Fault locator and weighting system

α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

The fault of electrode line can pose serious threat to the safe and stable operation of the High Voltage Direct Current (HVDC) system, but it is quite difficult to detect the line fault by normal ways because the grounding electrode line is usually at ground potential. This paper presents a novel fault monitoring and location system for HVDC electrode line, which is based on the TDR technique and specially designed according to the characteristics of electrode line. The system injects differential mode voltage pulse to monitor the electrode line, and reduces the cancellation of differential mode signal through series reactance. Theoretically, this system is immune to the interference signal from the HVDC system, and can greatly improve the Signal-to-Noise Ratio (SNR) of the reflected pulse. In addition, the system uses a waveform comparison algorithm to solve the problem of line parameter uniform and slowly changing, which could make much trouble to the normal TDR methods. For this fault monitoring and location system, the output pulse amplitude is designed as up to 500 V, the pulse width range is about 1-5 $\mu$s, and the pulse sampling rate is as high as 10 MHz. Simulation results and field applications show that this system can achieve excellent fault monitoring and location result for the HVDC electrode line, with the fault location accuracy better than 300m.

International Maritime Organization (IMO) implements the principles of risk management through a systematic process called Formal Safety Assessment (FSA). FSA is a structured and systematic methodology, aimed at enhancing maritime safety, including protection of life, health, the marine environment and property, by using risk analysis and cost benefit assessment. One of the most widely used hazard identification and risk analysis techniques in FSA is Fault Tree Analysis (FTA) method. However, only basic conventional methodology of the FTA technique is used in FSA. When dealing with large fault trees, the limitations of FTA in terms of accuracy and the efficiency become apparent. It is necessary for IMO to improve FTA method in FSA. In recent years, the Binary Decision Diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. In this paper, the BDD method is introduced for the application in FSA, as it has never been used in FSA studies. A commonly used construction method and some basic event ordering methods of BDD are described. By applying BDD to FTA, two main issues concerned in FSA, minimal cut set and top event probability, can be obtained efficiently and accurately comparing to the analysis using a conventional FTA method.

A new method for fuzzy fault tree analysis

Smart transmitters are increasingly being deployed in safety critical systems. Although the processing and diagnostic features provided by digital systems are advantageous, it introduces additional complexity in reliability modelling of systems. The conventional Fault Tree Analysis (FTA) method for system reliability analysis has its shortcomings in modelling digital systems, to incorporate its dynamic system interaction and failure modes in detail. This is because some of the failure modes may arise due to the dynamic interactions between the modules of the system. Dynamic Flowgraph Methodology (DFM) was identified as one of the optimistic approaches to handle these interactions. DFM approach combines multi‐valued logic modelling and analysis capabilities to handle systems consisting of components that have multiple degraded states and exhibit dynamic behaviour. However, only few DFM tools have been developed and some of them are not available in public domain. The DFM approach relies heavily on development and implementation of its decision tables. The logic information present within the decision tables can be meticulously implemented using Petri Nets. Also, there are plenty of free Petri Nets tools available online. Thus, the idea of DFM was used in this work to capture the dynamic interactions by developing decision tables. The decision tables were transformed into the Petri Net models to perform the analysis. The paper demonstrates the potential of the hybrid DFM – Petri Net approach for dynamic reliability analysis of smart transmitter.

A new fault monitoring and location system is available for load monitoring and fault detection of overhead distribution lines. Acquisition units (AUs) are mounted on all three phases and communicate with a data collection unit (DCU) at the bottom of the pole using short-range wireless communications. The DCU communicates with a remote master station through the mobile network, thus forming a complete fault monitoring and location system for the distribution network. The system provides high speed sampling of three-phase voltage and current signals through innovative sensors, the samples are synchronized through communications by the DCU. When a fault occurs, disturbance records are sent by the AUs to the DCU, so that zero sequence components can be calculated. This feature allows the system to detect high impedance earth faults. High speed sampling also allows transients of the signals to be derived, so that the system can be applied to unearthed and compensated networks. For such networks the earth fault current magnitude is insignificant, but there is a dominant transient signal which can be used to identify the faulty line and the faulty section.

Fault location information is critical for operating and maintaining transmission networks. Some of the challenges in calculating accurate fault location include fault resistance, zero-sequence mutual coupling, load, system nonhomogeneity, and transmission lines composed of multiple sections with considerably different characteristics. This paper presents a fully automated real-time fault location system that provides accurate fault location information for multiple transmission lines and makes the results available to system operators and maintenance personnel. The system performs the following tasks: • Retrieves all triggered event reports from relays and digital fault recorders (DFRs) automatically. With these reports, the system identifies the faulted transmission line(s) within the transmission network. • Determines the fault type and calculates the fault location and fault resistance using multiterminal fault location methods for different types of power lines, including overhead lines, underground cables, and composite lines that include both overhead line and underground cable sections. • Uses fault location methods that are accurate under fault resistance, parallel-line mutual coupling, system nonhomogeneity, and load. • Supports event reports with a fixed sampling rate or a variable sampling rate that is a multiple of the power system operating frequency. • Supports event reports generated by relays and DFRs connected to an IRIG-B time source. This paper compares fault location results calculated from a field event report that was obtained from an actual power system with actual fault location data.

Existing traveling wave fault location systems employ the double-end method and do not make use of data from neighboring substations. If one of the data acquisition equipments at either end of a line fails to capture the fault transient, no fault location result can be obtained. A wide-area traveling wave fault location (WA-TWFL) system, which makes use of traveling wave data from various substations across the monitored network, is introduced. The WA-TWFL algorithm first identifies the area of the monitored network in which the source of the disturbance originated by analyzing the traveling wave propagation times using the extended double end method. Then, the faulty line, and the distance to fault, are determined using selected records from the identified area of the disturbance. This approach provides fault location reliability than the traditional TWFL algorithm. Traveling wave data models based on IEC61850, which supports open communication and interoperability between data acquisition equipment and the master station of the WA-TWFL, are also presented in this paper. The feasibility of the proposed algorithm is proved using traveling wave data from an actual fault on a power grid in China.

Transmission lines are crucial for efficient and reliable electricity delivery of electricity but can also be susceptible to faults due to various factors such as external interference, device failures, and ambient conditions. The reliability, accuracy, and efficiency of fault management on the power grid depend heavily on fault classification and fault location. This study integrates a fault detection and classification system for transmission lines and an automatic fault localisation system (ANFIS) for fault location to present a comprehensive approach to managing transmission line problems. The proposed methodology aims to address the technological challenges associated with fault management in power grids by leveraging existing research on radial basis function neural networks (RBFNN) and ANFIS. The integration and validation of the proposed methodology involve incorporating improved fault classification via RBFNN and robust fault locating through ANIS. Simulations and real-world testing will validate the integrated methodology, assessing its performance in various fault scenarios and system configurations. The WSCC 9-bus system is used to validate and test the proposed design, which includes power plants, transformers, transmission networks, and distribution networks. The ClasLoc system has a classification accuracy of 90% and a location accuracy of 89%, ensuring a safe working environment.

Power system fault type classification and location prediction is critical in assessing the reliability of the power system, and later restoring it to a stable operating point followed by a fault. State of the art methods include sequence component, impedance measurement from the origin of the fault, and traveling wave based methods for fault type detection and classification problem. It is important to identify and classify the fault as quickly as possible for restoring power system stability to normal operation. Machine Learning and Deep Learning methods allow the analysis of large data of fault voltages and currents by using fast and efficient algorithms. These methods require large amount of data, however, with the recent advances in the field of power system, data acquisition using smart meters and Phasor Measurement Units (PMU), huge amount of system-wide data can be made available to analyze the problem of fault type classification and location prediction. This paper presents a comparative study of Stochastic Gradient Descent (SGD) based Deep Neural Network (DNN) and Machine Learning (ML) applied to power system fault type and location prediction problem. DNN architecture uses 10 hidden layers, each layer having 60 units with hyperbolic tangent as activation function, and a combination of Support Vector Machine (SVM) and Principal Component Analysis (PCA) method are considered. Comparative results in terms of time taken to run the algorithms, and accuracy of the results obtained are presented for a 3 machine 9 bus system. Results indicate that SVM method is an optimal choice for fault classification with high accuracy for location prediction and low computational requirements compared with DNN.

Limitations of conventional fault tree analysis (FTA) application to modern consumer electronics are outlined, and the necessity for an upgraded FTA is justified. Field and manufacturing failure modes, mechanisms and their interactions are arranged into a unified rank system. An advanced failure tree is developed; new application specific gates are substantiated and introduced. Detailed analysis, formulas, typical examples and recommendations are presented. It is shown that degree/accuracy of mathematical description is determined based on demands of practical accuracy. Broader application of the methodology is justified.

With the development of the industry and commerce in Taiwan, the demand of electricity grows rapidly in the urban areas. Because of the limits of overhead transmission line, underground transmission line becomes the major transmission equipment in Taiwan city. For now, the major fault location algorithms are single-ended, double-ended unsynchronized and double-ended synchronized algorithms. These three algorithms have their own advantages and disadvantages. In this paper, we will verify these three algorithms on Matlab/Simulink. Moreover, we use Google Map (GM) to show the fault location. GM is a powerful geographic information system (GIS) which can show the street scenes. We use it to indicate the location of the fault point and the manhole cover nearby. By this fault location system, Taiwan Power Company (TPC) maintenance personnel can perform maintenance or repair more quickly, and TPC is able to improve power system reliability and reduce customer complaints.

The signal source based on signal injection scheme which is the main equipment of fault location in distribution network injects AC constant current signal to fault lines.However, its constant current value can't be changed according to characteristics of lines.This paper proposes a high power signal source which can be controlled remotely through an Android mobile phone application.Firstly, the signal source has remote data sent-receive ability through GPRS module.Then data forwarding is realized by cloud server which has a fixed IP.Finally, data exchange between mobile client application and signal source GPRS module is realized by the connection to the cloud server.This achieves that Android client application can remote control the high power signal source for fault location in distribution network.

The secondary system of the intelligent substation has a complex logic structure and a variety of failure modes, making it difficult and time-consuming to locate the fault. Aiming at the shortcomings of traditional fault location methods, a fault tree-based positioning method is proposed. Fault tree analysis method can effectively react to fault alarms and the causal relationship between fault locations that cause them. The fault tree analysis method is applied to the expert system, which solves the problems of low efficiency, lack of knowledge and inaccurate reasoning results of the traditional rule-based expert system, and the logical relationship between knowledge is not reflected. Aiming at the difficulty of obtaining the probability of the event at the bottom of the fault tree, the fuzzy number method is proposed to improve the traditional fault tree method, and the triangular fuzzy number is used to express the fuzzy probability of the bottom events, thereby improving the efficiency of the fault maintenance and positioning of the maintenance personnel.

Traveling wave fault location technology is not affected by transition resistance, line structure and other factors, so it is popularized in the power grid application. In order to ensure the accuracy of the fault location system put into operation, it is necessary to carry out the research on the test technology of traveling wave fault location system. First, the technology and composition of voltage traveling wave fault location system were analyzed. Then, aiming at the shortcomings of the existing test methods, a voltage traveling wave fault location system test platform was built. The power cable lines were selected as test line which reflecting the distributed parameters. Through the laboratory equipment, the different types of grounding fault were simulated. The test results showed the test system is close to the grid operating environment, which can effectively test the positioning accuracy and function of the voltage traveling wave fault location system.