A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Fault Classification and Location Identification on Electrical Transmission Network Based on Machine Learning Methods

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type and location) with high accuracy increases the operational stability and reliability of the power system and helps to avoid huge power failure. Furthermore, proper operation of the protective relays requires the correct determination of the fault type as quickly as possible (e.g., reclosing relays). With advent of smart grid, digital technology is implemented allowing deployment of sensors along the transmission lines which can collect live fault data as they contain useful information which can be used for analyzing disturbances that occur in transmission lines. In this thesis, application of machine learning algorithms for fault classification and location identification on the transmission line has been explored. They have ability to “learn” from the data without explicitly programmed and can independently adapt when exposed to new data. The work presented makes following contributions: 1) Two different architectures are proposed which adapts to any N-terminal in the transmission line. 2) The models proposed do not require large dataset or high sampling frequency. Additionally, they can be trained quickly and generalize well to the problem. 3) The first architecture is based off decision trees for its simplicity, easy visualization which have not been used earlier. Fault location method uses traveling wave-based approach for location of faults. The method is tested with performance better than expected accuracy and fault location error is less than ±1%. 4) The second architecture uses single support vector machine to classify ten types of shunt faults and Regression model for fault location which eliminates manual work. The architecture was tested on real data and has proven to be better than first architecture. The regression model has fault location error less than ±1% for both three and two terminals. 5) Both the architectures are tested on real fault data which gives a substantial evidence of its application

Transient Monitoring Function based Fault Classifier for Relaying Applications

This paper proposes Transient monitoring function (TMF) based fault classification approach for transmission line protection. The classifier provides accurate results under various system conditions involving fault resistance, inception angle, location and load angle. The transient component during fault is measured by TMF and appropriate logics applied for fault classification. Simulation studies using MATLAB®/SIMULINK™ are carried out for a 400 kV, 50 Hz power system with variable system conditions. Results show that the proposed classifier has high classification accuracy. The method developed has been compared with a fault classification technique based on Discrete Wavelet Transform (DWT). The proposed technique can be implemented for real time protection schemes employing distance relaying

Power System Fault Detection Using the Discrete Wavelet Transform and Artificial Neural Networks

This project focuses on detecting various phase to ground faults in three phase power systems. In this research, the faults are generated using a power distribution system simulator; and the three phase voltage waveforms are analyzed using the discrete wavelet transform. Multi-layer feed forward neural networks are employed for fault detection and classification. The effectiveness of this approach is demonstrated by computer simulation results

Faults Detection for Power Systems

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DETECTION OF HIGH IMPEDANCE FAULT USING A PROBABILISTIC NEURAL-NETWORK CLASSFIER

In this paper, a simple and efficient method for detection high impedance fault (HIF) on power distribution systems using an intelligent approach the probabilistic neural network (PNN) combined with wavelet transform technique is proposed. A high impedance fault has impedance enough high so that conventional overcurrent devices, like overcurrent relays and fuses, cannot detect it. While low impedance faults, which include comparatively large fault currents are easily detected by conventional overcurrent devices. Both frequency and time data are needed to get the exact information to classify and detect no fault from HIF. In the proposed method, DWT is used to extract feature of the no fault and HIF signals. The features extracted which comprise the energy of detail and approximate coefficients of the voltage, current and power signals calculated at a chosen level frequency are utilized to train and test the probabilistic neural network (PNN) for a precise classification of no fault from HIFs

Program latihan industri di Kolej Universiti Teknologi Tun Hussein Onn : kajian terhadap perlaksanaan sistem penilaian

Kajian yang dijalankan adalah bertajuk "Program Lalilian lndustri Di Kolej Universiti Teknologi Tun Hussein Onn : Kajian Terhadap Perlaksanaan Sistem Penilaian". Sampel terdin daripada 6 orang pakar serta 63 orang pelajar yang terlibat dalam latihan industri. Maklumat yang diperolehi berdasarkan kaedah kualitatif dan kuantitatif Data dianalisis untuk meninjau kaedah penilaian yang dijalankan dan seterusnya memastikan apakali sistem penilaian yang perlu diperbaiki. Secara keseluruhannya, kebanyakan responden berpendapat bahawa sistem penilaian yang sedia ada adalah perlu diperbaki dan disistematikkan selaras dengan ISO 9000 : 2001. Berdasarkan daripada keputusan yang diperolehi dan bimbingnan pakar dari Unit Latihan lndustri KUiTTHO, maka satu "Buku Panduan Penilaian Latihan lndustri" dihasilkan dengan panduan yang ringkas dan lampiran borang-borang yang telah diperbaiki dan diubahsuai. Diharapkan produk mi dapat digunakan untuk masa-masa akan datang

Islanding Detection in Micro-grids using Sum of Voltage and Current Wavelet Coefficients Energy before the Main Circuit Breaker Side

This paper presents wavelet based islanding detection in distributed generation (DG) interfaced to the microgrid. Also a new fast method is developed for islanding detection based on measuring the utility currents and voltages signals processed by discrete wavelet transform. These currents and voltages signals are measured before the main circuit breaker of microgrid network and their features extracted by discrete wavelet transform. These features are sum of wavelet coefficients energy and are used for distinguishing the islanding conditions from non-islanding ones. Because of changing in measuring point of currents and voltages signals from point of common coupling (PCC) in traditional methods to before the main circuit breaker in proposed method, this new method detects the islanding conditions faster than the other methods. The proposed method has been examined under various scenarios; including mains supply faults, various one, two, or three phases' grid faults, and changes of rate of produced energy on IEEE 1547 anti-islanding test system. The numerical studies show the feasibility and applicability of the proposed method with satisfactory results