FAULT DIAGNOSIS AND LOCATION ON MULTI-LOAD DISTRIBUTION NETWORK BY A CURRENT CLARKE–CONCORDIA PATTERN APPROACH

Fault location in electrical energy distribution networks is an important task, as faults in distribution grids are among the main causes of electricity supply disruption. Fault location in the distribution systems, however, is a challenging task because of the topology of the distribution networks, as well as the main and side branches. Therefore, it is necessary to address these challenges through an intelligent approach to fault location. In this paper, fault location in electric energy distribution networks is addressed considering the changes in fault distance and fault resistance in the presence of different fault types. A new method for fault location is developed for conditions where the minimum information is available and only information at the beginning of the feeder is used. This facilitates wide adoption of the technique as it does not require significant investments in instrumentation and measurement. The proposed intelligent method is based on the impedance and transient state estimation. This technique employs a specific impedance analysis for determining possible fault locations considering the unbalanced performance of distribution systems, distances, and different fault resistances. To determine the real faulty section, real fault frequency component analysis and the simulated faults at possible fault locations are used. At this stage of the process, it is possible to eliminate multiple estimations with the help of comparison and identification of the similarities. Therefore, a real faulty section is determined. It is observed that some conditions of electric energy distribution networks affect the accuracy and performance of the proposed method significantly; thus, a detailed investigation is conducted to neutralize these conditions. Simulation results and calculations based on MATLAB along with a practical test of the proposed method in power network simulator confirm a satisfactory performance.

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.

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 combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives

This paper presents a new approach to fault location on distribution power lines. This approach uses an artificial neural network based learning algorithm and Clarke-Concordia transformation. The /spl alpha/,/spl beta/,0 components of line currents resulting from the Clarke-Concordia transformation are used to detect all types of fault. The neural network is trained to map the nonlinear relationship existing in fault location equations. The proposed approach is able to identify and locate all different types of faults (single line to ground, double line to ground, line-to-line and three-phase short-circuit). This approach is subdivided into several main steps: Data acquisition, corresponding on three-phase current signals; Mathematical treatment by the Clarke-Concordia transformation; Fault identification, obtained by the analysis of fault and pre-fault data; Fault location artificial neural network based learning algorithm. The fault position is presented as the output of the neural network on which, as the input, it was considered the eigenvalue of matrix representing transformed line current. Results are presented which shows the effectiveness of the proposed algorithm for a correct fault location on distribution power system networks.

In a low-voltage power distribution system, there are often disturbances that result in disruption of electricity distribution to customers, resulting in losses for customers and PLN. The problem that often occurs is the occurrence of voltage drops and power losses so that in planning the construction of a low-voltage electric power distribution network, it is necessary to anticipate the occurrence of these problems. This research is expected to be used as a lesson for the creation of a low-voltage electric power distribution network with suitable and environmentally friendly construction and with low investment costs. collected data by visiting the location to conduct a survey and obtain accurate data related to the state of the research location. The stages in planning the Low Voltage Electricity Distribution Network begin with a field survey to obtain the required data. The installed power for each unit is 1.300 VA, so the total installed power is 122,200 VA and the spare power is 5% of the installed power so that the total installed power is 128.310 VA. during peak load times it is estimated that power consumption will only absorb 80% or 97,760VA. The required substation is a Portal Type Pole with a transformator capacity is 160kVA. The total breakdown of the electricity distribution network budget at the Griya Arjuna Kejuden is IDR 382,333,141.

Autotransformer (AT) traction network with bilateral power supply mode is an effective and economical electrified railway feeding scheme. However, due to the nonlinear relationship between measured reactance and distance, as well as the complex power flow distribution, the accuracy of fault location is facing severe challenges. This paper proposes a precise fault location method applicable to various fault scenarios in the AT traction network. By employing a generalized symmetrical components method, a composite sequence network model including four-sequence components is developed, and the boundary conditions are derived to describe the characteristics of various fault types. Furthermore, a fault-location algorithm is developed to determine the fault positions based on the busbar voltages and the feeder currents at trackside traction substations. The impact factors, such as the rail impedance and the parameter differences between contact wires and feeder wires, are investigated, and then a reasonable correction method is applied to improve the fault location accuracy. Finally, simulations, experimental setups, and field tests show that the proposed method can obtain accurate fault-location results, with an improvement of at least 29%. Certainly, this work has significant application prospects in fault analysis and location for electrified railways.

Fault location in series capacitor compensated three-terminal transmission lines based on the analysis of voltage and current phasor equations and asynchronous data transfer

Novel fault location algorithm for meshed distribution networks with DERs

Distance protection has been a challenging task due to the complexity of the power system. An effective protection scheme must perform fault detection, classification and location that help in quick restoration of the power supply. Although significant hybrid methods are available for fault detection, classification, and location, separately; there is always a need for an improved integrated approach performing fault detection, classification, and location. This chapter presents an integrated approach using autoregressive modeling and extreme learning machine (ELM). The performance of the scheme is investigated on 11 kV, 6 km medium voltage underground cable. The test system is simulated using PSCAD/EMTDC and the integrated protection scheme is implemented in MATLAB. The scheme uses half-cycle duration current samples. The data preprocessing is done using autoregressive (AR) modeling. The scheme detects the fault using the power of the critical pole. ELM binary classifier uses the AR coefficients to classify the type of fault. ELM in regression mode locates the fault distance. A total of 1260 data set is used to train the ELM fault classifier and ELM fault locator. The integrated protection scheme is tested with 10,800 test cases covering wide variation in operating conditions. The classification accuracy of the ELM fault classifier is 99.85% and the fault location error is less than 0.12% for the ELM fault locator, which is better than the other hybrid methods. Hence this integrated approach can be considered as quite suitable for protection of medium voltage underground cables.

After a severe disturbance due to an insulation failure in a transmission line, the precise fault location is a critical problem for the maintenance crew. In order to avoid further economical and social costs, fault diagnosis has to be performed as soon as possible. Fault diagnosis has been a major area of investigation among power system problems and intelligent system applications. Several approaches have been proposed for solving this problem. This paper advocates the application of support vector machines for mapping the relationship between electrical signals and fault locations in transmission lines. The significance of voltages and currents is analyzed using steady-state and electromagnetic transient information, i.e., two ways of feeding the fault location models are compared. The tests consider different operating and fault conditions, including different types of fault, a variety of fault impedances, fault angles, line loading, equivalent system impedances, and fault locations

The fault location in transmission systems remains a challenging problem, primarily due to the fault location near the substation ends or the weak fault signals. In this study, an adaptive extended Kalman filter (EKF) based on the maximum likelihood (ML) is proposed to estimate the instantaneous amplitudes of the travelling waves. In this method, the EKF algorithm is used to estimate the optimal states (the clean travelling waves) with additive white noise while ML is used to adaptively optimise the error covariance matrices and the initial conditions of the EKF algorithm. Using the proposed method, the singularity points of travelling waves can be detected, and the exact arrival time of the initial wave head at the substations M and N can be easily yielded. Thus the fault distance can be calculated precisely. The effectiveness of exacting mutation feature using the proposed method has been demonstrated by a simulated instantaneous pulse. Also, the proposed method has been tested with different types of faults, such as different fault locations, different fault resistances and different fault inception angles using ATP simulation. The accuracy of fault location using the proposed method has been compared with conventional wavelet transformation scheme.

The application of Micro Phasor Measurement Unit (μPMU) provides a new technological approach for distribution network fault location. In order to improve the accuracy and reliability of fault location, the study of the characteristics for μPMU noise and its impact on fault location algorithms is of necessity. This paper proposed a PMU measurement error model for noise characteristics analysis. And the noise was introduced into the system to examine its effect on the traditional impedance fault location method. To analyze the noise characteristics, a median filter was used to extract the μPMU noise from the raw data. The Monte Carlo method was used to obtain the sample of the fault location error under the influence of μPMU noise. The observed data was fitted with the Gaussian Mixture Model (GMM) then evaluated by the goodness-of-fit (GOF) indexes in regression analysis theory. This method is used on both μPMU noise characteristics analysis and the fault location error under the influence of μPMU noise. The conclusion was reached: μPMU noise and the location error both obey the Gaussian distribution and the result was validated by actual μPMU measured data. The result of fault location error with μPMU noise is presented. The result shows that the accuracy of the impedance method is greatly influenced by the μPMU noise, and the accuracy of double-ended impedance method is higher than single-ended impedance method.

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.

The proposed technique consists of a pre-processing unit based on Continuous Wavelet Transform (CWT) in combination with an Artificial Neural Networks (ANN). CWT acts as an extractor of distinctive features in the transient current signals at the sending and receiving end of the transmission line. This information is then fed to the ANN for detecting the fault type and location of fault. The results presented clearly indicate that the present technique is very fast, computationally efficient and intelligent enough to accurately identify three different types of fault (LG & LLG) and their locations.