Fault Detection In Power Systems Research Articles

Hessian Locally Linear Embedding of PMU Data for Efficient Fault Detection in Power Systems

This article develops a computationally efficient fault-detection method for power systems, by exploiting phasor measurement unit (PMU) data and Hessian locally linear embedding (HLLE) technique. First, via HLLE technique, high-dimensional PMU data are transformed into low-dimensional embedding coordinates, which effectively captures the fluctuations of PMU data. Next, based on the feature space of embedding coordinates, T-squared statistic is employed for online fault detection. The method is evaluated on IEEE 39-bus system and a real-world system, exhibiting decent fault-detection performance as well as a considerably lower computational complexity when compared with existent methods.

Earth fault detection in distributed power systems on the basis of artificial neural networks approach

Nowadays, the advancement of microgrids promises numerous economic and environmental advantages of renewable energies to nations and societies. The presence of decentralized energy units, however, makes serious technical challenges; for instance, criteria and procedure of fault recognition and diagnosis in this condition is entirely changing. This article, therefore, proposed a novel accurate and fast technique based on Artificial Neural Networks (ANN) for earth fault detection. A sample distributed power system considered for the proposed technique and different earth faults applied to this system consist of one phase, two phases and three phases faults. Also, any alteration of current and voltage signals of all phases is investigated at the fault occurrence moment. Analysis of simulation results demonstrates how the proposed technique could make faster responses and improve the reliability of the distributed power system by more accurate fault recognition in comparison with the other traditional methods such as the Wavelet Transformation technique. The proposed technique is likely to enhance the growth of renewable energy sources usage by decreasing operational risk factors and fault recognition delays.

Artificial Neural Network Approach for Fault Detection and Identification in Power Systems with Wide Area Measurement Systems

Artificial Neural Network Approach for Fault Detection and Identification in Power Systems with Wide Area Measurement Systems

A real-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning

Frequency Disturbance Events (FDEs) occur due to various events such as Generator Trip (GT), Line Outage (LO), and Load Disconnection (LD), which affect the stability of power systems. Depending on disturbance severity, it has small to severe effects on an integrated system's performance. Accurate and fast detection and classification of the event and its location are crucial in monitoring resource adequacy, preventing economic losses and blackouts. This paper, focusing on an online cutting-edge hierarchical methodology, first detects the event, then identifies its classification. Eventually, the exact location of FDE is found based on Phasor Measurement Units (PMUs) data. Compared to other counterparts, which focused only on classifying or locating events separately, in this paper, both aims are achieved using the suggested novel hierarchical framework. This research employs Deep Learning (DL) advances to develop a Recurrent Neural Network (RNN) model and a Long Short-Term Memory (LSTM) model to distinguish and locate FDEs with significant accuracy. In the research, only a few time-series of Rate-Of-Change-Of-Frequency (ROCOF) received from a limited number of PMUs is used as the DL algorithm's inputs. This hierarchical methodology is tested in New England 39-bus, IEEE 14 bus systems, and modified IEEE 118-bus system. The evaluation results demonstrate the potential application of the proposed models to detect and classify FDEs compared to conventional algorithms and the frequency-based DL model. The proposed models have achieved significant classification accuracy. Simultaneously, the models locate the events with competitive performance in terms of precision, recall, accuracy, and F1-score.© 2017 Elsevier Inc. All rights reserved.

A Phasor Estimation Algorithm Robust to Decaying DC Component

Phasor measurement is widely used in fault detection and localization of power systems. To improve the phasor measurement accuracy when the fault signal contains the decaying dc component (DDC), this article proposes a half-cycle DFT (HCDFT)-based phasor estimation algorithm. The error caused by DDC is estimated and removed from the phasor estimate by combining multiple HCDFT results that are computed using both the conventional basis vector and its corresponding complex conjugate. Both the numerical tests and the IEEE 39-bus system simulation tests are implemented to evaluate the performance of the proposed algorithm in comparison with four related phasor estimation algorithms. Test results show that the proposed algorithm can achieve accurate phasor estimation in the presence of DDC of a wide range of time constants under the interference of harmonics and noise. Moreover, the proposed algorithm is computationally efficient and its performance is independent of sampling rate. These merits make the proposed algorithm suitable to be applied in measurement and protection devices in real power systems.

Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems

Fault detection, diagnosis, identification and location are crucial to improve the sensitivity and reliability of system protection. This maintains power systems continuous proper operation; however, it is challenging in large-scale multi-machine power systems. This paper introduces three novel Deep Learning (DL) classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP). These novel models explore full transient data from pre- and post-fault cycles to make reliable decisions; whereas current and voltage signals are measured through Phasor Measurement Units (PMUs) at different terminals and used as input features to the DRNN models. Sequential Deep Learning (SDL) is employed herein through Long Short-Term Memory (LSTM) to model spatiotemporal sequences of high-dimensional multivariate features to achieve accurate classification and prediction results. The proposed algorithms were tested in a Two-Area Four-Machine Power System. Training and testing data are collected during transmission lines faults of different types introduced at various locations in different regions. The presented algorithms achieved superior detection, classification and location performance with high accuracy and robustness compared to contemporary techniques.

Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

Analysis of Power System Relay Protection and Fault Detection Methods

Analysis of Power System Relay Protection and Fault Detection Methods

Gradient Ascent Optimization for Fault Detection in Electrical Power Systems based on Wavelet Transformation

Background: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like Artificial Neural Network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the Power Loss Rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TL signal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, Feature Extraction Accuracy (FEA), and Fault Detection Time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-ofthe- art works. Materials and Methods: An electric power system incorporates production, broadcast and distribution of electric energy. To send the electric power to massive load centers, transmission lines are exploited. The fast growth of electric power systems results in huge number of lines in operation and total length. TL are susceptible to faults in case of lightning, short circuits, mis-operation, human errors, overload, etc. Faults resulted in tiny to long power outages for customers. To protect the reliable power system operations, Fault identification, isolation and localization are imperative. The voltage lessened to minimal value, when fault occurs on TL. FD is an essential problem in power system engineering to minimize the PLR. DWT-GADNL Technique is introduced for FD in TL during transmission and distribution. Results: Power Loss due to the fault occurrence during the transmission and distribution is a common problem in electrical power system. To lessen the PLR, the fault is detected in earlier stage. From the sample transmission line, the features are extracted and the values are calculated. When the observed value is lesser than the actual value, the fault is detected through performing the gradient ascent optimization process in transmission line. In this optimization process, the local maxima are identified to reduce the PLR. At different time instances, PLR gets changed. At instance 3, the PLR of proposed DWTGADNL framework is 12% where the PLR of Fuzzy Logic Based Algorithm and Fault Diagnosis Framework are 27% and 19% respectively. Through comparing all the ten instances, PLR is reduced in GWMD-DE technique by 59% and 40% compared to existing respectively. Conclusion: DWT-GADNL Technique is introduced for FD during transmission and distribution with minimal PLR. Sample power TL signal is taken and min-max normalization process performs the various rated values estimation of transmission lines. DWT decomposes normalized TL signal to different components for feature extraction with higher accuracy. Gradient Ascent Deep Neural Learning detects the local maximum from extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. The performance of DWT-GADNL technique is tested with the metrics such as PLR, FEA and FDT. With the simulations conducted for all techniques, the proposed DWT-GADNL technique presents better performance on FD during transmission and distribution as evaluated to state-of-the-art works. From simulations results, the DWT-GADNL technique lessens PLR by 50% and enhances FEA by 9% than the existing methods.

A deep learning approach to fault detection in a satellite power system using Gramian angular field

In this paper, an approach based on time series-to-image mapping is proposed for fault detection in a satellite power system (SPS). This approach exploits the possibilities of encoding the SPS time series data as images using Gramian angular fields (GAF). The resulting images are analysed by a convolutional neural network (CNN) for recognising faulty and normal conditions of SPS. Validation with NASA's advanced diagnostics and prognostics testbed (ADAPT) dataset has demonstrated that the combination of CNN with GAF results in better performance when compared to other image encoding methods such as spectrogram and recurrence plot (RP). The proposed approach yields an accuracy of 85.13% with precision 84% and F1 score 0.91 suggesting that encoding multivariate time series data to images using GAF is worth considering for SPS fault diagnosis when compared to other time series-to-image encoding based approaches.

A deep learning approach to fault detection in satellite power system using Gramian angular field

In this paper, an approach based on time series-to-image mapping is proposed for fault detection in a satellite power system (SPS). This approach exploits the possibilities of encoding the SPS time series data as images using Gramian angular fields (GAF). The resulting images are analysed by a convolutional neural network (CNN) for recognising faulty and normal conditions of SPS. Validation with NASA's advanced diagnostics and prognostics testbed (ADAPT) dataset has demonstrated that the combination of CNN with GAF results in better performance when compared to other image encoding methods such as spectrogram and recurrence plot (RP). The proposed approach yields an accuracy of 85.13% with precision 84% and F1 score 0.91 suggesting that encoding multivariate time series data to images using GAF is worth considering for SPS fault diagnosis when compared to other time series-to-image encoding based approaches.

Fault detection in satellite power system using convolutional neural network

Satellite failures account for heavy, irreparable damages, especially when associated with the Power System which is the heart of a satellite. Anomalies in Satellite Power System (SPS) can lead to complete failure of the mission. This demands the need to understand the causes of power system related failures. Huge number of sensors installed in a satellite system conveys information regarding the health of the system. The conventional manual level checking of sensors can be augmented with data driven fault diagnosis approach to reduce the false alarm and burden on operating personnel. The latter has the advantage of exploiting the interrelationship between sensor measurements for fault diagnosis. In this work, Convolutional Neural Network (CNN) is trained on satellite telemetry data for sensor fault detection in SPS. Various processing schemes in time and frequency domains were explored to process the input data to CNN. Promising results were obtained with combination of Stockwell transform (S-transform) and CNN for data processing and classification, respectively. Advanced Diagnostics and Prognostics Testbed (ADAPT), a publicly-available dataset was analysed and used for validating the proposed algorithm, yielding an accuracy as high as 96.7%, precisison of 0.9, F1 score of 0.95 and AUC equal to 0.976.

Deep Learning for Hardware-Based Real-Time Fault Detection and Localization of All Electric Ship MVDC Power System

Deep Learning for Hardware-Based Real-Time Fault Detection and Localization of All Electric Ship MVDC Power System

Deep Learning for Hardware-Based Real-Time Fault Detection and Localization of All Electric Ship MVDC Power System

The tendency toward electrification of marine vessels has led the evolution of the all electric ship (AES). The harsh operating environment of the AES makes the shipboard power system (SPS) vulnerable, so a powerful monitoring system for fault detection and localization (FDL) is essential for safe navigation. We propose a machine learning based FDL method for monitoring the system condition with the problem of imbalanced training dataset. The generative adversarial network (GAN) comprising of deep convolutional neural networks was employed to synthesize numerous valid samples. Feature extraction and selection technologies were applied to time-series signals to reduce features for monitor training. Finally, the random forest (RF) model was trained using the augmented training dataset, combining real data with generated ones by GAN, to verify the capability of the GAN-RF based FDL method. Both real training and testing data were collected from the SPS model established in PSCAD/EMTDC. The results demonstrated that the monitor could distinguish different conditions in real-time with the help of hardware implementation on the FPGA and a 99% classification accuracy was achieved with excellent anti-noise capability.

Infrared Image Segment and Fault Location for Power Equipment

Infrared imaging technology relies on the characteristics of non-contact, high fault diagnosis efficiency, intuitive and accurate data, night monitoring, high equipment sensitivity, strong environmental adaptability, etc., and has become a very important tool in the field of online real-time monitoring of power system. This paper introduces the application of image segmentation in power system fault detection using infrared image, divides the image into fault area and non-fault area, realizes the recognition and location of equipment fault area, and introduces several methods of mainstream image segmentation. In this paper, the application of infrared image segmentation in fault maintenance of power equipment is discussed in detail by taking the fault location of electrical insulator as an example, and the segmentation methods are compared.

Unknown Input Observer Based Robust Actuator Fault Detection and Isolation in Interconnected Power Systems

In this paper, the performance degradation of a generating unit in a multi-area power system regulated by an area-wise decentralized load frequency control is detected and isolated via a scheme of unknown input observers. This scheme is robust to the changes in the load demand, which are considered as the unknown inputs. The actuator faults studied in this work are meant to be the performance degradations in generating units under load frequency control. Both stuck and non-stuck actuator faults are analyzed and the performances of the proposed method in both cases are tested through computer simulations on a two-area power system composed of five generating units. The results show that a performance degradation in any generating unit can be detected and isolated successfully provided that the faults in the sensors measuring generator outputs are absent.

Simultaneous fault detection and diagnosis in electric power system using hybrid method

Simultaneous fault is one of the challenging issues. Faults are the major hurdles in power system designing and protection .Simultaneous fault is the combination of faults indicates that that two or more faults which occur at the same time.The main objective of simultaneous fault detection, classification and location is satisfy accelerates line restoration, maintains system, stability, repairs the fault, decreases the restoration time and increases the system reliability.This paper presents an approach for analysis, detection, classification and location for simultaneous faults in bus bar and transmission line. Two port network is adapted for analysis , voltage and current measurement method is adapted in the fault detection, neural network in the fault classification and location for different types of fault and places were to estimation accurately fault location by analyzing the data available after the beginning of disturbance.All programs were written in MATLAB environment. The programs were test on IEEE- 11 bus bar network. The results clarified that the voltage and current measurement method and impedance method is very effective for simultaneous fault detection, classification and location.

Superresolution Reconstruction of Electrical Equipment Incipient Fault

With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.

Image fusion of fault detection in power system based on deep learning

Aiming at the three main problems of power system—leakage, high temperature and physical damage, a new image fusion of fault detection method in power system based on deep learning is proposed in this paper. The core of deep learning is achieved by capsule network model. The model is trained and tested by self-built image dataset of power system. There are three types of dataset: visible images,infrared images and ultraviolet images. After being preprocessed and feature-extracted, the visible image is used as the fusion image background, the infrared image provides the thermal information of power equipment, and the ultraviolet image provides the electric field information on the exterior of power equipment. The collected images are decomposed into corresponding high frequency component image and low frequency component image respectively, which reconstructed into fused images by the capsule network model. With the registration of the three types of images, the faults in the power system can be detected and displayed accurately in the fused image.

Power system fault detection and classification using wavelet transform and back propagation neural network

Power system fault detection and classification using wavelet transform and back propagation neural network