Location Time of Power Quality Disturbances and Noise Using Morphology Gradient and Skeletonization in 3D

In power system, there are some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients that may result in mal-function or failure in operation of some devices. Knowing the location where the disturbances occur in the system is an essential part in selecting the appropriate method on improving power quality issues in order to get effective and efficient results. One of methods in locating power quality disturbances is using Mathematical Morphology (MM). In this paper, signals with disturbances were filtered using morphology gradient. Top-hat transform is applied for the filtered signal using flat structuring element. The simulation results show that the location of disturbances can be detected accurately. Skeletonization is used to identify the location time of the noises in the system. By plotting the results in 3D, it makes easier to identify the location of disturbances with or without noises from their different color and shape as pattern recognition

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area

Patient-adapted and inter-patient ecg classification using neural network and gradient boosting

Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.Web of Science28325424

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Power Quality Signal De-noising with Sub band Adaptive Algorithm

A new level-dependent sub band adaptive noise reduction algorithm based on wavelet transform is proposed in order to improve the effect of power quality signal de-noising for power quality monitoring system. This threshold algorithm has two adjustable parameters to adjust the threshold both fine and coarsely, and the optimal parameters are determined by BP neural networks algorithm. Power disturbance data is referred to actual power disturbance data at IEEE open source and applied for test. The test results indicate that the proposed algorithm could denoise the different kind of power disturbances effectively, and the signal noise ratio is improved further with a smaller mean square error

An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways

Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h

Detection of Power Disturbances for Power Quality Monitoring Using Mathematical Morphology

In power quality monitoring, determining the type of power quality disturbance occurring in the power system is important. Some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients in power systems may result internal-function or failure in the operation of some devices. Knowing the location where the disturbances occur in the system can yield an effective and efficient result when an appropriate method is applied in the attempt to solve the power quality issues. Some traditional strategies such as, wavelet or Fast Fourier transforms have been applied to detect and locate power quality disturbances, suffer from the complexity of the algorithm and the calculation load. In this thesis, mathematical morphology has been investigated for this purpose due to the merits of robustness and the simple calculations needed. In this thesis, some novel strategies using mathematical morphology are presented to find the time location of the disturbances, that is defined as the start and end points when the disturbance occur in the time domain. The first method was using morphology gradient, top-hat transform, and Skeletonization to identify the time location of the disturbances and noise in the system, and then plotting the results in 3D for pattern recognition. This Skeletonization is also combined with Morphology Edge Detection to find the accurate time location of disturbances in the system for both noise free signals and signals with noise. The overall result shows the reduction of the error was significant compared to the result of morphology edge detection strategy. Another novel strategy is presented by converting a signal to an image then applying image processing techniques, which are then evaluated using a control chart to find the time location of any disturbances. This conversion strategy is also applied for detecting the times of power quality disturbances uses short data samples of the signal (4 samples), so that it can be implemented as a real time detection strategy. The results show an accurate strategy in detecting disturbances. Half Multi-resolution Morphology Gradients (HMMG) based on multi-resolution morphology gradients (MMG) is also presented as a novel strategy and it operates in level 1 only, reducing the processing and increase the speed of detection of disturbance. The results show accurate detection when disturbances occur in the system. Other applications of MM are also presented such as a new alternative method in estimating the frequency in a signal based on top-hat and bottom-hat transforms with the results showing the ability of this method to handle low frequencies when the signal is a noise free signal. Neural networks are also implemented with MM for the identification and classification of disturbances. All the novel strategies using Skeletonization, signal/image conversion and HMMG for disturbances detection were then evaluated using a real dataset and an experimental dataset. Overall results show that this three methods can detect disturbances accurately

Power Quality Management and Classification for Smart Grid Application using Machine Learning

The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development