A critical review of detection and classification of power quality events

Mahela, Om Prakash; Shaik, Abdul Gafoor; Gupta, Neeraj

doi:10.1016/j.rser.2014.08.070

Cited by 344 publications

(169 citation statements)

References 141 publications

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“…The most common electrical disturbances causing low PQ are interruptions, wave faults, voltage sag/swell, and harmonic distortion [3]. Electric supply networks require full attention and complex planning for maintaining an adequate PQ, and avoiding intensive operations induced by disturbances [5]. Each kind of disturbance can be generated by different conditions or defects in the electric network; for example, the source of voltage sags might be a short circuit, a motor starting-up, a transformer energizing, or reacceleration of a motor.…”

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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“…Disturbances of power frequency, faulty connections, harmonic distortion, the variation of power factor, electromagnetic interferences are present in power quality problems (70% to 80%) either in the supply of source side or load side [11,12]. To alleviate PQ problem consumers are equipped with various backup instruments distant from the grid supply.…”

Section: Power Quality Issuesmentioning

confidence: 99%

A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation

Praiselin¹,

Edward²

2018

IJISA

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Abstract-Due to the global demand for energy saving and reduction of greenhouse gas emissions, utilization of renewable energy sources have increased in electricity networks. The negative aspects of this technology are very complex and not well known which affect reliability and robustness of the grids. Microgrids based on renewable energy sources have gained significant popularity, due to the major benefits it has to offer for solving the increasing energy demand. Harmonic distortion in microgrids caused by the non-linear loads is an essential topic of study necessary for the better understanding of power quality impacts in microgrids. The various control techniques utilized to curtail the power quality impacts on micro grids are reviewed in this paper. Also, Optimization based control techniques utilized for power quality improvement in microgrids is discussed in this review.

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“…Equation (4) denotes that PE˚tend to a constant as the tree number increases, so RF is not easy to produce over-fitting problem. The margin function of RF is given as: Then, the strength of t f px, δ k qu is the mathematical expectation of marrpX, Yq:…”

Section: Rf Classification Capability Analysismentioning

confidence: 99%

“…Then, it is necessary to carry out in-depth monitoring and analysis of the PQ in all points accessed by DGs [3]. Therefore, the massive PQ data collected from a large number of monitors represents a higher real-time requirement for any PQ signal classification system [4].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

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A critical review of detection and classification of power quality events

Cited by 344 publications

References 141 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

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