Fault Section Identification Research Articles

Fault Location and Section Identification Algorithm on Multi-Terminal Transmission Lines Using DWT

Fault Location and Section Identification Algorithm on Multi-Terminal Transmission Lines Using DWT

A Generic Bayesian Method for Faulted Section Identification in Distribution Systems Against Wind-Induced Extreme Events

A Generic Bayesian Method for Faulted Section Identification in Distribution Systems Against Wind-Induced Extreme Events

Fault Section Recognition within Duration of Power Swing Using Teager–Kaiser Energy Operator and Sequence Components

Relaying operation is not reliable during power swing due to the presence of additional lower-frequency components of electrical signals. Relay operation is prohibited during Power swing blocking (PSB) operation to restrict the malfunctioning of the distance relay. During such periods, fault section identification (FSI) schemes need to work for safeguarding the electrical power system from disturbances. Fault detection and fault direction estimation are part of FSI. This article proposes a protection algorithm within the period of power swing where a Teager–Kaiser energy operator (TKEO) of superimposed negative sequence admittance (SNSA) is utilized for fault detection and an integrated scheme for determining their direction. In this integrated direction estimation approach, the direction of fault is recognized by calculating the phases of negative sequence admittance (NSA) and positive sequence admittance (PSA). The proposed technique is verified and tested on a WSCC 9-bus, 400 kV, 50 Hz system for different balanced and unbalanced fault cases with different fault conditions. Even, in some scenarios like CT saturation and single pole tripping (SPT), the proposed approach performs accurately. The results are simulated using EMTDC/PSCAD software.

Fault Section Identification of Hybrid Transmission Lines by the Transients in Modal Domain Free From the Refractions and Reflections at Cross-Bonded Nodes

Compared with the homogeneous transmission lines, the hybrid ones are composed of the overhead, underground and submarine cable lines, resulting in three different sets of parameters over the transmission path and posing great challenge to fault location. This paper proposes a fault section identification method for two types of the hybrid lines based on modal-domain analysis which eliminates the effects of the refractions and reflections at the cross-bonded nodes. First, a method for decoupling the transients in the high-voltage cable is proposed to obtain the unified model of two types of the hybrid lines in modal domain. Then, the concept of the energy of the decoupled transient is defined, the reflection and refraction features of the energy of the transient signal are analyzed, and a novel fault section identification method is developed based on the transient energy ratio. Finally, the model of a 110kV hybrid transmission line is established in PSCAD/EMTDC. Various fault experiments are conducted. The results show the effectiveness of the proposed method.

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

A Method for Fault Section Identification of Distribution Networks Based on Validation of Fault Indicators Using Artificial Neural Network

A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB.

Fault Section Identification of the Power Cables in Urban Distribution Networks by Amplitude Differences Between the Zero-Sequence Currents and Those Flowing in Cable Sheaths and Armors

The power supply reliability of the urban distribution networks is being challenged by cable faults. Owing to the complex factors, such as system neutral, fault condition, and DG integration, it is difficult to detect and locate the fault cable. In this paper, the equivalent circuit model of the distribution cable is firstly created by considering the multiconductor couplings. The mathematical relation of the amplitude differences between zero-sequence currents and those flowing in cable sheaths and armors at the local end of the healthy and fault cables are then derived in theory. Finally, according to the network topology and measurement locations, a novel method for identifying the fault cable section is proposed by using the defined space-measurement matrix. The simulation model of a real urban distribution network is established in PSCAD/EMTDC. The effectiveness and robustness of the proposed method are validated. The results demonstrate that the method is barely affected by the neutral grounding mode, fault initial condition, and distributed generation (DG) penetration level.

A fuzzy-based fault section identification method using dynamic partial tree in distribution systems

In a distribution management system (DMS), fault section identification (FSI) is an essential function for the self-healing capability of smart distribution networks. Conventional FSI methods based on the flag of fault indicators (FIs) cannot be properly utilized for distribution networks with distributed energy resources (DERs) since false FIs, unintentionally activated by fault currents flowing from the downstream of a fault location due to the presence of wye-delta transformers for the DER interconnection, are expected to be generated. For more accurate FSI, this paper proposes a fuzzy-based FSI method using the fault current information measured on feeder remote terminal units (FRTUs) installed at automatic switches in distribution lines (DLs). The proposed method takes advantage of the fuzzy evaluation to provide an intuitive index for decision-making by quantifying the possibility of fault for each candidate section. Furthermore, to achieve faster FSI by reducing waiting time for FSI, the partial trees using only validated FIs with fault current information for the faulty DL are dynamically generated, thereby enabling the fault section can be identified regardless of whether the fault current information from every FRTU is available or not. Case studies on IEEE 33-bus distribution system considering various fault conditions are conducted and simulation results show that the proposed method can accurately identify correct fault sections under false FI and communication failure situations with the improved time required for the FSI.

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

Faulted Section Identification in Mixed Lines Using One End Current Traveling Waves

A faulted section identification method for mixed lines is proposed using initial current traveling waves (TWs) of one end. The ratio of alpha-mode to zero-mode magnitude is used to identify the faulted cable/overhead line section. The properties of attenuation and transmission of zero-mode and alpha-mode current TWs in mixed lines are utilized to obtain a threshold for faulted section identification. The method does not require reflected TWs, data from the remote end, and data synchronization. The performance of the proposed method is evaluated for faults simulated on various transmission networks. The method is advantageous for faults near the junction when the line parameters are uncertain. Comparative assessment demonstrates the superiority of the proposed method for faulted section identification of mixed lines.

Setting Free Fault Location for Three-Terminal Hybrid Transmission Lines Connected With Conventional and Renewable Resources

Three-terminal hybrid transmission lines (TTHTLs) are attractive from both environmental and commercial view. Hybrid transmission lines are growing due to urbanization, connecting an industrial load and renewable integration. A TTHTL comprises sections of both overhead lines and underground/subsea cables or overhead lines with different X/R ratios. Faulted section identification (FSI) is key for defining the adaptive/selective auto-reclosing scheme and estimation of the fault location for TTHTLs. In this paper, FSI and fault location algorithms are proposed without using the parameters of any line section. Novelty of the methodology lies in a two-stage approach to the problem. In the first stage, series impedance parameters of all the sections are calculated using closed loop formulae. These parameters are then utilized in identifying the faulted section. In the second stage, the above calculated line section parameters and faulted section are used to estimate complete line parameters including shunt capacitance and subsequently the fault location. The advantage of the proposed method is that it does not require an initial guess of the line section parameters, is non-iterative in the first stage, and provides correct fault section identification for TTHTLs. These features make it suitable for designing the selective auto-reclosing protection scheme for the TTHTLs within traditional protection relaying hardware. More importantly, the series impedance parameters calculated in the first stage constitute good initialization values for the non-linear problem of estimating the complete line parameters. This results in better convergence of the algorithm and accurate parameter estimation. The developed solution is verified using the PSCAD/EMTDC simulations for TTHTLs connected with conventional and different inverter-based renewable resources. The performance of the proposed solution is compared with commercially available solutions, and it is found to be accurate. This solution is amenable for implementation in line differential protection relays without additional infrastructural changes.

Fault Location Method for the Double-circuit Transmission Line Based on Electromagnetic Time Reversal

The current fault location ways for the double-circuit transmission lines on the same tower do not consider the coupling relationship of transmission lines. The fault location accuracy is low in practical application. Therefore, a fault location method for the double-circuit transmission lines on the same tower basing on electromagnetic time reversal is proposed in this paper. The six-sequence component method is used to transform the transmission line’s impedance matrix to eliminate the coupling between the lines of the double-circuit transmission line on the same pole. The positive and negative phase characteristics of the transmission line are analyzed by using the fault section identification function to decide the transmission lines’ fault section. The mirror line is constructed by applying the electromagnetic time reversal to measure the distance of the fault point. The experimental results indicate that the maximum error of fault location is only 0. 01 km for a double-circuit transmission line on the same tower, which has high measurement accuracy and good practicability.

A novel fault location method for distribution networks with distributed generations based on the time matrix of traveling-waves

To improve location speed, accuracy and reliability, this paper proposes a fault location method for distribution networks based on the time matrix of fault traveling waves. First, an inherent time matrix is established according to the normalized topology of the target distribution network, and a post-fault time matrix is obtained by extracting the head data of initial waves from traveling wave detection devices. A time determination matrix is then obtained using the difference operation between the two matrices. The features of the time determination matrix are used for fault section identification and fault distance calculation, to accurately locate faults. The method is modified by considering economic benefits, through the optimal configuration of detection devices of traveling waves when calculating fault distances. Simulation results show that the proposed method has good adaptation with higher fault location accuracy than two other typical ones. It can deal with faults on invalid branches, and the error rate is under 0.5% even with connected DGs.

An automatic fault section identification method based on distributed traveling wave sensors

As electrification deepens and the society relies more and more on electricity, dependability and safety standard of the power distribution system becomes higher and higher. What is more, to minimize carbon emission, high percentage of renewable energy resources penetration is becoming a reality, which further complicates the distribution system. Because of equipment failure, construction mistake and other causes, power system faults are inevitable. Fault section identification and fault isolation is a critical mission in distribution system operation [1]. As the distribution system develops and evolves, two main ways to accomplish this goal were established: (a) Substation overcurrent relay detects the fault and the corresponding substation breaker isolates the fault. Then trial-and-error method is used to identify and isolate the fault section; (b) Distributed sensors are deployed in the distribution system. Distribution system automation software in the operation center then collects fault information sent from all the distributed sensors. Operation stuff make a judgement on the fault section and fault isolation is done on a remote control or manual operation fashion. These methods leave a lot of room for improvements, which will be elaborated in section II. In this paper, integrated usage of fundamental frequency signals and traveling waves signals is done for fault detection and fault type identification in distributed sensors. And then an automatic fault section identification algorithm is introduced. This algorithm does not require human intervention, which reduces outage time, saves manual efforts and also lays the foundation for automatic electricity restoration.

Fast Low Frequency Fault Location and Section Identification Scheme for VSC-Based Multi-Terminal HVDC Systems

Penetration of DC networks is rapidly increasing in modern power systems. As transient behavior of DC grids is different from that of AC ones, AC grid protection schemes might not be applicable to protect DC networks. Exact fault location is vital for protection of DC networks to reduce repair cost and achieve fast power recovery. This paper proposes a novel topology independent multi-terminal DC scheme to identify fault section and location for DC transmission systems. Voltage and current signals measured at the terminals are analyzed by the least squares error (LSE) algorithm. Then, the proposed scheme uses low frequency components of the signals to locate DC faults offline. Unlike some other methods, it does not require sophisticated sensors and hardware to capture very high frequency content of the signals. Hence, the proposed scheme can be implemented in practice without additional measuring infrastructure. The performance of the proposed scheme is evaluated under various fault conditions for different topologies of DC networks in both overhead lines and cables. Numerous simulations carried out demonstrate that the proposed scheme is quite accurate and provides excellent performance under different fault resistances, sections, and locations.

Faulted Section Identification and Fault Location in Power Network Based on Histogram Analysis of Three-phase Current and Voltage Modulated

Faulted Section Identification and Fault Location in Power Network Based on Histogram Analysis of Three-phase Current and Voltage Modulated

A novel fault location method for hybrid lines based on traveling wave

In this paper, a novel fault traveling wave-based location method for hybrid lines is proposed. According to the refraction characteristics of fault traveling wave (TW) at junction nodes, the node ratio coefficient value Q is defined based on the amplitude of traveling wave. Then, by comparing the Q values of junction nodes and fault points, a fault section identification principle is proposed to determine the fault line section for hybrid lines. After identification of fault line section, a double-ended fault location method is revised via adding the location compensation factors to mitigate the effect of traveling wave velocity changes on fault location for multi-section hybrid lines. Additionally, considering the detection problems of arrival time for TW signals, the extreme-point symmetric mode decomposition (ESMD) method and Teager energy operator (TEO) are proposed. The ESMD method is applied to decompose the sampled fault traveling wave signals into several intrinsic modes to filter out the fundamental frequency and low frequency signals. The TEO is employed to track the characteristics of TW signals. Effectiveness of the proposed method is validated in PSCAD. Results show that the ESMD-TEO can detect TW signals effectively, and the proposed fault location method can determine the fault line section and locate fault positions correctly.

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU.

Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.

Travelling wave fault location for LCC–MMC–MTDC transmission lines based on frequency-dependent characteristic

The paper studies the fault location principles of multi-terminal direct-current (MTDC) hybrid transmission systems based on line-commutated converter (LCC) and modular multi-level converter (MMC). Two issues should be addressed: i) the travelling wave (TW) fault location principle adaptable for the hybrid multi-terminal transmission system; and ii) the algorithm of obtaining frequency-dependent characteristic of the TW signal. In the paper, a novel TW fault location principle adaptable for the LCC–MMC–MTDC hybrid transmission lines is developed based on the frequency-dependent characteristic of TW signal. Firstly, the frequency-dependent characteristic of HVDC transmission line is studied. Secondly, a novel TW fault location principle is proposed, comprised of TW arrival time detection, TW propagation velocity calculation, fault section identification and fault location method. A typical ±400kV LCC–MMC–MTDC hybrid system is constructed using PSCAD/EMTDC for case study to validate the accuracy of the proposed principle. Its robustness against fault location, fault resistance and fault type is well demonstrated.

A Novel Fault Identification Method for HVDC Converter Station Section Based on Energy Relative Entropy

The ac line fault could lead to the operation of converter station protection for high voltage dc (HVDC) systems. In order to avoid the false operation of converter station protection, a novel fault section identification scheme based on energy relative entropy is proposed. The fault characteristics of internal and external for converter station section are analyzed, and it is found that the difference of forward current traveling wave and backward current traveling wave is obvious when the fault occurs in different section. The difference between the forward and backward current traveling wave is represented by the S-transform energy relative entropy. The S-transform energy relative entropy for the external ac line fault is smaller than that of the internal fault of converter station. The performance and effectiveness of the proposed section identification method are evaluated and verified based on simulation and experiment. Finally, the adaptation is studied by considering the fault position, fault type, fault initial angle, transition resistance, and noise. The proposed method can accurately identify the fault of ac system from the converter station and effectively prevent the converter station protection from incorrect operation.