High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based Algorithm

An integrated high impedance fault (HIF) and low impedance fault (LIF) detection method is proposed in his paper. For a HIF detection, the proposed technique is based on a number of characteristics of the HIF current. These characteristics are: fault current magnitude; magnitude of the 3rd harmonic current; magnitude of the 5th harmonic current; the angle of the third harmonic current; the angle difference between the third harmonics current and the fundamental voltage; and the negative sequence current of HIF. These characteristics are identified by modeling the distribution feeders in EMTP. Apart from these characteristics, the above ambient (average) negative sequence current is also considered. An adjustable block out region around the average load current is provided. The average load current is calculated at every 18000 cycles (5 minutes) interval. This adaptive feature will not only make the proposed scheme more sensitive to low fault current, but it will also prevent the relay from tripping during the normal load current. In this paper, the logic circuit required for implementing the proposed HIF detection method is also included. With minimal modifications, the logic developed for the HIF detection can be applied to low impedance fault detection. A complete logic circuit which detects both the HIF and LIF is proposed. Using this combined logic, the need of installing separate devices for HIF and LIF detection can be eliminated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Ensemble decision trees for high impedance fault detection in power distribution network

High impedance fault (HIF) is hard to detect owing to their relatively small magnitude compared to normal currents. When an overhead power line loses its support, fall on a poor conductive surface or substance, a high Impedance Fault results. As the fault current is very small during it becomes very difficult to be sensed by over current relay. So equipment and public safety become a matter of great concern. Due to its relatively small fault current nature it became an area of interest and need more detailed study to detect the HIF fast. Here, we try to present a new detection method which uses residual power as an input in the paper. The method is very simple and easy to implement compared to other heuristic methods. The proposed method is tested on eight bus system with DG connected at bus 4. The system is operating at 11kV voltage. The model is simulated in PS CAD. Capacitor switching, Generator switching, Unbalanced load condition, fault with and without DG are also simulated. Obtained results show that the method is able to detect High Impedance Fault with the inception of non-fault events also.

Summary form only given as follows. This paper presents a new approach to the detection of high impedance fault (HIF). The proposed approach (or detector which implements this approach) has four distinct features. First, the detector input signal is the three-phase unbalanced current (or feeder 3I/sub 0/), rather than the conventional three individual phase-currents. Secondly, the detector is designed by monitoring the energy variance for the second, fourth and sixth harmonics of 3I/sub 0/ individually, and by requiring the increment of randomness of all these harmonics to indicate an HIF. Thirdly, a self-tuning scheme based on the statistical confidence is applied to the automatic setting of threshold level for the energy variances monitored. Fourthly, effective counters are designed to detect both persistent and intermittent arcings. The detector has been tested on 51 staged faults and 8 unfaulted feeders. The test results show that the detector can have 100% fault detection rate so long as the arcing current at fault location is not less than 5 A. As to the dependability tests, no false alarm (or tripping) has happened for all of the unfaulted feeders under test. These feeders either supply special (including arc furnace) load, or have leakage current (referring to the intermittent zero-sequence load due to the feeder location along coast), or are under the normal capacitor or load switchings. The test results and the investigation for the high sensitivity and dependability and for the limitation of proposed fault detection method are presented.

It is a challenging task to detect high impedance fault (HIF) in distribution networks. On one hand, although several types of HIF models are available for HIF study, they still do not exhibit satisfactory fault waveforms. On the other hand, utilizing historical data has been a trend recently for using machine learning methods to improve HIF detection. Nonetheless, most proposed methodologies address the HIF issue starting with investigating a limited group of features, and can hardly provide a practical and implementable solution. This paper, however, proposes a systematic design of feature extraction, based on an HIF detection and classification method. For example, features are extracted according to when, how long, and what magnitude the fault events create. Complementary power expert information is also integrated into the feature pools. Subsequently, we propose a ranking procedure in the feature pool for balancing the information gain and the complexity to avoid over-fitting. For implementing the framework, we create an HIF detection logic from a practical perspective. Numerical methods show the proposed HIF detector has very high dependability and security performance under multiple fault scenarios, as compared with other traditional methods.

The transition resistance of high impedance fault (HIF) in distribution network can reach thousands of ohms. The fault characteristics are weak, and it is easy to be confused with normal transient disturbances (NTD), such as no-load line switching, load switching, capacitor switching. Therefore, the fault detection is very difficult. In this paper, the difference of traveling wave full waveform energy distribution between HIF and NTD is deeply analyzed: the energy distribution of HIF is relatively uniform, and the degree of sparseness is small; the energy distribution of NTD is relatively concentrated, and the degree of sparseness is large. Accordingly, a HIF detection method based on the energy distribution sparsity of the traveling wave full waveform is proposed. By calculating the sparseness factor of the time-frequency energy spectrum matrix of the traveling wave full waveform, the sparsity of the energy distribution is represented, and the HIF detection criterion is constructed to reliably identify the and NTD. Simulation analysis shows that this method can sensitively and reliably detect HIF in distribution networks up to 5kΩ.

This article presents a methodology for detection of high impedance faults (HIF). HIF occurs when e.g. a cable makes contact with objects of high electric resistance, resulting in a nonsignificant increase in current. Such faults cannot be detected by traditional protection devices that operate due to overcurrent. The developed methodology is based on making use of variables from a power quality meter (PQM), such as harmonic and sequence components of the currents measured at a substation. Such variables compose descriptors for decision making in artificial neural networks (ANN). In order to test the proposed method HIF events were simulated as well as other common network events using the ATP software. The proposed framework is part of a HIF detection system to be implemented in a substation of an electric utility in the state of Sao Paulo.

An increasing number of fire and electric shock accidents in power sector increases the need for high impedance fault (HIF) detection as the existing over-current relay in the system is not able to detect the HIF state. The HIF detector employing sequence components of current along with voltage is proposed in this paper. The proposed HIF detector composed of two components i.e. current HIF detector and voltage HIF detector, in conjunction with existing distribution network set up. The first component analyzes current sequence components when fault appears in the network whereas later analyzes voltage sequence components. The current HIF detector operates when, significant amount of zero and negative sequence components starts appearing in the network. On the other hand, voltage HIF detector assists the current HIF detector in confirming the HIF state by examining the negative voltage sequence component. The performance of combined detector is evaluated by inducing high impedance fault at different nodes on the IEEE-6-bus radial balanced distribution system. The proposed detector makes use of only current and voltage of the system for HIF detection and doesn't require any additional feature extraction phase. The HIF detector in the proposed paper requires only one unit of sequence converter. Now, sequence components coming out from sequence converter are fed to HIF relay logic which is used in conjunction with existing over-current relay. Hence, this detector is cost effective as compared with the different HIF detectors presented in literature. The proposed method is simulated in a simulation environment. This method is fast and reliable as it detects the fault within one cycle.

High impedance faults (HIFs) are difficult to detect due to the low magnitudes of the associated fault currents, which can be comparable to current levels in steady state operation. When a conductor of a distribution system breaks and falls over on a surface with high electrical resistance, a HIF occurs as well as the formation of an electric arc. Since current levels during HIFs are low, conventional overcurrent protection is not suitable for fault detection, which may represent a risk to people’s safety and to distribution system assets. In this paper, a HIF detection method is developed for Smart Distribution Grids based on the Discrete Wavelet Transform (DWT). The proposed method utilizes the Multiresolution Signal Decomposition (MSD) to decompose current signals in DWT coefficients. The technique is used to identify specific high frequency content in the faulted phase current, caused by the presence of the electric arc during the HIF. Based on the analysis of the DWT coefficients behavior during the fault, a HIF detection algorithm is developed according to a certain indicators. The proposed method is tested on the IEEE 13 Node Test Feeder, which is simulated using the software PSCAD/EMTP.

High impedance fault detection method based on improved Mayr Arc model and fitting curve coefficients

The objective of intelligent electronic devices in grids running fault detection algorithm is to evaluate the special features in patterns of the voltages and currents samples taken at strategic points for transmission and distribution networks and detect if a fault has occurred. Fault detection is necessary to improve power grid reliability and efficiency for safe power supply transmission and distribution. High Impedance Faults (HIF) are an important type of power system faults that are difficult to be detected by conventional over-current protective relays because of their very low fault currents. High impedance faults (HIFs) creates lot of transients in the power system and can result in equipment damage and electrical shocks. Therefore special methods have to be developed and utilized in order to detect them quickly. DWT is an effective method for HIF detection as it is capable of extracting transient information in both frequency and time domains simultaneously. High frequency transient energies is shown to be useful for accurate fault detection and classification.

Single Wire Earth Return (SWER) system is an inexpensive method for extending power to remote areas in scantly populated countries such as Australia. SWER network introduces several challenges to the conventional methods for protection relays. This paper focuses on the protection aspect of the SWER network and investigates the effectiveness of the existing conventional protection mechanism in detecting and suppressing common fault conditions including High Impedance faults (HIFs). While the existing SWER distribution network protection system can detect HIFs, it may be often taken as a normal step load increase due to the low fault current levels. The persistence of the HIFs compromises safety of people within the vicinity of the fault and also increase risk of fire, therefore, a new protection strategy for the detection and identification of HIFs to mitigate the risks is necessary. This paper presents a new algorithm based on mathematical morphology, called the Decomposed Open-Close Alternating Sequence, (DOCAS) for the detection and identification of any power system disturbances in SWER systems. The effectiveness of the proposed method in the detection and identification of the High Impedance Fault and differentiate with other conditions such as Single line-to-ground (SLG) fault and step load increase is demonstrated. The simulation results show that the HIFs can be clearly distinguished from the other conditions by analysing the transients generated by the HIFs.

Detecting high impedance faults (HIFs) is one of the challenging issues for electrical engineers. This type of fault occurs often when one of the overhead conductors is downed and makes contact with the ground, causing a high-voltage conductor to be within the reach of personnel. As the wavelet transform (WT) technique is a powerful tool for transient analysis of fault signals and gives information both on the time domain and frequency domain, this technique has been considered for an unconventional fault like high impedance fault. This paper presents a new technique that utilizes the features of energy contents in detail coefficients (D4 and D5) from the extracted current signal using a discrete wavelet transform in the multiresolution analysis (MRA). The adaptive neurofuzzy inference system (ANFIS) is utilized as a machine learning technique to discriminate HIF from other transient phenomena such as capacitor or load switching, the new protection designed scheme is fully analyzed using MATLAB feeding practical fault data. Simulation studies reveal that the proposed protection is able to detect HIFs in a distribution network with high reliability and can successfully differentiate high impedance faults from other transients.

This paper evaluates the accuracy of a commercialized high impedance fault (HIF) detection relay module for distinguishing HIFs in a real distribution network. Actual HIFs which are modeled based on real-world measurement are used to investigate the detection rate. It is almost impossible to verify the effectiveness of any HIF method under comprehensive scenarios in the field. Therefore, an actual distribution feeder is modeled using a real-time digital simulator (RTDS) and RSCAD software and a hardware-in-the-loop (HIL) platform. Several HIFs with different ground surface types are studied for different positions along the lines with different arc detection sensitivity levels. Extensive tests have been conducted to evaluate the performance of the HIF detection method.

High impedance faults (HIFs) are easy to occur in collective feeders in wind farms and may cause the cascading of wind generators tripping. This kind of faults is difficult to be detected by traditional relay or fuse due to the limited fault current values and the situation is worse in wind farms. The mostly adopted HIF detection algorithms are based on the 3rd harmonic characteristic of the fault zero-sequence currents, whereas these 3rd harmonics are very easy to be polluted by wind power back-to-back converters. To solve this problem, the typical HIF arc flash based on Mayr's arc model is first built, and the typical fault waveforms of HIF in wind farm are presented. Then the characteristics of high impedance faults are analysed to distinguish the occurrence of HIF from other harmonic interference. After that, the performance of the traditional harmonic based HIF detection algorithm is discussed, and a novel detection algorithm is presented to prevent harmonic interference influence in wind farms.