Power Network Fault Research Articles

Optimized Fault Detector Based Pattern Recognition Technique to Classify and Localize Electrical Faults in Modern Distribution Systems

This research presents a method that integrates artificial neural networks (ANN) and discrete wavelet transform (DWT) to identify and classify faults in large power networks, as well as to pinpoint the zones where these faults occur. The objective is to enhance reliability and safety by accurately detecting and categorizing electrical faults. To manage the computational demands of processing the extensive and complex data from the power system, the network is divided into optimal zones, each made visible for fault detection. Niche Binary particle swarm optimization (NBPSO) is employed to place the fault detectors (FD) in each zone. This allows for precise measurement of fault voltage and current phasors without significant cost. The ANN module is tasked with identifying the fault area and locating the exact fault within that zone, as well as classifying the specific type of fault. Discrete Wavelet Transform is used for feature extraction, and a phase locked loop (PLL) is used for load angle computation. The proposed method's validity has been tested on the IEEE-33 bus distribution network.

Modeling, Integration and Simulation of the Photovoltaic Power Plant Considering LVRT Capability and Transient Voltage Stability

This paper investigates how high photovoltaic energy penetration impacts dynamic performance and voltage regulation of the modified IEEE-9 bus grid. The transmission power system was modeled and simulated using PSCAD-EMTDC software to conduct the study. Load flow analysis is implemented to explore the power system’s capability to incorporate the desired photovoltaic power. Moreover, the study is based on time response simulations to grid disturbances. The supply and control of reactive power from solar power generation plants are becoming critical issues to study because they can facilitate the integration of PV in power grids under different operating conditions. Network-related faults like a PV solar power plant event outage, a three-phase short-circuit at a conventional bus, and a voltage dip at the PV solar power plant have been considered. The results will help identify the protective devices and strategies needed to maintain the stability and reliability of the system operation and transient analysis of the network under external power network fault and recovery operation. Thus, it has practical significance for real utility studies. Moreover, this comprehensive study will be a valuable guide for assessing and improving the grid’s performance under the study of any other grids, which also gives the vast potential and need for solar energy penetration into the grid systems.

Power Distribution System Faults and Wildfires: Mechanisms and Prevention

Wildfires are one of the most hazardous natural disasters in Australia in terms of fatalities, property damage and financial losses. Events of catastrophic wildfires are recorded across the world including in the United States and Canada. Failures along power distribution infrastructure and network faults have been identified as some of the causes for the initiation of wildfires. Thus, it is critical to better understand the mechanisms and the potential prevention strategies for wildfires caused by power distribution system faults. In this light, this paper presents how the power distribution network faults cause wildfires highlighting the main mechanisms. Further, this paper reviewed studies on recent advancements for the prediction, detection and prevention of wildfires. Condition assessment of power distribution infrastructure including poles, crossarms, overhead cables and other attachments are paramount to detect potential defects and to carry out timely replacements which can subsequently mitigate the possibility of wildfire initiation. Therefore, this paper summarized the studies on condition monitoring and surveillance techniques for power distribution infrastructure. Altogether, this paper aimed to enhance the awareness about the prevention strategies for wildfires caused by power distribution system faults.

IoT Supervised PV-HVDC Combined Wide Area Power Network Security Scheme Using Wavelet-Neuro Analysis

Power system networks are one of the most widely used methods in the real world for transferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission.Due to an increase in power demand, integration of renewable sources to minimise the voltage fluctuations and compensate for power loss is necessary. This is a mandatory requirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under various weather conditions as well as electrical parametric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters calibrated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at different frequencies, which provides important information related to the detailed analysis of faults in power networks. The major goals of this research are to analyse faults using detailed coefficients of current signals through the bior-1.5 mother wavelet for fault identification and artificial neural network analysis for fault localization. This proposed approach furnishes an IoT supervised Photovoltaic - High Voltage Direct Current (HVDC) combined wide area power network security scheme using wavelet detailed coefficients under various types of faults with Fault-Inception-Angles.

Optimal instantaneous prediction of voltage instability due to transient faults in power networks taking into account the dynamic effect of generators

Optimal instantaneous prediction of voltage instability due to transient faults in power networks taking into account the dynamic effect of generators

Power network fault identification based on deep reinforcement learning

With the integration of alternative energy and renewables, the issue of stability and resilience of the power network has received considerable attention. The basic step of fault diagnosis and isolation is fault identification and location. The conventional intelligent fault identification method is supervised, where the characteristics are labelled manually and its hunger to large amount of labelled data. In order to enhance the ability of the intelligent method and get rid of the dependence on a large amount of labelled data, this paper investigates a novel fault identification method based on deep reinforcement learning (DRL), which has not received enough attention in the field of fault identification. The proposed method considers different faults as a kind of parameters of the model so as to expand the scope of fault identification. In addition, the DRL algorithm is able to intelligently modify the fault parameters according to the observations obtained from the environment of power network rather than tuning the parameters manually and mechanically. The methodology is tested on the IEEE 14 bus for several scenarios and the performance of the proposed method is compared with that of population-based optimization methods and supervised learning methods. The obtained results have confirmed the feasibility and effectiveness of proposed method.

Reconfiguration Performance of the Urban Power Distribution System Based on the Genetic-Ant Colony Fusion Algorithm

This study aims to enhance the reliability of the urban power grid system and decrease the economic loss due to power network faults. Based on the analysis of the traditional algorithms for restructuring the urban distribution system after faults, this study proposes an upgraded genetic algorithm (GA) and ant colony algorithm (ACA) and combines these two to overcome the limitations of the local optimum of GAs and low convergence speed of ACAs. Taking the IEEE33-node system as the research object, the network loss, maximum recovery of the power-loss load, and the number of switching operations as the objective function, the impact of different algorithms on the restoration and reconfiguration of the distribution system was examined according to MATLAB system simulation and the optimal algorithm for the reconfiguration of the urban distribution system failure recovery. The experimental results revealed that compared with the current distribution system reconfiguration algorithm, the genetic-ant colony algorithm (GACA) has higher algorithm time efficiency and solution accuracy and can markedly decrease the recovery time and improve the impact of the distribution system in a short period. Overall, the proposed GACA is an efficient self-healing algorithm of urban distribution systems and useful for augmenting the reliability of the urban power system.

Pilot Tests of FasTR Method for Locating Transient Faults in Medium Voltage Underground Power Networks

This paper presents the results of pilot tests performed on a real medium voltage (MV) underground network in Switzerland with the aim of assessing the performance of a fault location system. The deployed fault location platform is based on a very sensitive and precise acquisition system which is capable of capturing perturbations or events on a dedicated portion of a given MV network. Thanks to a post processing algorithm based on the tenets of time reversal theory, FasTR method, an accurate localization of relevant events can be worked. Due to the fact that each acquisition system shares a clock reference based on a GPS antenna, the localization accuracy is within tens of meters. To the best of the Authors knowledge, this is the first time that the FasTR-based fault location technique is validated through live tests. The pilot network is a three branch system part of live radial MV distribution feeder consisting of a total of 2.6 km long underground three-phase cables. The branched lines are composed of coaxial cables of different types. The tests involve solid- and resistive-type single-phase-to-ground fault occurrences triggered along one of the laterals when the network is operational. The obtained results demonstrated the capacity of an on-deck portable system to make an on-line accurate detection of transient faults in a complex network.

An Effective Method Based on Time Reversal and Optimization Techniques for Locating Faults on Power Grids

Electromagnetic time reversal (EMTR) has recently emerged as a promising technique applied for locating faults in power networks. It directly transposes the idea of focusing energy back to its source introduced in original time-reversal (TR) methods. Accordingly, we present in this paper, FasTR, a method based on the tenets of TR, that estimates the fault location by employing optimization based algorithms for fetching the highest peak amplitude with maximum coherence in space and time . However, it uses an alternative approach for executing the cumbersome TR post-processing, thanks to a simplified analytical model capable of evaluating the voltage (or current) at any position and any instant of the tested network resulting from the back-injection of the recorded time-revered signals. FasTR is shown to accurately locate a fault in a complex network with just a basic knowledge of its topology in no more than a couple tens of seconds. More importantly is its ability to locate multiple faults in non-homogeneous networks. The performance of the proposed method is validated by numerical simulations as well as an experimental setup by making reference to a reduced-scale coaxial cable network where real faults are hardware-emulated.

Distributed Power Grid Fault Diagnosis Based on Naive Bayesian Network and D-S Evidence Theory

On the basis of Bayesian network, evidence theory is introduced, a kind of distributed power network fault diagnosis method based on Naive Bayesian network and D-S evidence theory is proposed. Firstly, the fault area is determined by the real-time connection method, the fault region is segmented by the butterfly segmentation method. Secondly, according to the historical fault samples, the decision table is established, and knowledge reduction based on Rough Set Theory. The naive Bayesian network model is constructed according the best combination, and the probability of each node is trained. Finally, D-S evidence fusion is performed on the fault component diagnosis information in the overlap between the subnets. The simulation results show that the proposed method can reduce the complexity of modeling and improve the fault tolerance of the system in the condition of incomplete information, and has good diagnosis results.

Electromagnetic Time Reversal Similarity Characteristics and Its Application to Locating Faults in Power Networks

The existing Electromagnetic Time Reversal (EMTR)-based fault location methods generally leverage the features (e.g., energy) of the signals simulated in the EMTR reversed-time stage (relative to the direct-time stage wherein a fault occurs and its originated transient signals are measured). This paper presents for the first time an integrated time-frequency analysis that identifies the similarity between the signals respectively present in the direct time and in the reversed time. It is demonstrated that the fault current observed at the true fault location exclusively behaves as a quasi-scaled copy of the time-reversed back-injected transient current, such that, the fault location can be identified through a similarity assessment. In this respect, the paper proposes calculating a cross-correlation metric to quantitatively represent the level of similarity between the back-injected transient signal and the fault current signal simulated at the guessed fault locations. The similarity characteristic and the fault location performance of the proposed cross-correlation metric are validated first through simulation case studies based on the IEEE 34-bus test distribution feeder, and then using a pilot test wherein a live medium-voltage radial distribution network was subject to realistic fault occurrences. It is demonstrated that the proposed similarity metric is applicable to locating various types of faults and is robust against uncertainties like fault impedance and fault inception angle.

An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.

A High Precision Time Synchronization Method for Smart Grid based on Beidou Satellite System

With the rapid development of power grid and the large-scale application of automation equipment in power station, the requirement of high precision time synchronization for power system production and control is rapid growth. The accuracy of time synchronization for power system is an important basis to guarantee power network operation control and fault analysis, and a fundamental guarantee to improve power network accident analysis and stability control. In order to ensure that the automatic device receives accurate time synchronization signal, time monitoring and application schemes of different technical routes are studied in this paper. It is proposed that the precise monitoring based on the direct measurement of Beidou can realize high precision time monitoring and synchronization, better guarantee the accuracy of time synchronization between power equipment, ensure the precision of phase measurement, line fault location and time sequence recording of power system, and provide technical support for the stable and reliable operation of power system.

Application Research of Multi-source Information Fusion Technology in Power Network Fault Diagnosis

In this paper, a power grid fault diagnosis method based on multi-source information fusion technology is proposed for grid faults. Firstly, the wavelet transform is used to analyze the electrical quantity information of the fault recorder and PMU, and the three fault reliability characterizations of the components are obtained. Secondly, the information fusion based on the improved D-S evidence theory is carried out for each fault characterization, and the comprehensive fault credibility of the components is obtained, and the fault criterion is given. Finally, the fuzzy C-means method is used to make the diagnosis decision of the faulty component, and the final diagnosis result is obtained. The simulation results show that the method combines the electrical quantities of the fault recorder and the PMU to obtain accurate fault diagnosis results, which can effectively reduce the impact of protection and switch refusal misoperation on power grid fault diagnosis. This method has a good application prospect in power system fault diagnosis.

Research on Abnormal Identification Technology of Circuit Breaker extinction based on wavelet analysis

Circuit breaker is the actuator of power network fault removal and operation mode conversion. If the circuit breaker itself cannot complete the breaking operation, it may cause the accident impact range to expand, or even cause more serious chain failure. The fault phase current is decomposed based on wavelet analysis and Fourier transform, and the unstructured recorded wave data is converted into structured fault characteristic quantity to realize the analysis of current mutation characteristics. The high frequency characteristic singular value at the occurrence of arc is obtained by wavelet transform, which provides the key characteristic quantity for the identification of abnormal arc extinguishing performance of circuit breaker and realizes the identification of abnormal arc extinguishing state of circuit breaker. Issue an alarm before abnormal expansion of circuit breaker or adverse impact, remind relevant personnel to timely check and deal with defects, reduce the occurrence of abnormal tripping circuit breaker phenomenon.

Design of power network fault diagnosis based on time series matching

Common grid fault diagnosis does not fully utilize the alarm timing information generated by the fault. To solve this problem, this paper proposes a fault diagnosis method based on time series. The method analyzes the alarm hypothesis sequence generated by the grid fault and the time sequence actually received by the dispatch center, and utilizes the discrete characteristics of the edit distance and reflects the event discreteness and time continuity of the alarm information by adding the time distance. The calculated data of the similarity between the two sequences and the confidence of the alarm hypothesis sequence determine the faulty component.

The Practical Application of Key Skills of Relay Protection for Dispatching Training

It is one of the basic individual qualities that a dispatcher could deal with all kinds of power network faults calmly. With the development of the society and the updating of the power equipment, the efficiency of the dispatchers are decided by the distinguish ability of the abnormal alarm information of the power grid. Thipaper puts forward three steps of key skills of dispatching training about relay protection, which could help dispatchers to improve their distinguish ability.

Switched-capacitor high-speed emulator for real-time fault location in electrical power systems

This research presents a discrete-time transmission line model based on the propagation of travelling waves. In this approach, the transmission line is emulated by means of many interconnected unit delay cells implemented with switched-capacitor (SC) circuits. The accuracy and limitations of this method is compared to existing transconductance–capacitor solutions and is evaluated in the frame of a novel power network fault location method based on the electromagnetic time-reversal principle. The impact of the non-ideal effects associated to analog CMOS SC circuits, such as amplifier finite gain, offset and switch charge injection is evaluated in the same context. A possible application of the model for the simulation of interconnected or multi-conductor lines is also discussed. After an AMS 0.35 µm process implementation, it is shown that the present method allows a fault location within 1% resolution and is a hundred times faster than nowadays digital solutions. This speed improvement allows a fault location within 160 ms, making thus real-time applications realistic.

High‐speed analogue sampled‐data signal processing for real‐time fault location in electrical power networks

The emulation of low-loss or lossless one-dimensional (1D) or 2D transmission mediums using analogue sampled-data signal processing is presented. Based on discrete-time wave propagation simulation, transmission lines are emulated with many elementary identical delay elements, implemented by simple equivalent switched-capacitor (SC) circuits. The accuracy and limitations of this discrete time model are studied in the frame of power network fault location using electromagnetic time-reversal principle. The sensitivities to non-ideal effects usually plaguing analogue CMOS SC circuits, such as amplifier finite open-loop gain, offset, and parasitic charge injection due to clock feedthrough, are evaluated in the same context. It is shown that the SC line emulation is well suited to the presented fault location technique and considerably reduces the fault location time (by a factor up to 100) in comparison to standard digital solutions, allowing fault location resolutions of typically 1% within a few hundred milliseconds. These expectations are confirmed by measurements realised on the presented line model integrated-circuit, implemented in an AMS 0.35 μm CMOS process. The speed improvement obtained through the presented method is essential, potentially allowing real-time fault management in power grids.

A Novel Methodology for Identifying Cross-Country Faults in Series-Compensated Double Circuit Transmission Lines

In this paper, a competent mechanism for classifying cross-country faults in series-compensated (SC) double circuit transmission line (DCTL) is presented. Cross-country faults are usually different from frequently occurring short-circuit faults in the transmission system. The faults that strike up at the same time but on different positions in the transmission network and can involve same or different phases are termed as cross-country faults. An occurrence of such abnormality in the transmission network significantly hampered the functioning of traditional distance relaying. This paper presents a novel methodology for discriminating normal short-circuit faults and cross-country faults in SC double circuit power network based on empirical mode decomposition (EMD) and intelligent technique. The efficacy of the proposed mechanism for discriminating normal short-circuit faults and cross-country faults in the series-compensated DCTL system, various cases of normal short-circuit faults and cross-country faults (with varying system conditions) have been simulated in Real Time Digital Simulator (RTDS). The results acquired after performing several test cases reveal the practicability of applying proposed EMD and intelligent techniques based mechanism for classification of cross-country faults from normal short-circuit abnormality in the transmission network.