Power Quality Disturbance Monitoring Research Articles

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems

Power quality (PQ) in microgrids is a relevant topic, particularly with the complex dynamic behaviour of disturbances produced by the electronic components intrinsic in these technologies. In this paper, a qualitative-quantitative hybrid approach named multiscale recurrence quantification decomposition (MSRQD) combined with support vector machines (SVM) is proposed for PQ monitoring in microgrid systems. MSRQD combines variational mode decomposition (VMD) with recurrence quantification analysis (RQA). To validate the effectiveness of the proposed method, it is compared against other methods. In addition, three study cases are conducted in a microgrid test system using simulations, and real measured data of a main distribution feeder to prove the classification capability of the proposed method in a real environment. This novel approach, provides qualitative graphics and quantitative statistical indexes for disturbance localisation, disturbance time evolution and disturbance characterisation, as well as 3D visualisation feature space of perturbations that makes easy the interpretation to the system operator.

Low voltage smart meter for monitoring of power quality disturbances applied in smart grid

The large amounts of data collected by smart meters (SM), such as electric energy, water gas consumption and power quality (PQ) metrics, can create a massive flow of data transmitted between consumers and utilities. In this context, an edge-fog-cloud architecture based on a low-cost SM is proposed. The employed SM acquires voltage and current signals to obtain their frequency and amplitude, allowing PQ to be monitored through methods of detection and classification of disturbances in order to send only information about the detected disturbances to the utility, thus reducing network traffic associated with PQ disturbances in Smart Grids. The proposed methodology was embedded at a low-cost SM to enable data exchange with the utility, offering an enormous potential for real scenarios.

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

The excessive use of power semiconductor devices in a grid utility increases the malfunction of the control system, produces power quality disturbances (PQDs) and reduces the electrical component life. The present work proposes a novel algorithm based on Improved Principal Component Analysis (IPCA) and 1-Dimensional Convolution Neural Network (1-D-CNN) for detection and classification of PQDs. Firstly, IPCA is used to extract the statistical features of PQDs such as Root Mean Square, Skewness, Range, Kurtosis, Crest Factor, Form Factor. IPCA is decomposed into four levels. The principal component (PC) is obtained by IPCA, and it contains a maximum amount of original data as compare to PCA. 1-D-CNN is also used to extract features such as mean, energy, standard deviation, Shannon entropy, and log-energy entropy. The statistical analysis is employed for optimal feature selection. Secondly, these improved features of the PQDs are fed to the 1-D-CNN-based classifier to gain maximum classification accuracy. The proposed IPCA-1-D-CNN is utilized for classification of 12 types of synthetic and simulated single and multiple PQDs. The simulated PQDs are generated from a modified IEEE bus system with wind energy penetration in the balanced distribution system. Finally, the proposed IPCA-1-D-CNN algorithm has been tested with noise (50 dB to 20 dB) and noiseless environment. The obtained results are compared with SVM and other existing techniques. The comparative results show that the proposed method gives significantly higher classification accuracy.

A new fast discrete S-transform and decision tree for the classification and monitoring of power quality disturbance waveforms

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.

Denoising Power Quality Signal Using Savitzky-Golay Based on Virtual Instrument

In power quality monitoring system, there are unavoidably existing various kinds of noises in collected data，the presence of noise may result in increased false classification rate, denoising is an extremely important work for detection and classification of power quality disturbances. In order to improve the denoising result of power quality signal, an denoising method for power quality signal using Savitzky-Golay is proposed. Numerical results show that the proposed method can eliminate the influence of noise components and implement transient power quality disturbance detection and localization, thus providing good foundations for transient power quality disturbance monitoring under noise environment.

A new fast discrete S-transform for power quality disturbance monitoring

AbstractIn this paper a new fast discrete S-transform (ST) has been proposed for the estimation of power quality (PQ) disturbance signal parameters in an electric power system. The proliferation of non-stationary non-linear loads and various PQ disturbances such as voltage dips, swells, transients, interruptions, voltage flicker, and spikes, etc., necessitate high PQ level in micro and smart grids. The proposed algorithm is based on the generalised Fourier algorithm that combines down sampling and signal cropping to produce a fast discrete ST. The technique is based on Heisenberg’s uncertainty principle, which limits the time-frequency resolution of ST and employs a trade-off between the time resolution and frequency resolution. In ST the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Hence the signal can be down sampled at low frequencies and cropped at high frequencies to result in fewer samples to be evaluated....

A New Fast Discrete S-Transform for Power Quality Disturbance Monitoring

In this paper a new fast discrete S-transform (ST) has been proposed for the estimation of power quality (PQ) disturbance signal parameters in an electric power system. The proliferation of non-stationary non-linear loads and various PQ disturbances such as voltage dips, swells, transients, interruptions, voltage flicker, and spikes, etc., necessitate high PQ level in micro and smart grids. The proposed algorithm is based on the generalised Fourier algorithm that combines down sampling and signal cropping to produce a fast discrete ST. The technique is based on Heisenberg’s uncertainty principle, which limits the time-frequency resolution of ST and employs a trade-off between the time resolution and frequency resolution. In ST the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Hence the signal can be down sampled at low frequencies and cropped at high frequencies to result in fewer samples to be evaluated. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time PQ indices estimation. Various PQ disturbance waveforms like voltage sag, swell, harmonics and interharmonics, and oscillatory transients are considered for tracking and estimation of PQ indices.

Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals

Recently utilities and end users become more concerned about power quality issues because the load equipments are more sensitive to various power quality disturbances, such as harmonics and voltage fluctuation. Harmonic distortion and voltage flicker are the major causes in growing concern about electric power quality. Power quality disturbance monitoring plays an important role in the deregulated power market scenario due to competitiveness among the utilities. This paper presents an evolutionary algorithm approach based on Adaptive Particle Swarm Optimization (APSO) to determine the amplitude, phase and frequency of a power quality signal. In this APSO algorithm the time varying inertia weight is modified as rank based, and re-initialization is used to increase the diversity. In this paper, to the authors highlight the efficacy of different evolutionary optimization techniques like classical PSO, Constriction based PSO, Clonal Algorithm (CLONALOG), Adaptive Bacterial Foraging (ABF) and the proposed Adaptive Particle Swarm Optimization (APSO) to extract different parameters like amplitude, phase and frequency of harmonic distorted power quality signal and voltage flicker.