Classification Of Disturbances Research Articles

Real-Time Detection and Classification of Power Quality Disturbances.

This paper considers the problem of real-time detection and classification of power quality disturbances in power delivery systems. We propose a sequential and multivariate disturbance detection method (aiming for quick and accurate detection). Our proposed detector follows a non-parametric and supervised approach, i.e., it learns nominal and anomalous patterns from training data involving clean and disturbance signals. The multivariate nature of the method enables joint processing of data from multiple meters, facilitating quicker detection as a result of the cooperative analysis. We further extend our supervised sequential detection method to a multi-hypothesis setting, which aims to classify the disturbance events as quickly and accurately as possible in a real-time manner. The multi-hypothesis method requires a training dataset per hypothesis, i.e., per each disturbance type as well as the ’no disturbance’ case. The proposed classification method is demonstrated to quickly and accurately detect and classify power disturbances.

LSTM power quality disturbance classification with wavelets and attention mechanism

Efficient detection and classification of power quality disturbances is required with the increasing penetration of multi-energy systems such as microgrids and features from renewable energy resources. Machine learning approach is popular to generate useful and optimal features from data learning to improve the classification performance. This paper aims to analyse the classification performance using the hybrid model of multi-resolution analysis and long short-term memory network. The proposed model uses four-level decomposition wavelet transform to increase the resolution of input signals into multi-bands signal representation. Spatial and temporal feature representation of the wavelet coefficients are highlighted using attention mechanism before feeding into long short-term memory network for sequence feature extraction. The sequence feature output is then passed into multiple dense layer for the classification process. Synthetic disturbance signals are used as training samples. The performance test carried out includes the condition of 20–50 dB signal-to-noise ratio signals, where additive white Gaussian noise are added into the test samples.

Research on power quality disturbance analysis and identification based on LSTM

In view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identification based on LSTM. Firstly, the random single electric energy signal is spliced into a large signal to form a continuous electric energy signal sequence. Secondly, based on the existing neural network, an LSTM model suitable for PQD classification is constructed, and then the spliced large signals are used as input to train and optimize the model. The LSTM model will classify different power quality disturbances. Finally, six common power quality disturbances such as voltage sag, voltage swell, interruption, impact, oscillation and harmonic are simulated and verified respectively. The analysis results show that the high accuracy of the method is reflected, which proves the correctness and effectiveness of the proposed method, and is suitable for the power quality disturbance identification system.

Lifting the Voices of Black Students Labeled With Emotional Disturbance: Calling All Special Education Researchers

Ill-defined behaviors related to emotional disturbance (ED) classification and a lack of cultural competence have contributed to the over-representation of Black children in special education. Several meta-analyses and systematic reviews of the literature have been conducted to examine the topic of over-representation, but to date, there remains debate surrounding this issue. At the same time, and in recognition of the fact that statistical analyses from quantitative research do not lend voice to the participants in research studies, there is a dearth of qualitative research studies focused on Black students eligible for special education under an ED label where the researchers spoke to the students themselves, rather than asking others (e.g., teachers, parents) about them. In the current forum paper, we call attention to this issue by reviewing the existing studies where researchers have spoken to Black students with ED about their school experience, and we place a call to action before the field of special education researchers.

Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid

Due to the popularity of microgrids and power quality disturbances (PQD) induced by renewable energies, monitoring in microgrids has risen in popularity in recent years. For monitoring the PQD, many strategies based on artificial intelligence have been proposed. However, when the electrical parameters change, the need to retrain the Artificial neural network (ANN) becomes a significant issue. This paper presents a new approach to the power quality disturbance detection and monitoring of integrated solar microgrids. The power quality event detection is accomplished by analyzing the frequency signal with Wavelet transformation (WT). The classification of power quality disturbance is achieved based on the features. For the classification of PQDs, the retrieved features are fed into a Convolutional neural network (CNN) classifier.

Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight.

It is a multi-criteria decision issue to conduct a risk assessment of the tunnel. In this paper, a tunnel collapse risk assessment model based on the improved theory of quantification III and the fuzzy comprehensive evaluation method is proposed. According to the geological conditions and the construction disturbance classification method, the evaluation factors are selected, and the tunnel collapse risk level is divided into 5 levels according to the principle of maximum membership degree. The three groups of scores with the largest correlation ratio are calculated by the theory of quantification III to form the X, Y, and Z axes of the spatial coordinate system, The spatial distance of each evaluation factor is optimized by the Kendall correlation coefficient combined with the empirical formula, so that it can be used to judge the probability of the occurrence of the evaluation factor; taking the coupling of the objective entropy weight method (EW) and the subjective analytic hierarchy process (AHP) as the weight. Finally, the fuzzy comprehensive evaluation method is used to determine the possibility classification of tunnel collapse. Taking the Ka-Shuang water diversion tunnel as a case study, the comparison between the evaluation results of 10 tunnel samples and the status quo of the actual engineering area verifies the reliability of the method.

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.

Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network

Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.

A new signal processing approach/method for classification of power quality disturbances

The disadvantage of conventional signal processing methods used for Power Quality (PQ) classification is that their mathematical calculations are complex. Therefore, it is necessary to develop simple, effective, and fast signal processing tools to analyze the PQ signal. In this study, a new signal processing method with simple mathematical operations based on the Integer Factor (IF) down sampling/Approximation Derivatives (AD) was developed to analyze PQ signals. IF was employed to analyze the signals at various sampling frequencies, while the AD method was employed to obtain different degrees of detail and approximation coefficients for the signals. In order to prove that the IF/AD signal processing method can perform a fast and detailed signal processing analysis, the IF/AD method and the Discrete Wavelet Transforms (DWT)/Multiresolution method, which is one of the basic signal processing methods, were compared. It was determined that the developed signal processing method has analyzed PQ disturbances in a shorter time and in more detail than DWT/Multiresolution method. After it was determined that the IF/AD method was effective in the analysis of PQ signals, a new classification method based on the IF-AD signal processing approach, the Slime Mould feature selection algorithm, and Support Vector Machine was developed. The classification method was applied to synthetic signals with noise, real-time data generated in the laboratory and PQ online data. The results proved that the developed classification method was successful. Therefore, this study will provide a different approach to PQ classification methods and non-stationary signal analysis studies.

Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI—Explainable Artificial Intelligence

Advanced machine learning techniques have recently demonstrated outstanding performance when applied to power quality disturbance (PQD) classification. Nevertheless, a possible problem is that power experts may find it hard to trust the results of such algorithms, if they do not fully understand the reasons for their outputs. In this light, this article suggests a method that explains the outputs of PQD classifiers, using explainable artificial intelligence (XAI). The method operates as follows: first, various XAI techniques and classifiers are combined and scored based on their explanations during the validation step. Then, the best combination of classifier and XAI technique for each disturbance is used on the testing set, such that the classifier outputs are more transparent. To accomplish these steps, a definition for the correct explanation in PQD is given. Also, to determine the quality of an explanation for a certain output, we propose an evaluation process to measure the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explainability score</i> for each XAI technique and classifier. By means of this approach, the PQD classifier outputs are optimized to be both accurate and easy to understand, allowing experts to make informed and trustworthy decisions.

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

Siamese Sigmoid Networks for the open classification of grid disturbances in power transmission systems

AbstractThe online classification of grid disturbances is an important prerequisite for an automated and reliable operation of power transmission systems. Most of the state‐of‐the‐art approaches assume that all classes are already known in the training phase and cannot handle new disturbance events, which appear in the application phase and lead to severe misclassifications. To mitigate this shortcoming, the disturbance detection is investigated as an open classification task and a novel recurrent Siamese neural network architecture is introduced to identify and locate known and unknown disturbance events from phasor measurements. Extending preliminary work, a probabilistic distance‐based classification approach with an integrated rejection mechanism is presented, which enables to learn class‐dependent decision boundaries and margins to reduce the open‐set risk. A detailed performance analysis is presented including multiple benchmark methods in different closed‐set and open‐set classification tasks for a simulated power transmission system. Additionally, a limited and full observability of the grid with phasor measurements are addressed in the experiments.

Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

Power quality is one of the most important research eras for the energy sector. Suddenly dropped voltages or suddenly rising voltages and harmonics in energy should be identified. All of these distortions are called power quality disturbances (PQDs). Deep learning based convolutional artificial neural networks with an attention model approach has been carried out. The main idea is to develop a new approach to convolutional neural network (CNN) based which classifies a particular power signal into its respective power quality condition. The attention model approach is based on the idea that the best solution will be taken from the newly produced data pool obtained by rescaling the available data according to the total number of pixels before the average data pool is created and then deep CNN processes will continue. In the attention model approach, all data is multiplied by the number of elements by the number of epoch time sixty-six tensors. The dataset used here contains signals which belong to one of the 9 classes. This means that each signal is characterized by 622 data points and 5600 data parameters. All signals provided are in time domain. Power quality (PQ) is directly depending on power disturbances’ absence or scarcity. The accuracy and error values of the developed model were obtained according to both the number of epochs and the number of iterations.

A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Multiple power quality disturbance (PQD) contains various single disturbances, so its classification is essentially a multilabel classification task. Due to the complexity of multilabel tasks, the performance of existing multilabel methods for PQD is hard to meet practical needs, which severely restricts the engineering application of multilabel methods. Therefore, in this article, we propose a novel multilabel method named LGAN, which incorporates deep learning and explores the correlations between PQD labels to improve performance. First, 1-D convolutional neural network extracts features automatically from PQD signals. Then, a label-guided attention module is adopted to learn the specific feature representation of each PQD category. Finally, the bidirectional recurrent neural network models the label correlations from the label-related features, and then predicts the final PQD category. Various comparative experiments show that the performance of LGAN is much better than the existing multilabel methods. Additionally, the test using real-time detection platform further verifies the availability of the proposed method.

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

A New Classification Scheme Based on Extended Kalman Filter and Support Vector Machine

In the field of power quality monitoring, due to the extensive quantity of measurements data, it is preferred that the detection, identification and classification of Power Quality Disturbances (PQD) can be performed automatically. This paper proposes a new efficient classification scheme based on two powerful techniques, the Extended Kalman Filter (EKF) and the Support Vector Machine (SVM). The feature extraction step plays the most important role, where, the EKF has a good advantage in reducing time computation by dispensing with the segmentation step. Moreover, only three suitable features are extracted from the parameters estimated based on the EKF, thus conserve memory space and time do without need for feature selection step. After the normalization of the extracted features, in the second step, the SVM classifier is applied to classify these disturbances into their specific categories. Simulation results are examined under different Gaussian white noises to prove the efficiency of the proposed classification diagram. Moreover, performance comparisons with other methods proposed in the literature are conducted, to show the effectiveness of the proposed scheme in term of, consumption of memory space, computation time, implementation complexity, and classification accuracy.

A new deep learning method for the classification of power quality disturbances in hybrid power system

A new deep learning method for the classification of power quality disturbances in hybrid power system

Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection

Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.

SVM 을 이용한 무인이동체의 외란 분류 기법 연구

In this paper, we present disturbance classification from unmanned vehicle data using a support vector machine (SVM).Disturbances that occur while operating unmanned vehicles are classified into various categories. We considered a problem in which the unmanned vehicle was a quadrotor model, and the type of operation data that could be used to classify disturbances well was selected by considering the characteristics of the model. Time series data of unmanned vehicle operation are not suitable for solving using SVM, necessitating the extraction of statistical features from time series data that can be well classified. Accordingly, we modeled a quadrotor and various disturbances to investigate unmanned vehicle disturbance classification using an SVM with selected statistical features. Experiments show that the statistical features and SVM can classify the disturbances well.