Complex Power Quality Disturbances Research Articles

Dual-layer loss reduction strategy for virtual distribution transformer integrating energy storage converter

To solve the problem that power quality disturbance aggravates the loss of distribution network in new power systems, this paper proposes a loss reduction strategy for virtual distribution transformer with integrated energy storage converter. Firstly, the concept of the virtual distribution transformer is defined through the analysis of the impact of complex power quality disturbances on distribution network losses. Secondly, a dual-layer control strategy for loss reduction based on the virtual distribution transformer is proposed. In the outer control layer, power quality control considering the load rate of the distribution transformer is achieved by extracting the integrated command signal that incorporates load fluctuations and using the improved quasi-proportional resonance controller. In the inner control layer, a grid-connected power stabilization strategy based on an adaptive double-window moving average filter is proposed, which adjusts the sliding window length in real-time according to the amplitude of fluctuation and state of charge, ensuring power meets the grid-connected standard while improving energy storage charge and discharge margins. Finally, simulation results validate that the proposed strategy enables the rapid realization of functions such as virtual capacity enhancement for distribution transformers, power flow control, and integration of new energy sources within short time scales while effectively reducing operating losses in low-voltage distribution networks.

An intelligent complex power quality disturbance recognition method based on two dimension encoding conversion and machine vison

This study proposes an intelligent methodology for accurate identification of complex power quality disturbance (PQD) based on 2D encoding conversion and machine vision. Firstly, a 2D image encoding method as improved Markov transition field (IMTF) is proposed to convert 1D PQD signals to 2D images, which can realize the enhancement of disturbance feature display. It can solve the information loss and duplication problems of traditional Markov transition field (MTF). Then, a new classification method AFF-ResNetXt50 is proposed to realize the PQD accurate recognition. It can address the problem that traditional ResNetXt50 is susceptible to the interference from irrelevant information and thus severely degrades the classification accuracy. Compared with other popular methods, this proposed method can obtain higher accuracy as 98.5 % for PQD signals recognition. Finally, PQD experimental platform is established, and IEEE PES dataset is also used to certify the proposed method has the excellent performances and robustness.

Aiming to Complex Power Quality Disturbances: A Novel Decomposition and Detection Framework

Aiming to Complex Power Quality Disturbances: A Novel Decomposition and Detection Framework

A novel recognition method for complex power quality disturbances based on Markov transition field and improved densely connected network

In order to solve the difficulty that complex power quality disturbances (PQDs) are difficult to recognize accurately and efficiently under the new power system background, this paper proposes a novel PQDs recognition method based on markov transition field (MTF) and improved densely connected network (DenseNet). Firstly, the one-dimensional PQDs signal is mapped into the two-dimensional image with clear texture features by using MTF encoding method. Then, a DenseNet-S lightweight network is designed and the convolutional attention module (CBAM) is introduced to improve its feature extraction ability, so as to enhance the performance of the network. Finally, the images are input into the improved model for training and learning, and PQDs recognition is realized through the optimal model. In order to verify the effectiveness of the proposed method, experimental tests are carried out based on IEEE 1159 standard simulation dataset and real-world field measured signals dataset, and compared with existing recognition methods. The results show that the proposed method can effectively improve the recognition accuracy and noise robustness of complex PQDs, and has more advantages in disturbances recognition efficiency. It can meet the recognition accuracy and efficiency requirements of massive and complex PQDs events in engineering applications.

Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU

Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU’s hidden states at different times is proposed to improve the GRU’s feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity.

A novel dual-attention optimization model for points classification of power quality disturbances

The rapid development of the power grid makes power quality disturbances (PQDs) more complex. Accurately classifying PQDs and measuring the duration of each disturbance element are both crucial for studying the causes of PQDs and subsequent countermeasures. To satisfy the requirements, a novel dual-attention optimization model (DAOM) is presented in this paper for points classification. First, the local feature attention mechanism (LFAM) based on Hilbert transform (HT) is proposed in the input layer to enhance the local features of PQDs. Subsequently, on the basis of convolutional neural network (CNN), a channel attention mechanism (CAM) based on squeeze-and-excitation network (SENet) is introduced to each convolutional layer to achieve the purpose of automatically learning the importance of each channel feature. Finally, each sampling point is classified in the form of multiclass-multioutput. The proposed model is built through the Keras framework, and a synthetic database containing 49 types of PQDs is established to test the model. The proposal achieves an average classification accuracy of 98.95% in a 30 dB white noise environment, which is more precise and robust than other deep learning-based models. At the same time, the proposed model consistently performs best when evaluated through imbalanced data classification metrics and nonparametric test results. Unlike traditional methods, the proposal can accurately identify the occurrence and end time of each element in complex PQDs. Moreover, through a hardware experiment based on the AC source, the proposed model achieves an average accuracy of 98.30%, which is ahead of other comparison models as well.

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.

An Automatic Identification Framework for Complex Power Quality Disturbances Based on Ensemble CNN

A large number of electric vehicles (EVs) are connected to the grid, increasing the risk of power quality deterioration. Meanwhile, power quality disturbances (PQDs) directly affect EV charging safety. Intelligent identification of complex PQDs is the basis for solving the power quality problem, which is very meaningful for improving EV charging quality. This paper proposes an automatic recognition framework for complex PQDs based on ensemble convolution neural network (ECNN). Firstly, a multifusion structure on account of the time and frequency domain feature of PQDs signals is introduced. In addition, a composite convolution is proposed to reduce network complexity, which is using the standard convolution and depthwise separable convolution. Then, we design an adaptive-context mechanism to extend the versatility of ECNN. At the same time, the need to use batch normalization to accelerate training convergence and prevent training overfitting is verified. Furthermore, some visualization methods are performed to analyze the inner mode and illustrate the working mechanism of ECNN. Finally, we apply various experiments to prove the effectiveness of ECNN. Compared to other advanced deep neural networks and traditional methods, ECNN has better noise resistance, higher accuracy, and lower training cost.

Research on Start-stop standby energy storage element participating in wind power filtering under the influence of power quality disturbance

In order to realize effective noise reduction of wind power under the influence of complex power quality disturbance(PQD), an application fusion method of improved smoothing and noise reduction strategy and start-stop energy storage element is proposed in this paper. Firstly, the complex power quality disturbance is filtered and de-noised based on Wiener filtering(WF) algorithm to obtain stable disturbance output. Then, a filtering self-built library(FSBL) is established to perform parallel operations of multiple filtering algorithms and obtain the optimal output power according to the constraints of time scale. After that, a hybrid energy storage system(HESS) with start-stop standby energy storage element is proposed. According to the maximum entropy difference drop point and permutation entropy test, the start-stop instructions of standby energy storage elements can be achieved. Finally, the state of charge(SOC) is optimized according to fuzzy control constraints. In order to verify the effectiveness of the proposed method, this paper constructs the wind power output based on the hardware experimental platform. The power quality disturbance database is further constructed to verify and analyze power quality disturbance types from simple to complex. The results show that the proposed method is reliable. And the hybrid energy storage system with start-stop standby energy storage elements can be widely used in many fields and scenarios.

An Integrated Approach Improved Fast S-transform and SVD Noise Reduction for Classification of Power Quality Disruptions in Noisy Environments

Degraded power quality (PQ) significantly jeopardizes the safety, economics, and efficiency of electricity users. Effective power quality disturbance (PQD) classification is critical for power quality control. To address the issue of noise obscuring the time-frequency domain characteristics of PQD extraction, this research introduces a novel method based on singular value decomposition (SVD) and an improved fast S-transform (IFST). To begin, the disturbance signal is noise reduced using SVD to produce a denoised signal. This denoised signal is then processed using differential sum to generate the exact feature F1, which is used to distinguish stationary from nonstationary disturbances. Additionally, the denoised signal is subjected to IFST to extract features F2–F5. Finally, the five most effective features are fed into a simple ruled decision tree (DT) to automate the classification of disturbances, which includes seven single PQDs and six complex PQDs. The utility of the proposed technique was proven in this research by utilizing both simulated disturbances under varied noise conditions and real data. In comparison to established techniques, the unique method outperforms them in terms of anti-noise performance, allowing for more precise classification of varied types of disturbances, and the extracted specific features can intuitively reflect the dynamic changes in disturbances.

FFNet: An automated identification framework for complex power quality disturbances

Effective identification of complex power quality (PQ) disturbances is the premise and key to improve power quality issues in the current complex power grid environment. However, the influx of nonlinear loads and impact loads makes power system disturbance signals distorted and complex, which increases the difficulty of PQ disturbances identification. To address this issue, this work presents a novel feature fusion network (FFNet) for the automated detection and classification of complex PQ disturbances. Firstly, an adaptive double-resolution S-transform (ADRST) algorithm is proposed for PQ disturbance time-frequency analysis. The adaptive window parameters optimization method is developed in ADRST to improve time-frequency resolution based on energy concentration maximization. Next, an improved convolutional neural network (CNN) method is presented for feature automatical extraction and multiple disturbances classification. Integrating ADRST and improved CNN, a classification framework called FFNet is further proposed to identify various complex PQ disturbances. Finally, experiment cases based on simulation and experimental PQ signals are conducted, where the results demonstrate that the classification accuracy of proposed framework can reach 99.47% even in 20 dB noise level.

A Hybrid Signal Processing Technique for Recognition of Complex Power Quality Disturbances

This paper proposes an algorithm based on the Stockwell transform (ST) and Hilbert transform (HT) to classify complex power quality disturbances (CPQDs). Two indices are used for detection and locating the CPQDs. The first index is the hybrid power quality index (HPQI), which is used to classify CPQDs. The Stockwell maximum values factor (SMVF), Stockwell summation values factor (SSVF), and Stockwell variance values factor (SVVF) are computed by utilizing the ST to process a voltage signal having a superimposed PQ disturbance. To compute the Hilbert index (HI), the voltage signal is also processed using the HT. The HPQI index is calculated by element-to-element multiplication of the SMVF, SSVF, SVVF, HI, and a weight factor (WF). The second index is the hybrid power quality disturbance time localization index (HPLI), which is used to locate the complex PQ events in a given time frame. For classification of investigated CPQDs, seven different features extracted from various indices defined using ST and HT are taken as input to the rule-based decision tree (RBDT). RBDT classifies the disturbances using simple decision rules, which has the merit of low computational time. The algorithm is able to correctly detect CPQDs with a 98% accuracy rate. The proposed algorithm outperforms the literature's algorithm, which is based on HT and RBDT. Furthermore, it is demonstrated that the algorithm effectively recognizes CPQDs that occur in real-time on a practical distribution network in Rajasthan State, India.

Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine

Accurate detection and classification of power quality (PQ) disturbances is an essential prerequisite for PQ mitigation. To address this issue, a new PQ assessment framework based on modified empirical wavelet transform (MEWT) and light gradient boosting machine (LightGBM) is proposed here. First, the frequency estimation mechanism and band segmentation rule of empirical wavelet transform (EWT) is modified, which makes EWT suitable for analysing complex PQ signals. Second, based on the PQ disturbance analysis results of MEWT, 8 characteristic curves are defined and statistical features are extracted in both the time and frequency domain. Third, binary relevance-based LightGBM (BR-LightGBM) is designed for the multilabel classification of massive PQ events. Considering the impact of input features and model structure, feature selection and hyper parameter optimization are conducted for achieving better classification performance. Finally, extensive experiments based on synthetic data and two groups of measured data show the effectiveness of the proposed method on 48 types of complex PQ disturbances. Compared with other algorithms for multiple PQ signals, the proposed method is fast for computation and performs better in classification accuracy and robustness, which is a prospective alternative for the PQ monitoring system.

Complex Power Quality Disturbances Classification Based on Multi-label Active Learning

Complex Power Quality Disturbances Classification Based on Multi-label Active Learning

A Novel Recognition Method for Complex Power Quality Disturbances Based on Visualization Trajectory Circle and Machine Vision

A Novel Recognition Method for Complex Power Quality Disturbances Based on Visualization Trajectory Circle and Machine Vision

Automatic Power Quality Disturbance Diagnosis Based on Residual Denoising Convolutional Auto-Encoder

With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality (PQ) disturbances. Features are extracted from the signals using fast kurtogram, envelope of filtered voltage signal and amplitude spectrum of squared envelop. Proposed algorithm can be implemented for the recognition of the complex PQ disturbances, which include the combination of voltage sag and harmonics, voltage momentary interruption (MI) and oscillatory transient (OT), voltage MI and harmonics, voltage sag and impulsive transient (IT), voltage sag, OT, IT and harmonics. Proposed work has been performed using the MATLAB software. Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform (DWT) and fuzzy C-means clustering (FCM).

A Novel Three-Step Classification Approach Based on Time-Dependent Spectral Features for Complex Power Quality Disturbances

Power quality events caused by renewable-energy integration are usually associated with complex disturbances; therefore, their type identification is the primary task of subsequent pollution control. This study proposes a novel three-step classification approach based on time-dependent spectral features (TDSFs) for the classification of complex power quality disturbances (PQDs). First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is adopted to decompose the PQDs into several intrinsic mode functions (IMFs). The related IMFs are selected by correlation coefficient and kurtosis. Then the eight eigenvalues of each IMF are extracted, including TDSFs. Finally, the eigenvalue dimension of each IMF is reduced by linear discriminant analysis (LDA). Moreover, the classifier of the adaptive k-nearest neighbor with excluding outliers (AdaKNNEO) can confirm the PQDs type. To verify the effectiveness of the proposed approach, a series of simulations and hardware experiments are conducted. The overall result shows robustness and high accuracy of the proposed method, and especially for the complex PQDs, it possesses the highest entirely correct of 96% compared to other advanced methods.

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Deteriorated quality of power leads to problems, such as equipment failure, automatic device resets, data errors, failure of circuit boards, loss of memory, power supply issues, uninterrupted power supply (UPS) systems generate alarm, corruption of software, and heating of wires in distribution network. These problems become more severe when complex (multiple) power quality (PQ) disturbances appear. Hence, this manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell’s transform (ST) and decision tree (DT) using rules. PQ events with complex nature are generated in view of IEEE-1159 standard. Eighteen different types of complex PQ issues are considered and studied which include second, third, and fourth order disturbances. These are obtained by combining the single stage PQ events such as sag & swell in voltage, momentary interruption (MI), spike, flicker, harmonics, notch, impulsive transient (IT), and oscillatory transient (OT). The ST supported frequency contour and proposed plots such as amplitude, summing absolute values, phase and frequency-amplitude obtained by multi-resolution analysis (MRA) of signals are used to identify the complex PQ events. The statistical features such as sum factor, Skewness, amplitude factor, and Kurtosis extracted from these plots are utilized to classify the complex PQ events using rule-based DT. This is established that proposed approach effectively identifies a number of complex nature PQ events with accuracy above 98%. Performance of the proposed method is tested successfully even with noise level of 20 dB signal to noise ratio (SNR). Effectiveness of the proposed algorithm is established by comparing it with the methods reported in literature such as fuzzy c-means clustering (FCM) & adaptive particle swarm optimization (APSO), Wavelet transform (WT) & neural network (NN), spline WT & ST, ST & NN, and ST & fuzzy expert system (FES). Results of simulations are validated by comparing them with real time results computed by Real Time Digital Simulator (RTDS). Different stages for design of complex PQ monitoring device using the proposed approach are also described. It is verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices.

Classification of Complex Power Quality Disturbances Using Optimized S-Transform and Kernel SVM

Accurate power quality disturbance (PQD) classification is significantly important for power grid pollution control. However, the use of nonlinear loads makes power system signals complex and distorted and, thus, increases the difficulty of detecting and classifying PQD signals. To address this issue, this article first proposes an optimized S-transform (OST). It optimizes different window parameters to improve time-frequency resolution using maximum energy concentration. A kernel SVM (KSVM) classifier is proposed to classify multiple features using a combination of kernels. Integrating OST and KSVM, a classification framework is further proposed to detect and classify various PQD signals. Extensive experiments on computer simulations and experimental signals demonstrate that the proposed classification framework shows better performance than several state-of-art methods in classifying not only single and multiple PQD signals but also PQD signals with different noise levels. More importantly, our framework has superior performance in detecting nonlinearly mixed PQD signals.