Traveling-Wave-Based Fault Location in Electrical Distribution Systems With Digital Simulations

Traveling-wave-based fault location in electrical distribution systems is an important safeguard for the distribution network reliability. The effectiveness of the methods is verified directly in power grid in the early stages, while different fault types can't appear in a short time. And normal dynamic physical simulation cannot meet the teaching demand either because of the limitation of transmission line model and other factors. So PSCAD/EMTDC and MATLAB are used to illustrate the the fault location methods in this paper, which can promote the traveling-wave-based fault-location technology. Meanwhile, the traveling-wave-based fault-location method based on characteristic frequencies is analyzed in this paper

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

An Effective EMTR-Based High-Impedance Fault Location Method for Transmission Lines

This paper summarizes the electromagnetic time reversal (EMTR) technique for fault location, and further numerically validates its effectiveness when the fault impedance is negligible. In addition, a specific EMTR model considering the fault impedance is derived, and the correctness of the model derivation is verified by various calculation methods. Based on this, we found that when the fault impedance is large, the existing EMTR methods might fail to accurately locate the fault. We propose an EMTR method that improves the location effect of high-impedance faults by injecting double-ended signals simultaneously. Theoretical calculations show that this method can achieve accurate location for high-impedance faults. To further illustrate the effectiveness, the proposed method is compared with the existing EMTR methods and the most commonly used traveling wave-based method using wavelet transform. The simulation results show that the proposed double-ended EMTR method can effectively locate high-impedance faults, and it is more robust against synchronization errors compared to the traveling wave method. In addition, the proposed method does not require the knowledge or the a priori guess of the unknown fault impedance

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

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio

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

A Gossip Algorithm based Clock Synchronization Scheme for Smart Grid Applications

The uprising interest in multi-agent based networked system, and the numerous number of applications in the distributed control of the smart grid leads us to address the problem of time synchronization in the smart grid. Utility companies look for new packet based time synchronization solutions with Global Positioning System (GPS) level accuracies beyond traditional packet methods such as Network Time Proto- col (NTP). However GPS based solutions have poor reception in indoor environments and dense urban canyons as well as GPS antenna installation might be costly. Some smart grid nodes such as Phasor Measurement Units (PMUs), fault detection, Wide Area Measurement Systems (WAMS) etc., requires synchronous accuracy as low as 1 ms. On the other hand, 1 sec accuracy is acceptable in management information domain. Acknowledging this, in this study, we introduce gossip algorithm based clock synchronization method among network entities from the decision control and communication point of view. Our method synchronizes clock within dense network with a bandwidth limited environment. Our technique has been tested in different kinds of network topologies- complete, star and random geometric network and demonstrated satisfactory performance