High Impedance Fault Detection Method Research Articles

Reliable high impedance fault detection method based on the roughness of the neutral current in active distribution systems

Detecting High Impedance Faults (HIFs) in Distribution Systems (DSs) still requires effective solutions. HIFs can cause damage, such as shock hazards and bushfires. However, conventional protection cannot identify HIFs due to their low fault current, and most existing solutions neglect the multiple operating scenarios of the DSs. Thus, this paper proposes a new self-adaptive approach for HIF detection, considering the conductor fall period in a system with distributed generation, a condition usually overlooked in existing studies. The algorithm is based only on the fundamental component and third harmonic extracted from the neutral current using the Stockwell Transform. The approach was thoroughly evaluated, considering several scenarios, such as changes in the system loading, background noise, events that can cause false detection, and variations in the distributed generator power output. The tests considered HIFs signals from real field measurements and generated by a simulation model. The results showed that the algorithm is promising, with high detection rates for all the evaluated scenarios, which included over 400,000 HIF and 6,000 non-HIF cases. Additionally, a comparison proved the superiority of the proposed approach over existing methodologies.

Active High-Impedance Fault Detection Method for Resonant Grounding Distribution Networks

Active High-Impedance Fault Detection Method for Resonant Grounding Distribution Networks

An incremental high impedance fault detection method under non-stationary environments in distribution networks

In the non-stationary environments of distribution networks, where operating conditions continually evolve, maintaining reliable high impedance faults (HIF) detection is a significant challenge due to the frequent changes in data distribution caused by environmental variations. In this paper, we propose a novel HIF detection method based on incremental learning to handle non-stationary data stream with changing distributions. The proposed method utilizes stationary wavelet transform (SWT) to extract fault characteristics in different frequency domains from zero-sequence current data. Subsequently, a complex mapping from signal features to operational conditions is established using backpropagation neural network (BPNN) to achieve online detection of HIF. Additionally, signal features are analyzed using density-based spatial clustering of applications with noise (DBSCAN) to monitor the distribution of data. After encountering multiple distribution changes, an incremental learning process based on data replay is initiated to evolve the BPNN model for adapting to the changing data distribution. It is worth noting that the data replay mechanism ensures that the model retains previously acquired knowledge while learning from newly encountered data distributions. The proposed method was implemented in a prototype of a designed edge intelligent terminal and validated using a 10 kV testing system data. The experimental results indicate that the proposed method is capable of identifying and learning new distribution data information within non-stationary data stream. This enables the classifier model to maintain a high level of detection accuracy for the current cycle data, effectively enhancing the reliability of HIF detection.

High-impedance fault detection method based on sparse data divergence discrimination in distribution networks

Due to the weak fault current characteristics, strong nonlinearity and uncertainty, and presence of similar transient features of switching events, it is difficult for traditional methods to accurately detect and identify high faults in a high-noise environment. This paper proposes a high-impedance fault detection method based on sparse data divergence discrimination. First, sensitive sparse principal component analysis is applied to extract the joint features contained in the measurement data. Then, according to the sparse data and singular covariance matrices, an improved Wasserstein divergence solution method is proposed to obtain the difference between data probability distributions before and during a fault. Finally, threshold and time criteria are applied to discriminate against high impedance faults in high-noise cases. The results show that the proposed method can accurately detect and identify high-impedance faults with good robustness to the fault inception angle, transition impedance, noise, and switching events.© 2017 Elsevier Inc. All rights reserved.

High-impedance Fault Detection Method Based on Feature Extraction and Synchronous Data Divergence Discrimination in Distribution Networks

High-impedance Fault Detection Method Based on Feature Extraction and Synchronous Data Divergence Discrimination in Distribution Networks

High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet

The paper proposes a high impedance fault (HIF) detection method in distribution network based on improved Emanuel and DenseNet. Firstly, the improved Emanuel model is introduced to accurately simulate HIF under different working conditions. Secondly, an improved DenseNet model is developed to implement HIF detection, the model uses multi-scale convolution kernels to extract different scale fault information, applies dual-channel Dense Block to transmit fault features more effectively, which can alleviate the vanishing gradient problem. ELU is employed as an activation function to expedite convergent speed of iterative operation. The comparative experiments verify the effectiveness of the proposed method.

Savitzky-Golay Filter integrated matrix pencil method to identify high impedance fault in a renewable penetrated distribution system

Detection and discrimination of a High Impedance Fault (HIF) is difficult from other noteworthy switching events because of the fault path resistance and current signature. The nonlinear current variation poses several protection challenges to existing methods. To mitigate this issue, a Savitzky-Golay Filter (SGF) integrated with a Matrix Pencil Method (MPM) is used to develop a Transient Detection Index (TDI). The SGF-MPM offers advanced filtering to the noisy signal. The extracted SGF is then reconstructed using MPM and an error is estimated which contains only fault generated components. The Teager energy of the error is estimated and compared with an adaptive threshold for secure detection of HIF. The index is verified for several HIFs and non-HIFs using Aalborg test feeder, and modified IEEE 30 bus test systems. The impacts of noise, harmonics, unbalanced loading of lines, voltage sag, system grounding configuration, distribution operating mode, and different switching events on the performance of the proposed method are verified. A comparison with other existing methods is done. From the detailed analysis, it is found that the percentage of accuracy and dependability for the proposed adaptive HIF detection method are obtained as 98.6% and 98.1% respectively.

A High-Impedance Fault Detection Method for Distribution Systems Based on Empirical Wavelet Transform and Differential Faulty Energy

High-impedance faults (HIFs) pose the greatest challenge for distribution system protection, especially for microgrids and distribution networks with distributed generators (DGs) that have flexible operation modes. This paper analyzes the faulty features of HIFs and proposes a HIF detection method that uses empirical wavelet transform (EWT) and differential faulty energy. The proposed method is as follows. First, the various time-frequency components are obtained by utilizing the EWT to decompose the differential faulty energy and adaptively select the feature component with the largest permutation entropy. Second, the permutation variance index is constructed based on the sample point number and feature component energy, and then it is employed to detect HIFs. Finally, low voltage microgrid simulation tests, medium voltage distribution system integrated by DG simulation tests, and field tests show that the proposed method can correctly distinguish HIFs from normal disturbances, including operation mode switches, load switches, capacitor switches, and DG switches. The advantages of the proposed method are also elaborated in detail, from signal preprocessing and feature extraction.

A high impedance fault detection method based on novel attention mechanism and object detection

A high impedance fault detection method based on novel attention mechanism and object detection

High impedance fault detection method for distribution networks under non-linear conditions

This paper presents a new high impedance fault (HIF) detection method considering the presence of non-linear loads in the distribution network. It adopts a cumulative error-based index to monitor the third harmonic phase angle, which is extracted with Stockwell transform and compared to a self-adaptive threshold for fault detection and classification. The proposed method requires only a single measurement device on the substation. Results show that the proposed method is able to quickly detect HIF in all simulated cases, considering several fault locations and different contact surfaces, not being affected by conditions such as fault inception angle, system loading percentage, or fault location. Additionally, results suggest that it can distinguish HIF from capacitor banks switching, low impedance faults, transformer energizing, and feeder energizing along with linear and non-linear loads switching. HIF real oscillographic records are also used for validation. The proposed method achieved a fast detection, in less than 11 cycles for most cases, and a high detection rate, over 99%, with no false positives.

Mathematical morphology-based local fault detection in DC Microgrid clusters

A new local current-based fast high impedance fault (HIF) detection scheme for DC microgrid clusters using mathematical morphology (MM) is proposed in this paper. The proposed strategy consists of two MM based parts. The first part is MM erosion filtering to extract the current signals and its components to extract the differential feature vector. The second part is MM regional maxima, for defining a determinative value to detect faults in a line segment by the lowest possible time. This scheme also uses local measured values to eliminate the need for communication channels, which provide a low cost, reliable, and fast fault detection method for DC microgrid clusters. Moreover, to provide an accurate HIF detection method, the accurate HIF model in DC systems is presented and used in the proposed method. For demonstrating the efficiency, authenticity, and compatibility of the proposed method, digital time-domain simulations are carried out in MATLAB/Simulink environment under different scenarios such as overload, noise, low and HIFs to distinguish between overloads and HIFs, and the results are compared with several reported algorithms. The obtained simulation results are verified by experimental tests, which validate the proposed strategy's accuracy and speed under different conditions.

A Transfer Learning-Based High Impedance Fault Detection Method Under a Cloud-Edge Collaboration Framework

High impedance faults (HIFs) in distribution networks are hard to describe and be detected precisely because of the complexity and randomness of their features. Therefore, traditional feature analysis methods may lack sufficient reliability and generalization, which makes data-based methods a more appropriate option. However, according to previous statistical analyses, in practical scenarios, only a small quantity of historical HIF data (less than 20%) can be recorded and utilized. In this article, a transfer learning-based HIF detection method is proposed under a cloud-edge collaboration framework of the Internet of Things, which can solve the problem of insufficient data by integrating historical data from multiple distribution networks. Through the cloud-edge collaboration framework, all features from different distribution networks are first integrated to form a basic cloud convolutional neural network model for HIF detection. The features are extracted and updated by edge computers based on the accurate synchronous measurements provided by distribution-level phasor measurement units. To uniform the data scales of the different distribution networks, principal component analysis is adopted during feature extraction. Specific to each distribution network, the target HIF detection model is transferred from the basic cloud model by fine-tuning. Furthermore, a data augmentation method based on locality sensitive hashing is proposed to improve the performance of the transferred model. The proposed HIF detection method can be operated in both online and offline modes. The performance was verified by seven different distribution networks in numerical simulations and one practical experimental distribution network.

High Impedance Fault Detection Method Based on Variational Mode Decomposition and Teager–Kaiser Energy Operators for Distribution Network

The focus of the paper is the difficulty of high impedance fault (HIF) detection in distribution network, and its ease to be confused with capacitor switching (CS) and load switching (LS). Based on the intermittent reignition and extinction characteristics of HIF current, this paper proposes a novel HIF detection method, which combines variational mode decomposition (VMD) and Teager–Kaiser energy operators (TKEOs). The HIF detection method is as follows: First, perform the VMD on transient zero sequence currents to obtain the intrinsic mode functions (IMFs) and select the IMFs with the largest kurtosis value as the characteristic IMFs. Second, calculate the characteristic IMFs to obtain TKEOs and divide into subintervals of TKEOs waveform to calculate the time entropy values. Finally, construct HIF detection criterion as follows: when time entropy value is 0, it is judged as CS or LS. When the entropy value is not 0, it is judged as HIF. A large number of simulations and field data tests show that the method is accurate and stable, and under the interference of 1 dB strong noise, it can accurately judge. Compared with other methods, the method has higher feature extraction accuracy, less calculation time, and better judgment accuracy.

High impedance fault detection in distribution system

<p>High impedance faults (HIFs) present a huge complexity of identification in an electric power distribution network (EPDN) due to their characteristics. Further, the growth of non-linear load adds complexity in HIF detection. One primary challenge of power system engineers is to reliably detect and discriminate HIFs from normal distribution system load and other switching transient disturbances. In this study, a novel HIF detection method is proposed based on the simulation of an accurate model of an actual EPDN study with real data. The proposed method uses current signal alone and does not require voltage signal. Wavelet transform (WT) is used for signal decomposition to extract statistical features and classification of HIF into Non-HIF (NHIF) by Neural Networks (NNs). The simulation study of the proposed method provides good, consistent and powerful protection for HIF.</p>

A Feature Selection Method for High Impedance Fault Detection

High impedance fault (HIF) has been a challenging task to detect in distribution networks. On one hand, although several types of HIF models are available for HIF study, they are still not exhibiting 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 comparing with other traditional methods.

Fault detection in distribution networks in presence of distributed generations using a data mining–driven wavelet transform

Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

High impedance fault detection method based on improved complete ensemble empirical mode decomposition for DC distribution network

Aiming at DC distribution network, we proposed a novel high impedance fault detection method in the paper, it main procedures are as follows: Firstly, used the algorithm of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to extract the first intrinsic mode function (IMF) of the characteristic mode. Secondly, calculated the singular point of mutation and the cumulative slope by the acquisition of the first order difference operation, and then, achieved to distinguish the fault state and the normal state by the comparison between the slope and the starting threshold. Thirdly, identified the first IMF with Prony algorithm to obtain the parameters of characteristic frequency components (CFC) and direct current components (DC), and calculated the energy ratio between them, and then, distinguished small impedance fault (SIF), medium impedance fault (MIF), high impedance fault (HIF) and load switching (LS) by different values of energy ratio. A large number of experiments show that the proposed method is accurate and effective. Compared with other methods, this method shows its merits in feature extraction accuracy, detection accuracy and calculation speed.

High impedance fault detection method based on the short‐time Fourier transform

A method to detect high impedance faults (HIFs) based on low-order harmonics is proposed. Short-time Fourier transform (STFT) is used to extract the main harmonic components of phase current, as magnitude and phase of the third harmonic and magnitude of the second and fifth harmonics, which are used to identify HIF occurrence. In addition, this study presents an analysis of the window length and type used in STFT and its suitability for the application. The method proposed is able to detect HIF occurrence at various points of the test system, as well as HIF happening on different soil surfaces. It is also capable of distinguishing HIF events from similar distribution system disturbances such as capacitor banks switching and feeder energising. Furthermore, it is effective when real HIF oscillography is applied, indicating its tolerance to typical noises present on real signals.

Method for high-impedance fault detection

Correct detection of high-impedance faults (HIFs) is crucial because they produce a serious threat for humans, livestock and property. HIF currents, typically with fault resistances between 10 and 100 kΩ, cannot be detected with traditional protection functions and they behave randomly consisting of unstable and unpredictable dynamic fluctuations in the amplitude, harmonic levels etc. One main challenge is to reliably separate HIFs from normal network load and other switching events. In this study, new HIF detection method is proposed based on extensive power systems computer aided design (PSCAD) simulation studies and tests with real-life HIF measurements. The proposed method is applicable to different MV networks with different grounding practices. Depending on the grounding type, HIF detection is based on the use of zero sequence current I o or negative sequence current I 2. Method works in both 50 and 60 Hz networks and does not need voltage measurement. It also works as tripping protection function with sufficiently short operation time, is simple/flexible and has good usability from end user point of view.

High-impedance fault detection technology based on transient information in a resonant grounding system

High-impedance faults (HIFs) occur frequently in resonant grounding systems and are difficult to detect. Thus, an effective HIF feeder identification technology is urgently in demand. An HIF equivalent circuit in a resonant grounding system is established. The magnitude and phase relationships of transient zero-sequence currents among the faulty feeder, healthy feeders, detection points, and fault point, as well as the relationships between their projection coefficients with respect to the transient zero-sequence voltage are analysed. A novel HIF detection method based on the comparison of the magnitude and polarity of the transient zero-sequence current's projection coefficient is proposed. The accuracy of the proposed method is verified by simulations and field data.