Internal Faults Research Articles

A pilot protection based on low frequency component model for HVDC transmission lines

AbstractReliable and fast identification of high‐voltage direct current (HVDC) transmission line faults is important to the safety of power system. In traditional DC transmission line protection, differential protection is invulnerable to high fault resistance, but it needs long time delay for the influence of distributed capacitance current. In order to solve this problem, a novel pilot protection is proposed. The characteristics of the voltage and current processed by low‐pass filter under different fault conditions are analysed, and based on it the protection criteria is proposed. When external fault occurs, it reflects the line capacitance where the first forward fault traveling wave passes, which is increasing until equal to the whole line capacitance, then remains constant. However, when internal fault occurs, its value is negative in the initial stage, and after that its value is very different from the whole line capacitance. The analysis proves that this pilot protection method has fast operation speed, and can be applied for a relatively long period of time. What is more, the applicable time of superposition principle is considered in the analysis, which is not considered in many literatures. An UHVDC system is modelled for simulation in PSCAD/EMTDC, and the correctness and effectiveness of the proposed scheme is verified.

Learning-Based Faulty State Estimation Using SOH-Coupled Model Under Internal Thermal Faults in Lithium-Ion Batteries

Estimating the state of charge (SOC), state of health (SOH), and core temperature under internal faults will significantly improve the battery management system’s (BMS’s) autonomy and accuracy in range prediction. This paper presents a neural network (NN) based state estimation scheme that can estimate the SOC, core temperature, and SOH under internal faults in lithium-ion batteries (LIBs). First, we propose a model-based internal fault detection scheme by employing a SOH-coupled electro-thermal-aging model (ETA) of the LIB. Then, a nonlinear observer is used to estimate the proposed SOH-coupled model’s healthy states for a residual generation. The fault diagnosis scheme compares the output voltage and surface temperature residuals against the designed adaptive threshold to detect thermal faults. The adaptive threshold effectively alleviates the false positives due to degradation and model uncertainties of the battery under no-fault conditions. Upon fault detection, we employ an additional NN-based observer in the second step to learn the faulty dynamics. A novel NN weight tuning algorithm is proposed using the measured voltage, surface temperature, and estimated healthy states. The convergence of the nonlinear and NN-based observer state estimation errors is proven using the Lyapunov theory. Finally, numerical simulation results are presented.

A pilot protection scheme based on high‐frequency transient current waveform similarity for AC lines connected to HVDC inverter station

AbstractAffected by inverter station commutation failure and fast control, line fault characteristics are obviously different from traditional AC transmission line fault characteristics. The existing protection methods for AC line connected to line commutated converter based high voltage DC (LCC‐HVDC) inverter station (ACLs) have the problems of complex setting, weak anti‐noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high‐frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high‐frequency current is extracted with the improved synchronous squeezed S‐transform (SST) method. Finally, a large number of power systems computer aided design (PSCAD) simulation results show that the proposed protection method does not require strict data synchronization, has strong anti‐noise performance, and is tolerant to the influence of abnormal point fluctuations.

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing

In recent years, internal short circuits have frequently been the cause of safety accidents, and traditional diagnosis methods for such problem have limitations that prevent their application under complex working conditions. Furthermore, due to the scarcity of real internal short circuit data, it is difficult to obtain a large amount of real vehicle data, which makes internal short circuit fault diagnosis even more challenging. In this paper, fault batteries with unknown parameters are diagnosed using a residual network based on multi-label processed battery data and transfer learning is utilized to optimize the diagnosis effect for fault localization and fault degree identification, thus increasing the information richness of the training model samples and improving the training effect. Accuracy, recall, and false positive rate are used to evaluate the diagnosis results of the residual network. The results demonstrate that the accuracy rate increased by 11.1% after transfer learning, indicating a significant reduction in misdiagnoses. The proposed fault diagnosis method using residual network and transfer learning has practical significance for enhancing battery fault detection accuracy and efficiency. Especially in areas such as electric vehicles and energy storage systems, it can provide effective technical support for battery health monitoring and early warning.

A Backup Protection Based on Compensated Voltage for Transmission Lines Connected to Wind Power Plants

Due to the influence of complex control, the short circuit current provided by wind power generation units will exhibit new characteristics, such as limited amplitude and controlled phase, affecting the performance of traditional protections. This paper analyzes the work principle and defects of one kind of multiphase compensated distance protection. Drawing on the idea of fault identification by multiphase compensated distance protection, novel pilot protection using the compensated voltage on both sides is proposed. The proposed method has a strong anti-fault resistance ability. The test results indicate that the proposed method can distinguish the internal faults and external faults reliably, unaffected by the new characteristics of short circuit current.

Transient pilot protection based on directional traveling wave principle for wind power transmission line

The short-circuit current from the new energy power generator presents special characteristics such as frequency shift, restricted amplitude, unequal positive and negative sequence impedance, high percentage of harmonic content, etc., resulting in the risk of misoperation or rejection of conventional protection. By analyzing the propagation characteristics of traveling wave in the transmission line after the fault, this paper reveals difference between traveling wave waveforms at both ends of the line under internal fault and external fault. The similarity between forward traveling wave and reverse traveling wave at both ends of line is higher when an external fault occurs, while the similarity between the forward traveling wave and reverse traveling wave is lower after internal fault. According to this difference, a pilot protection algorithm based on discrete Frechet distance is proposed, which can calculate similarity coefficient of forward traveling wave and reverse traveling wave at both ends of line to construct protection criteria, and then identify internal and external fault. Through the analysis of PSCAD/EMTDC and experiment platform of hardware-in-the-loop test verify that the proposed protection algorithm can accurately and reliably distinguish internal and external fault.

A novel methodology for transformer winding defects diagnosis based on its measured frequency response signal impedance morphology interpretation

This article proposes the characteristics study of the key parameters of power transformer winding (viz. the equivalent capacitance and the equivalent inductance) extracted from the frequency response analysis (FRA) of the winding. However, the FRA curve of the winding can be divided into three frequency ranges (low, medium, and high), where; the low and the high frequency response (LFR and HFR) ranges represent the inductive and capacitive effects of the winding respectively, which can establish best analysing and interpreting tool of the winding response behaviour to diagnose their failures. Furthermore, real axial deformations were carried out on a winding constructed for this study. In addition, the variation of the equivalent inductance and capacitance obtained from the impedance curve in the LFR and HFR ranges were analysed to locate and evaluate the faulty states with the reference state of the winding. Consequently, the proposed method provides a tool for locating and assessing the internal faults severity in power transformer windings.

Dynamic PMSM Motor Model with Turn Short Fault for Wind Power Generation

A PMSM (Permanent Magnet Synchronous Motor) stator inter turn short fault generates the back emf and impedance unbalance element. Those unbalance element cause the torque ripple and the escalating partial heat defect. This paper proposes a dynamic motor model under the internal turn short fault. The proposed model is derived in the positive and negative sequence synchronous reference frame to be easily utilized for the fault detection rule. To validate the proposed scheme, the finite elements method (FEM) simulation with electric circuit is conducted on the PMSM.

Time-normalized voltage gradient protection for fault detection in MMC-HVDC grids

Travelling wave fault detection is the mature and fastest protection scheme for HVDC grids. However, it faces the challenge of low resistance coverage. Unlike conventional voltage gradient-based travelling wave protections, this paper explores the new property of the initial fault travelling waves. It is found that gradient properties of initial fault travelling waves are affected by fault location as well as resistance but not in the same way as the fault severity. However, time to reach the maximum voltage gradient is only affected by fault location in case of internal faults. Based on these investigations, this paper proposes a new protection scheme termed as time-normalized voltage gradient protection. In this scheme, the locally available gradients of modal voltages are averaged out over the time to maximum gradient with proper power weightage to the timing factor. The proposed scheme can discriminate the internal and external faults in both forward as well as backward directions. At the same time, it is robust to fault resistance as high as 300 Ω, as fast as practically mature traditional gradient-based methods. The proposed protection is achievable at practically common sampling frequency and, therefore, simple for practical implementation. Validation studies on four terminal meshed MMC-HVDC grid simulated in PSCAD/EMTDC proves the proposed strategy’s effectiveness, adaptive rapidity and accuracy.

Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM

Abstract During the normal life of the transformers, they are subjected to different electromagnetic stresses. One of these stresses is the electromagnetic forces resulting from the passage of short-circuit current in the transformer coils due to internal or external faults and may lead to the failure of the transformer when this electromagnetic force exceeds the threshold level. This work deals with the computation and analysis of leakage magnetic flux and electromagnetic forces when the worst-case fault (symmetrical short-circuit current) occurs in distribution transformer (DT) coils using the finite element method (FEM). The three types of DTs that were adopted in this work are similar in capacity and voltage transformation ratio (250 kVA and 11,000/416 V), but they are different in the shape of coils (oval, cylindrical, and rectangular coils). ANSYS software was used to build two-dimensional models of the three transformers, which were different in coil shapes and in the type of iron core. The objective of this work is to compare the effect of coil shape on the distribution of electromagnetic forces and their value, in order to find out which coil shape is the best to withstand electromagnetic forces when short-circuit current is passed in the coils. The results of the simulation of the finite element models were approximately equivalent to the results of the design calculations that depend on the classical method (the analytical method), but the FEM is more accurate, due to the accuracy of calculating the magnetic flux and its distribution, which cannot be calculated using the classical method. One of the most important contributions of this research is the analysis and calculation of electromagnetic forces for three types of DTs with different coils by applying the same design parameters to all the transformers. The research also contributed to determining the best coil shape by comparing simulation results, and it was found that transformers with cylindrical coils are the best to withstand electromagnetic forces due to the homogeneity of the cylindrical coil structure from all sides.

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

AbstractPower transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle.

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.

Modelling and Analysis of Electromechanical Stress in Transformers Caused by Short-Circuits

A common reason for internal faults in transformers windings is the weakness insulation caused by vibration / deformation related to electromechanical forces produced by high short circuit currents. This phenomenon significantly reduces the transformer life expectancy and may even lead to its instantaneous or timing destruction. Focusing this subject this paper is aimed at presenting the performance of a time domain model inserted in a finite element program i.e. the 3D software package and the investigation of the relationship between high current levels occurring at transformer windings and the internal mechanical stresses. To highlight the overall model and the software performance, a laboratory 15 kVA transformer is utilized to show the programme facilities and potentially. The transformer has been built with concentric double-layer windings and ferromagnetic core with three columns and this equipment has been submitted to a balanced three-phase short- circuit. In addition, the computational simulations were carried out using distinct geometries to the windings i.e. with and without deformation.

Alternative methods for internal arc tests on 12 kV and 24 kV metal-enclosed switchgears with compact RMU

Arcing due to an internal arc fault test is carrying out in order to demonstrate the capability of the metal-enclosed switchgear to withstand to stresses generate by electric arc. Firstly, it is aimed the limitation of the destructive effects of the enclosures and protection of the persons near the metal-enclosed switchgear. The behaviour of the metal-enclosed switchgear during the test is pointed out by video recordings, which lead to the evaluation of the test result together with the observations stated after test on metal-enclosed switchgear and indicators also. The paper presents two alternative methods for verification at electric arc action of metal-enclosed switchgears according to international standards.

Qualitative and quantitative diagnosis of internal hydrogen leakage in fuel cell based on air flowrate control and open-circuit voltage measurements

Internal hydrogen leakage faults in fuel cells can cause severe performance loss and safety risks. The aim of this study is to achieve qualitative and quantitative diagnoses of internal leakage in fuel cell based on air flowrate control and transient voltage measurements. The hydrogen crossover characteristics of a fault-free and defective fuel cell are both investigated, and the effectiveness of the diagnosis method is evaluated by comparing and analyzing the results before and after the leakage fault introduction. The results suggest that hydrogen leakage failure inside fuel cell is dominated by convection processes. In airflow interruption tests, where the fuel cell is operated under open-circuit conditions with given anode overpressures, the fall time of the cell voltage is used as a qualitative measure of internal hydrogen leakage, and fault occurrence can be recognized by a considerable decrease in this indicator. Through cyclic staircase flowrate sweep tests, we obtain good estimates of internal leakage rates in the defective fuel cell. These diagnostic methods can be performed directly under hydrogen/air conditions, and there is no need for additional power source equipment dedicated to generating excitation signals for fuel cell, giving them great potential in on-board applications.

ROCOV scheme for Fault Detection and Location in HVDC systems

A reliable DC fault protection system is essential for the development of HVDC grids. Therefore, this paper deals with the voltage derivative ROCOV scheme to locate and detect DC faults. The algorithm is able to differentiate internal and external faults considerably fast. The proposed algorithm is analyzed in a HVDC grid with different fault case scenarios. Finally, the ROCOV protection thresholds are discussed.

Machine learning for power transformer SFRA based fault detection

This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer. Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime. The presented machine learning methods can be used to improve health assessment of power transformers.

Reinforcement learning robust nonlinear control of a microgrid with hybrid energy storage systems

The parameter variations in the microgrid models including internal and external faults, modelling uncertainties, unexpected changes in load or disturbances and harmonic currents are unavoidable in a microgrid. Here, a hybrid energy generation system consisting of Proton-Exchange Membrane Fuel Cells)PEMFC(/PhotoVoltiaic)PV (/Battery Energy Storage System)BESS(is considered. A new Backstepping Fractional order Adaptive Terminal Sliding Mode Control (BFATSMC) based on Reinforcement Learning (RL) is designed to soar robustness and disturbance rejection for an autonomous microgrid. To verify the performance of the BFATSMC a comparative study is investigated and the proposed method is applied to an islanded microgrid. In the presence of nonlinear and unbalanced loads with harmonic, unmodeled dynamics, and parametric uncertainties, the proposed method has successfully controlled the voltage.

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

The Indirect Symmetrical Phase Shift Transformer (ISPST) stands out from a power transformer due to its combination of electrically connected and magnetically coupled circuits. Hence in this work, an intelligent differential protection algorithm, based on Discrete Wavelet Transform (DWT) and Chebyshev Neural Network (ChNN), is proposed as main classifier to discriminate internal fault and inrush. Half cycle thee phase post fault differential current is considered for the proposed algorithm. PSCAD/EMTDC software is utilized to simulate different operating conditions of ISPST, resulting in the simulation of a significant amount of internal faults and inrush cases. The algorithm under consideration has undergone extensive evaluation across numerous cases, resulting in an accuracy rate exceeding 99%. The results indicate that the classifier based on DWT - ChNN yields extremely promising results, even when dealing with a noisy signal, current transformer (CT) saturation, and varying ISPST ratings. Superiority of the proposed algorithm is also compared with Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) and Probabilistic Neural Network (PNN) based approaches under the same conditions and it is found that proposed classifier is the most efficient and rapid among all alternative classifiers for the differential protection scheme of an ISPST under the considered conditions.