study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of S -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.
219 citations
"Comprehensive Review on Detection a..." refers background in this paper
...presented in [90], [106], [120], [139], [152], [172], [191]....
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...with wind turbines [89] and hybrid Solar Photo-Voltaic (SPV), Fuel Cell (FC), and Wind Energy (WE) penetration have been presented in [90]....
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