State Estimation Based Fault Location Method for Active Distribution Networks

Better reliability of power is assured with the inculcation of distributed generations in a distribution network. Smart sensors and latest grid communication protocols has led to the development of intelligent microgrids (MG). Protection of such grids are a key concern for power engineers, as conventional protection schemes fails while operating in microgrids. This paper presents, data analysis based fault isolation and its location prognosis. The proposed approach takes post fault, one cycle three phase voltage and current measurements as the inputs for developing a fault isolation module (FIM) and fault locator module (FLM). These modules are supposed to be available at central protection system (CPS), designed through meticulous data training, using Gaussian process regression (GPR) for fault location prediction and support vector machine (SVM) for fault identification. The effectiveness of proposed methodology is represented by considering practical grid scenarios such as varying load and DG penetration. A 14 bus meshed AC microgrid structure has been modelled in SIMULINK having three DGs and two grid sources. Data analytics tools of MATLAB 2018a has been explored to develop machine learning based protection strategy for microgrids.

DC fault current is contributed by various distributed energy resources in DC distribution systems. Most DC distribution systems have low line impedance. The fault current increases fast because of the low impedance. Some converters in DC distribution systems include Fault Current Limiting (FCL) function. The controlled fault currents at different locations are very close. Thus, it is important to design a reliable and fast fault detection and location method for DC distribution systems. This paper proposes a novel local measurement-based fault location algorithm for DC distribution systems. The proposed fault location algorithm can estimate the equivalent inductance from a protective device to a fault in less than one millisecond. The performance of the developed protection algorithm was validated in both numerical simulation and hardware testing.

Accurate fault location in distribution systems greatly shortens maintenance time and improves reliability. This paper presents novel methods to pinpoint fault location and identify possible bad measurements for enhanced accuracy. It is assumed that network parameters and topology of the distribution network are available. The methods are applicable to a single fault as well as simultaneous faults and are applicable to both balanced and unbalanced networks. The methods utilize synchronized voltage and current phasor measurements to locate the fault. The methods are validated by simulation studies using the modified IEEE 34-Node Test System. Case studies have demonstrated that the methods are suitable for distribution systems with high penetration of distributed generations.

The knowledge of line parameters is fundamental for building a network model that can become the basis for all the monitoring and management applications. Network line impedances are employed, for instance, for the state estimation or for protection applications in relay setting. The Phasor Measurement Units (PMUs), thanks to the synchronized voltage and current phasor measurements, provide the accurate information that can be used to evaluate the network parameters of the monitored branches. Nevertheless, the impact of instrument transformers (ITs) makes the parameter estimation a hard task. For this reason, in this paper an estimation paradigm is proposed that includes the estimation of both systematic errors (mainly due to ITs) and line parameters. The proposed method, particularly focused on distribution systems, is designed to perform simultaneous estimations across different branches of the network, aiming at exploiting the links between the involved parameters, due to topology and operating conditions. The estimation is based on a sound and detailed model of the uncertainty sources.

This work presents a new fault location algorithm for transmission lines with tapped legs. For the transmission lines connected with short interim tapped leg on the midway, the conventional multi-terminals fault location algorithms are inappropriate for these systems. The proposed fault location algorithm only uses the synchronized phasors measured on two terminals of the original lines to calculate the fault location. Thus, the existing two-terminals fault locator can still be used via adopting the now proposed algorithms. Using the proposed algorithm, the computation of the fault location does not need the model of tapped leg. The proposed algorithm can be easily applied to any type of tapped leg, such as generators, loads or combined systems. EMTP simulation of a 100 km, 345 kV transmission line have been used to evaluate the performance of the proposed algorithm. The tested cases include various fault types, fault locations, fault resistances, fault inception angles, etc. The study also considers the effect of various types of tapped leg. Simulation results indicate that the proposed algorithm can achieve up to 99.95% accuracy for most tested cases.

A fault detection and location algorithm on three-terminal transmission line is proposed. Vast literature on fault location shows that to simplify the analysis, line charging currents are often ignored. To improve the accuracy of fault location, synchronized current and voltage measurements from three ends of the three-terminal line are utilized to detect and locate distance to fault while considering the line charging currents. The method first detects the zone at which the fault has occurred and then locates it from the respective line ends. Since three-terminal lines provide solutions for connecting existing system to the grid, facilitating bulk transfer of power, thus the fault detection and location on the same is important to maintain reliability and security of the system. Performance of the proposed algorithm is tested for various fault types, fault locations, fault resistances and fault inception angles. The 200-kV system is simulated in ATP/EMTP; however, the algorithm is developed in MATLAB. The extensive simulation studies demonstrate accuracy of the proposed scheme.

A multi-terminal traveling wave fault location method for active distribution network based on residual clustering

Based on the traveling wave fault location principle, the traveling wave fault location method of Teed circuit is studied, and the idea of comprehensively using the three-terminal measurement data of Teed-circuit for fault location is proposed, and a new criterion for fault branch identification and a new method for fault location are proposed accordingly. The branch identification criterion considers the influence of the actual fault location error factors on the branch misjudgment to ensure the validity of the branch identification; and the proposed fault location method makes full use of the three-terminal measurement data of Teed-circuit. Meanwhile, the influence caused by the uncertainty of wave velocity on the fault location result is eliminated in the fault location expression, and the fault location error caused by the line sags is eliminated to a certain extent. The ATP/EMTP simulation results show that the method proposed in this paper can accurately identify fault branches and locate fault points on Teed-circuit.

Electric power distribution network holds a critical role in uninterruptable power supply for modern societies. The distribution network comprising of complex of primary and secondary distribution lines is vulnerable to various short-circuit (SC) faults caused by lightning strikes, storms, growing vegetation, animals, insulation breakdown, and other environmental situations. Most SC faults result in breakdowns that need to be repaired in order to restore electric supply to consumers. Rapid and accurate fault localization method helps in an accelerated system restoration process and thus reduces power outage time. In this paper, a new approach related to the noncontact magnetic field (MF)-based measurement system to localize SC faults on 11-kV overhead distribution lines is presented. The method uses highly sensitive and energy-efficient magnetic sensors to detect variations in MF levels measured along the distribution lines. The fault localization algorithm is developed which identifies SC fault on main feeders and its subbranches and then localize the fault by analyzing MF over window size of 20 cycles. The developed algorithm is implemented on a complex distribution system and tested for different cases. Laboratory experiments were conducted to detect and localize SC faults in a multibranch configuration to validate the proposed method, where measurement uncertainty of less than 5% is observed for the worst case scenario.

Efficient and accurate fault location techniques are beneficial to pinpoint fault position and reduce power outages. Faced with this issue, this paper proposes a novel fault location technique for active distribution networks that utilizes multiple measurement-based Bayesian learning. Specifically, fault location problem in this paper is firstly transformed into solving a block-sparse signal recovery model. In order to enhance robustness in noisy conditions and achieve satisfactory recovery performance, this paper extends the recovery model to a multiple measurement-based model and adopts a block-sparse Bayesian learning (BSBL) algorithm for block-sparse signal recovery. The proposed method requires only a limited number of distribution-level synchronized measurements to be placed, instead of yielding a full observable network. The effectiveness of the proposed method under different noise levels is verified by using IEEE 33-node active distribution system.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Advanced fault location in MTDC networks utilising optically-multiplexed current measurements and machine learning approach

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit.

Penetration of renewable energy sources (RESs) in power systems is increased considerably and fault location algorithms face considerable problems with the presence of RESs. Also, the third terminal of three-terminal lines is expanded as a cost-effective solution to energize the remote areas of medium voltage (MV) systems, while its data may not be measured and easily available. In addition, in cases where this data is obtainable, due to the excessive harmonics of the inverter-based resources (IBRs) in that terminal, the measured data includes high error. Therefore, providing a method for fault location in three-terminal lines in the presence of RESs is challenging. This paper proposes an accurate method to identify the fault location on these lines incorporating RESs without requiring the third terminal data. Due to current transformers saturation and transient response of capacitive voltage transformers, the fault location based on the faulted phase data contains significant errors. The proposed method combines both the faulty and healthy phases data to improve the accuracy of fault location. Its performance is evaluated on a MV system for different scenarios including various fault resistances, locations and types, and third terminal integration of RESs. The obtained results confirm superiority of the proposed method.

This paper presents a general fault-location method for large transmission networks which uses phasor measurement unit (PMU) voltage measurements where the injected current at a fault point can be calculated by using the voltage change and its relevant transfer impedance on any bus. A two-stage fault-location optimization model is proposed, along with defining a matching degree index. The first stage is the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage is used to identify the exact fault line and fault distance. A method to determine optimal PMU placement is also proposed in this paper. Case studies verify that the proposed fault-location algorithm and optimal PMU placement scheme can locate faults in large transmission networks quickly and accurately without requiring fault-type classification or fault phase selection.