Types Of Power Quality Disturbances Research Articles

Mitigation of voltage sag and voltage swell by using dynamic voltage restorer

Recent power quality (PQ) research shows that the most common types of power quality disturbances are voltage sags and swells in medium and low-voltage distribution grids. This paper shows how to improve two significant power quality disturbances: sags and swells voltage. To find out what effect these two PQ issues have in real life, a study case based on real nonlinear loads data from large induction motors at Beshai Company in Sadat City in Egypt is investigated. The dynamic voltage restorer (DVR) has been suggested as a solution to the voltage swell and voltage sag issues. The point of common coupling (PCC) is linked with the dynamic voltage restorer to mitigate the PQ problems that have been found. The power network, loads, and DVR may all be modeled using the MATLAB/Simulink platform. The jellyfish search optimizer (JFS) is used to get the gain settings of the proportional and integral (PI) controller for the proposed DVR. MATLAB/Simulink's results show that the proposed device is effective, reliable, and has low latency.

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

In the evolving landscape of power systems, the integration of various renewable energy resources (RERs) introduces complex challenges, particularly in maintaining power quality, which are paramount for system stability. To address this issue, an adaptive power quality disturbance (PQD) detection framework is implemented in this paper. First, the optimal mode decomposition (OMD) is developed to decompose the compound PQDs into sub-ingredients to make them more visible based on the optimal energy ratio. Subsequently, we propose an improved attention convolutional neural network (IACNN), an advanced neural network architecture that leverages an enhanced attention mechanism to expedite the identification of PQDs. Importantly, the sub-ingredients can be strengthened based on the established PQD detection framework. Finally, a series of experiments are conducted under different noise levels and various types of PQDs. The results demonstrate that the proposed framework has profound detection effectivity with about 99.2% accuracy under the simulation condition of 20 dB noise level. In addition, the experimental verification analysis proves a satisfactory real-time performance. This underscores the potential of the proposed framework as a significant advancement in the realm of power quality management, offering a robust solution to the challenges posed by the integration of RERs into modern power systems.

Real-Time Detection of Power Quality Disturbance Using Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System

Power quality disturbances cause equipment damage or financial losses. Therefore, the electric power system needs to identify and distinguish any power quality disturbances to reduce problems. This paper proposes hybrid methods combining FFT and ANFIS algorithm for detection of power quality disturbances. There are 11 types of power quality disturbances that can be detected, such as sag, swell, undervoltage, overvoltage, voltage flicker, voltage harmonic, sag + harmonic, swell + harmonic, undervoltage + harmonic, overvoltage + harmonic, and flicker + harmonic. The parameters used to detect disturbances are Vrms, Duration, THDv (Total Harmonic Distortion voltage), and Fluctuation-Count. The detection process starts by sensing voltage and calculating all the parameters, where THDv was obtained by Fast Fourier Transform. All the parameters such as Vrms, Duration, THDv, and Fluctuation-Count are processed by Adaptive Neuro-Fuzzy Inference System, and the result is the type of disturbance. Matlab simulations show that the suggested method performs outstandingly to identify 11 type of Power Quality Disturbances with 99.3% accuracy.

Empirical Investigations: Power Quality Disturbance Classification

In electrical power systems, one of the most essential parameters is a system signal with stable fundamental amplitude and frequency. Some disturbances have a negative impact on this stability, yet.These disturbances, known as power quality disturbances (PQDs), encompass phenomena such as sag, interruption, swell, harmonics, flicker, interharmonics, spike, notch, and transients. PQDs can ariseindividually or in some combinations. They pose unpredictable and variable effects on system componentsgiving rise to destructive outcomes. Acquisition of real-world datasets related to these disturbances is challenging due to the randomness and complex nature of power systems. Therefore, the importance of conducting experimental studies to investigate and analyze PQDs has significantly increased. This study aims to serve as a valuable resource for researchers investigating PQDs. It provides a guidance, offering insights into some types of PQDs and their characteristics. This paper supports researchers in understanding and addressing the challenges based on PQDs by presenting knowledge about these disturbances and their impacts. Recognizing the significance of experimental studies, the compilation includes methodologies, experimental setups, and tools employed for studying PQDs. It emphasizes the necessity for experimental datasets to enhance research in this field and highlights the importance of PQD investigation.

A Multidimensional Feature-Driven Ensemble Model for Accurate Classification of Complex Power Quality Disturbance

As the proportion of power electronics-related facilities in modern power systems increases, the types of power quality disturbances (PQDs) tend to become more complex. Traditional methods struggle to accurately perform the classification task of complex PQDs under artificial empirical guidance. This paper proposes a multidimensional feature-driven ensemble model for the accurate classification of complex PQDs, which can complete the self-learning function from data. Unlike existing deep learning-based methods, this model considers both spatial features in the time-frequency domain and temporal relational features. Based on fully convolutional networks (FCN) and bidirectional gated recurrent unit (BiGRU), sub-modules suitable for multidimensional features mining are constructed separately. Meanwhile, the squeeze-and-excitation network (SENet) is introduced to complete the computation of the channel attention mechanism for each convolutional layer, which effectively improves the training efficiency and classification accuracy of the model. The proposed model has been thoroughly tested to validate its effectiveness on a synthetic dataset consisting of 71 different types of PQDs under varying signal-to-noise ratios (SNRs). Additionally, the model has been proven to be robust in the face of external factors such as DC offset, frequency variations, and phase jumps. To further demonstrate its reliability, the proposed model has been tested on real PQDs generated from an AC power source. Results from both simulation and experimentation have conclusively shown that the proposed method is superior to existing deep learning-based methods for the classification of complex PQDs.

OPTIMAL PLACEMENT AND RATING OF CUSTOM POWER DEVICES IN 15-BUS SYSTEM FOR POWER QUALITY IMPROMENT WITH STATIC SYNCHRONOUS COMPENSATORS (STATCOMs) AND ACTIVE LINE POWER CONDTIONER

This paper presents on power quality improvement in distributed radial network by using custom power device (CPD). In power system, the power quality issue become more complex at all level. Here, for this concern, we introduce a concept of custom power, basically it is a power electronics controller, to provide a quality of power to the supplier as much as for consumers. Custom power device (CPD) including active power line conditioner (APLC) and static synchronous compensators (STATCOMs), simulating on modified distributed IEEE-15 bus distorted bus system with the help of MATLAB Simulink software to explore performance and the efficiency of each device under various quality (power)disturbances includes voltage swell and sag, interruption in power and total harmonics distortion as well as the performances of allocated CPD in control the voltage quality and reactive power. Here, we used the particle swarm optimization to determine optimal location and sizes of CPD. Here, simulation results reveal all the effect of each device to compensate several types of power quality disturbance depends on the devices configurations and in this paper we much emphasized on STATCOMs and its arrangement.

Comparison analysis of different classification methods of power quality disturbances

<span lang="EN-US">Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.</span>

An image-based deep transfer learning approach to classify power quality disturbances

• Pre-trained deep learning models can be effectively used to detect power quality disturbances. • A new approach is proposed and validated using real-time data. • This approach is based on the use of Transfer Learning and adopts images of voltage waveforms. • The proposed approach allows reducing the complexity of the training process and is suitable for real-time deployment. Power quality disturbances (PQDs) consist in deviation of voltage and current waveforms from the ideal sinusoid at fundamental frequency, and need to be monitored to ensure a reliabile electrical supply. While, traditionally, power quality monitoring has been performed using signal processing techniques, coupled with shallow Machine Learning classifiers or wave change detection methods, more recently, new approaches, based on Deep Learning, have been proposed. These methods have the potential to achieve high classification accuracy and to remove the need of extensive data pre-processing, hence being more suitable for real-time deployments. However, high classification performance has been only demonstrated using synthetically generated data. In order to address limitations related to processing time and accuracy, this paper proposes a novel end-to-end framework for automated detection of PQDs based on Deep Transfer Learning. The proposed approach uses a small set of images of voltage waveforms to train the model and classify different types of PQDs. This method leverages on the high performance of existing pre-trained models for image classification and shows consistent high accuracy for data with varying resolution. The proposed methodology provides a pathway towards effective deployment of Deep Learning in power quality monitoring systems and real-time applications.

Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization-artificial neural network

With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.

PERFORMANCE ASSESSMENT OF IMPROVED CURRENT CONTROL STRATEGY BASED SHUNT ACTIVE POWER FILTER: A REVIEW

The most powerful solution to current harmonics is a shunt active power filter (SAPF). Because of its simple implementation features, the Synchronous Reference Frame (SRF) concept has been commonly used in current harmonics extraction algorithms in its controller. The traditional SRF algorithm, on the other hand, has a significant time delay due to its heavy dependency on slow numerical filters. Furthermore, the algorithm is still thought to have unnecessary features that place an undue computational burden on the controller. Active control filters are commonly used in the power system to reduce harmonics induced by nonlinear loads. The Shunt Active Power Filter (SAPF) injects a suitable compensating current at a line point known as the point of common coupling (PCC) to cancel out the line's harmonics and restore the sinusoidal existence of voltage and current waveforms. As an active filter, a three-phase current-controlled voltage source inverter (VSI) with a DC connection capacitor across it is used. Shunt active power filter (SAPF) is the best solution that minimises the harmonic problems in power systems, but how fast and how effective it is. This paper describes a detailed study about the various types of power quality disturbances and their mitigation techniques and their comparative details.

Research on Advanced Power System Analysis and Control Based on Big Data Technology

The continuous development of big data technology has brought many new ideas and challenges to power system analysis and control. With the development of distributed power sources, energy storage, monitoring, and protection devices, the traditional distribution network has gradually evolved into much Active distribution networks with control ability. At present, the power quality of the power system is a hot issue in power system research. There are many types of power quality disturbances, mainly including single type and compound type disturbances. The thesis first uses Matlab to model and simulate the power quality disturbance signal and then obtains a complex number matrix through S transform, then calculates its modulus, and extracts the corresponding characteristic parameters to form the characteristic vector. Secondly, the thesis uses a simple and efficient decision tree to correctly classify and identify power quality disturbance signals. Finally, the thesis’s simulation analysis results show that this research method combining S transform and decision tree has high recognition accuracy and strong anti-noise ability, which is a very suitable method.

Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines

This paper presents a new method for identifying different types of power quality disturbances (PQDs). In this article, adaptive chirp mode pursuit (ACMP) is used to extract useful features and the greedy algorithm that uses the similar principles of the matching pursuit method is adopted in the ACMP. Besides, the ACMP takes advantage of the sparse matrices, which reduce the computational cost. The infinite feature selection as the filter-based algorithm is applied for the elimination of improper features. Also, the grasshopper optimization algorithm (GOA) is used to optimize the parameters of the SVM as the classifier. The obtained results of the simulations and real disturbances show the high accuracy and speed of the proposed algorithm, which makes it possible to use it in power quality measuring and analysis devices. Also, the proposed method has noise rejection capability, which is another advantage that justifies its use in measuring and analyzing PQDs.

Classification of power quality disturbances based on KF‐ML‐aided S‐transform and multilayers feedforward neural networks

Classifying power quality (PQ) disturbances is one of the most important issues for PQ control. The S-transform (ST)-based neural networks in conjunction with Kalman filter based on maximum likelihood (KF-ML) are presented for classification of PQ disturbances. To accurately extract features in high-noise cases, the KF-ML is used to remove noise from the original distorted waveform. Then, ST technique is used to extract the significant features of disturbances. Finally, a classifier based on multilayers feedforward neural networks can accurately recognise various types of PQ disturbances. Six simulated single disturbances and six complex ones with different noise levels are tested for the sensitivity to noise. Classification results show that the classification accuracy of the proposed method is more than 95% even in 20 dB high-noise condition, and also validate the superiority of strong rejection to noises. Comparison studies between the proposed method and other classification methods are also reported to show the advantages of the proposed approach.

Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance

This study presents a new technique for power quality (PQ) disturbance detection. The technique focuses on voltage sags and interruptions that are related to various faults, i.e. transmission line, feeder, and transformer faults. A maximal overlap discrete wavelet transform-based PQ detection algorithm is proposed to provide accurate points of disturbance initiation and recovery. The proposed PQ detection algorithm is robust even without a detection threshold and independent of the sampling frequency of PQ recording. In consideration of the presence of noise conditions, the preprocessed PQ waveforms are converted into 2D binary vectors using space vector transformation. Then, an improved stacked sparse denoising autoencoder combined with supervised backpropagation training is proposed as a robust classifier. Results show that the proposed method is suitable for detecting various types of PQ disturbances and possesses high recognition accuracy despite insufficient training samples.

Detection and classification of power quality disturbances in distribution network based on VMD and DFA

In this paper, a novel algorithm is proposed to detect and classify the power quality (PQ) disturbances for distribution networks with distributed generation. First, a distribution system with photovoltaic and wind power generation is built as a test platform. Then, nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition (VMD) and the noise is filtered. Meanwhile, the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis (DFA). Power quality disturbances are classified from the view of their distributed energy operational status, and three types of windows are set up to deal with different frequency disturbances. The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted, and the criteria are determined to distinguish the disturbances under the different operating conditions. The simulations show that the algorithm is simpler, more accurate and feasible. It provides an approach for online real-time detection of embedded systems.

Power Quality in DC Distribution Networks

This paper presents an overview of power quality in low-voltage DC distribution networks. We study which of the power quality disturbances in AC networks are also relevant in DC networks, as well as other disturbances specific to DC networks. The paper reviews the current status of international regulations in this topic and proposes different indices for the detection and characterization of the main types of power quality disturbances, presenting some results obtained in different laboratory tests in DC networks using different DC voltage shapes delivered by different DC power source types.

Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

Power utility providers and power industry service providers face a significant challenge in identifying the type of Power Quality Disturbances (PQD) automatically. This paper discusses a method to classify PQD using signal decomposition, statistical analysis and machine learning. Firstly, Discrete Wavelet Transform (DWT) is applied on the generated PQD signals to decompose the signal to obtain its representation in time and frequency domain. Secondly, first and second order statistical parameters are computed on the selected sub-band of DWT. These parameters are used as features vector for the machine learning based classifier. Our database consists of 2400 generated signals of PQD, which were divided into train and test set. Another set of noise corrupted signal database was generated to evaluate the capability of the system. SVM using quadratic kernel was selected as the classifier of the Power Quality Disturbances feature vector. Comparisons were also made with other types of classifiers and other types of mother wavelet filter functions. The results show that the combination of DWT and SVM managed to classify Power Quality Disturbances with high accuracy and has a strong resistance towards noise.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers.

Characterization and Localization of Power Quality disturbances Based on S-transform and Fuzzy Expert System

This paper presents a new technique to characterize and localize the various types of power quality (PQ) disturbances using S-Transform and Fuzzy expert system.The S-Transform method is used to extract the two input features (Standard deviation and Variances) from the distorted voltage waveforms simulated using parametric equations. The extracted features are given as input to Fuzzy expert system for the classification of various types of power quality disturbances. The results clearly indicate that the proposed method has the ability to classify the power quality disturbances with great accuracy. The performance of the proposed

ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification

This study was conducted in order to identify the different types of PQD based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the Power Quality Disturbances (PQD) parameters. The datum of PQD from the PSCAD/EMTDC® simulation has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using cycle windowing technique based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection and detection. The feature and characteristics of four types of signal such as Sag, Swell, Transient and sinusoidal normal signal are obtained. The outcome of the analysis shows that a new approach ANOVA have a different result in term of identification of PQD.