Short-circuit Fault Detection Research Articles

Fault detection method for VSC-HVDC System with new energy sources considering fault current limiter

Voltage source converter based high voltage direct current transmission (VSC-HVDC) is widely adopted in the power system with new energy sources, but with the problem of fast rise of fault current. Magnetic saturation fault current limiter (MSFCL) suppresses the rise of short-circuit current by increasing the inductance value, which has the advantages of high speed and no additional control. However, the change of inductance will affect the detection of VSC-HVDC system fault, may leading to missed or incorrect removal of the fault and causing harm. Based on this problem, this paper proposes a fault detection method used in VSC-HVDC system considering MSFCL, which has fast detection speed, high sensitivity and reliability. The proposed method is robust to short-circuit resistance variation and can locate faults external and internal. Firstly, we establish the short-circuit equivalent circuit of VSC-HVDC system. Then we establish the differential equation and solve it, to obtain the analytical expression of voltage across MSFCL. Furthermore, we proposed a short circuit fault detection method based on the rise rate of the voltage across MSFCL. Finally we establish a four port VSC-HVDC system simulation model to prove that the method is effective. The results show that compared with traditional methods, the proposed method has faster detection speed. The proposed method is independent of the power flow direction, can identify the short circuit fault with large short-circuit resistance of 500 Ω and can discriminate the faulty branch from the healthy ones accurately.

An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft internal short-circuit faults in lithium-ion battery packs is critical to ensuring the safe and reliable operation of electric vehicles. This article proposes a fault diagnosis method that can achieve the detection and assessment of soft internal short-circuit faults for lithium-ion battery packs. Specifically, based on the incremental capacity curve, fault features are extracted from the data, making them easier to identify than small voltage differences. Then, the local outlier factor method is proposed to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack. Furthermore, soft short-circuit simulations of a series-connected battery pack under different conditions and various short-circuit resistance values are conducted to generate an internal short-circuit fault data set. Finally, the validity of the proposed fault diagnosis method is verified using simulation and real-world vehicle data. The results show that the proposed method can effectively identify the short-circuit fault of the battery at the early stage, accurately locate the faulty cells in the battery pack, and describe the severity of the fault.

Short-circuit fault detection scheme for DC microgrids on offshore platforms

Short-circuit fault detection scheme for DC microgrids on offshore platforms

Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform

Abstract This paper presents the development of a new algorithm for the diagnosis of induction motor inter-turn short circuit and mixed combined fault detection based on the reduction Park’s vector modulus and discrete wavelet transform. The proposed method is based on a combination between the RPVM approach and DWT. This new signal of the RPVM is a cost-effective alternative implementation of the Extended Park’s Vector approach through monitoring only the fluctuations of the stator current zero-crossing times in the frequency domain. Using this technique, the characteristic frequency components of the fault are extracted from only one phase stator current when compared to Park’s vector modulus and Hilbert modulus. The main advantages of the RPVM signal are to acquire the values of a single-phase stator current to allow reducing the calculation complexity and for extracting features frequencies of the faults. Thus, the reduced amount of data samples must be stored and processed. This study investigates theoretically by using the proposed RPVM signal for performing to diagnose induction motor faults through the DWT method and processed via energy eigenvalue of energies. This proposed approach RPVM-DWT is validated experimentally to demonstrate its effectiveness in that the proposed algorithm reduces the computational cost and can be used in induction motor fault detections and identifications with different fault severity conditions.

Inter-turn short-circuit fault detection with high-frequency signal injection for inverter-fed PMSM systems

Inter-turn short-circuit fault detection with high-frequency signal injection for inverter-fed PMSM systems

A fault detection method of electric vehicle battery through Hausdorff distance and modified Z-score for real-world data

Conventional fault diagnosis methods are tough to detect early faults when the abnormal characteristics of the battery are not obvious. The main purpose of this manuscript is to propose an online fault detection method for lithium-ion battery pack based on the combination of Hausdorff distance and modified Z-score. It enables the detection and location of the internal short circuit fault of the battery pack by detecting the Hausdorff distance between the voltage curve of each cell and the median voltage curve in a moving window. The validity of the algorithm is further verified by utilizing real-world data and compared with threshold method, Pearson correlation coefficient method, and Shannon entropy weighting method. The results show that the proposed method not only possesses higher reliability than these above but also does well without any model. In addition, the proposed method owns good robustness to the battery system with poor consistency and can be applied online.

Protection of DC Microgrids Based on Complex Power During Faults in On/Off-Grid Scenarios

The DC microgrid is an effective platform for integration of renewable energy sources, energy storage systems, and smart electronic loads. However, the integration of distributed generators can result weak fault currents with change in its direction during fault conditions, which lead to failure of conventional over-current relays with poor coordination. The above scenario may exhibits relay mal-operation and force the DC microgrid into blackout mode which is extremely undesirable. Thus, this paper proposes an complex power concept (real and imaginary), which can be extracted using Fast Fourier Transform (FFT) to construct the effective protection schemes for rapid short-circuit fault detection and fault isolation in a DC microgrid. To achieve fault isolation, solid-state DC circuit breakers are used in conjunction with proposed real and imaginary power, which is extracted from the total FFT power signal (i.e. by using bus voltage and line current). Further, the relay trip threshold value for real and imaginary power is also determined under various pole-pole (P-P) and pole-ground (P-G) fault scenarios in the DC microgrid. The proposed protection scheme is validated on simple and modified IEEE 9-bus DC microgrids under various P-P and P-G fault scenarios during On/Off-Grid modes through MATLAB/Simulink software. The simulation results reveal that the proposed protection scheme based on real and imaginary power has accurately identified the fault in the simulated DC microgrids. Thus, the proposed complex power based fault identification approach can be quite effective for protection of DC microgrids during On/Off-Grid scenarios.

Fault Detection in Distribution Network with the Cauchy-M Estimate—RVFLN Method

Fault detection is an important issue in today’s distribution networks, the structure of which is becoming more complex. In this article, a data-based Cauchy distribution weighting M-estimate RVFLNs method is proposed for short-circuit fault detection in distribution networks. The proposed method detects short circuits based on current and voltage measurements. In addition, noises were added to the data to ensure the robustness of the method. The performance of the method was examined in the RTDS RTS simulator using the IEEE 33-bus-bar system model with the help of real-time simulations. The success rate of the proposed method is between 98% and 100% for low-impedance (0 ohm) short-circuit faults, depending on the fault type. The success rate of high-impedance (100 ohm) short-circuit faults, which are more difficult to detect, is between 80% and 92%, depending on the fault type.

Real-Time Sensing and Fault Diagnosis for Transmission Lines

Article Real-Time Sensing and Fault Diagnosis for Transmission Lines Fatemeh Mohammadi Shakiba 1, Milad Shojaee 1, S. Mohsen Azizi 1,2, and Mengchu Zhou 1,* 1 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark 07102, NJ, USA. 2 The school of Applied Engineering and Technology, New Jersey Institute of Technology, Newark 07102, NJ, USA. * Correspondence: mengchu.zhou@njit.edu Received: 12 October 2022 Accepted: 8 November 2022 Published: 22 December 2022 Abstract: Protection of high voltage transmission lines is one of the crucial problems in the power system engineering. Accurate and timely detection and identification of transmission line short circuit faults can considerably improve and simplify their recovery process and hence save the costs associated with the downtime of a power system. Hence, it is essential that a robust and reliable fault diagnosis system completes its operation within an acceptable time window after fault occurrence in the presence of uncertainties and disturbances in the system. The significant costs of mistakenly detected or undetected faults based on the conventional techniques motivate us to present a robust detection and identification system by using the convolutional neural networks. The robustness of this technique is analyzed for the variations of the phase difference between two connected buses, fault resistance, source inductance fluctuations, fault inception angle, local bus voltage fluctuations, and measurement noises. The time delay analysis is also conducted to indicate that the presented technique is able to detect, identify, and estimate the location of faults before tripping relays and circuit breakers disconnect a faulty region.

Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual three-phase permanent magnet synchronous motors (PMSMs) with complex winding structures, and this kind of incipient fault detection is more complicated. To solve this detection difficulty, an IITSC detection method for dual three-phase PMSMs is proposed based on a modified deep autoencoder (MDAE). This autoencoder (AE) adopts an improved distribution metric combined with the maximum mean discrepancy (MMD) and the maximum covariance discrepancy (MCD) to extract the fault feature from the common features, which can improve the feature difference between the normal state and the incipient fault state. Then, the permutation entropy of the extracted features is calculated to detect the IITSC faults. The results illustrate that this method can not only detect IITSC faults online effectively and robustly, but also reduce the false alarm rate of the fault detection for dual three-phase PMSMs.

Inter-turn fault detection of induction motors using a method based on spectrogram of motor currents

Steady-state signatures seem more reliable than incipient fault signatures for detection of inter-turn short circuit (ITSC) fault in induction motors (IMs). An efficient detection approach is proposed based on the deviation of the histogram relevant to the time–frequency plane’s image of the steady-state modal current signal from the standard normal distribution. First, the misleading frequency components like 3rd, 5th, and 7th harmonics are excluded using Kalman Filter (KF). Then, from the time–frequency spectrogram of the signal, the converted gray-level image and its histogram are obtained. A considerable deviation of the histogram distribution from a normal distribution is observed in the case of ITSC fault, used for the detection. A cumulative index including normalized skewness and kurtosis of the histogram is used as the criterion of the detection. Finally, a novel threshold setting method based on Otsu’s threshold principle is proposed, which efficiently distinct faulty conditions from healthy operations. The required data for assessment of the approach is gathered from different experiments carried out on a test bench, subjected to various fault percentages and different load levels. The results confirm the effectiveness of the proposed methodology.

An Inter-Turn Short-Circuits Fault Detection Strategy Considering Inverter Nonlinearity and Current Measurement Errors for Sensorless Control of SPMSM

Aiming at the misdiagnosis problem of the interturn short-circuit (ITSC) fault detection method caused by the inverter nonlinearity and the current measurement errors, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis residual voltage-based fault diagnostic strategy is proposed for sensorless control of the surface permanent magnet synchronous motor (SPMSM). The model of the three-phase wye-connected SPMSM with ITSC fault is established, and the expression of the d-axis residual voltage caused by the ITSC fault, the inverter nonlinearity, the current measurement errors and the motor parameter errors is derived. The proposed fault diagnostic strategy can eliminate the current relative gain coefficient error and locate the ITSC fault by using the eighth and second harmonics of the estimated d-axis residual voltage, respectively. The experimental results verify the effectiveness of the proposed fault diagnostic strategy.

Short and Open Circuit Fault Detection in On-Grid Photovoltaic Systems 1MWP Bangli Based on Current and Voltage Observation

Photovoltaic (PV) systems as clean and green electrical energy generators have increased sharply in the last 10 years. The installation of a PV system in an open area is one of the causes of frequent faults/damage to the PV system. Fault in the PV system causes a decrease in efficiency, weak reliability, and disruption of the continuity of the electrical power distribution, which in turn causes the low performance of the system. This research does on the on-grid PV system 1MWp Bangli which consists of 278 PV arrays and 5004 monocrystalline solar modules 200Wp, which started operation in 2013. This research aims to find open circuit and short circuit faults that occur in the PV system Bangli. The method used in this research is the current and voltage observation method. Current and output voltage measurements from the PV array are carried out as well as measurements of working module temperature and solar irradiance. To calculate the output PV array's current (RI) and voltage (RV) indicators; Calculation of the current (RIM) and voltage (RVM) indicators under fault-free conditions, and the short circuit (TIO) and open voltage (TVS) threshold are calculated. As a result, this study succeeded in determining short and open circuit faults that occur in PV systems on grid 1MWp Bangli.

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Wind turbines are increasingly expanding worldwide and Doubly-Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning-based method is proposed for incipient detection of inter-turn short-circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state-of-the-art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi-class classification problem and a Long Short-Term Memory network, which is more appropriate for time-series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well-known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady-state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning-based methods.

Residual-based Short-Circuit fault detection and isolation in LVDC microgrid

Designing protection for a DC microgrid is challenging due to its DC nature and heavily capacitor-dominated DC bus that induces high amplitude current spike during short-circuit faults. Moreover, for small-scale applications such as residential DC microgrids, low line-resistance cables increase the fault detection time constraint. Thus, this paper proposes a fast short-circuit fault detection and isolation method based on current residual computation for a standalone residential DC microgrid. The proposed method continuously compares the computed residual from sensed branch currents to their respective defined thresholds. These thresholds are predetermined by analyzing the microgrid operation in healthy mode, considering both steady-state and transient operations. The protection device isolating the faulty branch is then activated once the corresponding residual exceeds its threshold. The protection scheme is validated through a processor-in-the-Loop simulation approach, during which the power stage of the system remains simulated on the host computer using PSIM software, while its control algorithm is executed from a Texas Instrument TMS320F28335 hardware target. The proposed protection algorithm is able to discriminate transient perturbations due to load connection from short-circuit faults. Furthermore, the algorithm implementation is not limited by the size of the DC microgrid nor by the type of the connected loads.

A Temperature and Magnetic Field-Based Approach for Stator Inter-Turn Fault Detection

Effective fault detection techniques are needed to reduce the maintenance and downtime costs of induction motors. This article presents an approach for stator end-winding inter-turn short-circuit fault detection using a metal-coated fiber Bragg grating (FBG) sensor with 0.5-mm diameter and 10-mm length to monitor the temperature and magnetic field around the end winding. The sensing theory, design, manufacturing, and installation of the sensor are detailed in this article. The nickel coating on the surface of the sensor is formed by electroless plating and electroplating, which makes the sensor sensitive to both temperature and magnetic field. The feasibility of the presented approach has been supported by both simulation and experimental results. The temperature and magnetic field signals are decoupled by filtering, and the experimental data are analyzed in the time-frequency domain. The results show that the inter-turn short-circuit fault can be detected at an incipient stage with 5% shorted turns.

Inter-Turn Short-Circuit Faults Detection and Monitoring of Induction Machines Using WPT-Fuzzy Logic Approach Based on Online Condition

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed.

Short-Circuit Fault Detection and Isolation Using Filter Capacitor Current Signature in Low-Voltage DC Microgrid Applications

This article describes a novel short-circuit (SC) fault detection approach to protect the low-voltage dc microgrid (LV-DCMG). The SC faults are the most common fault in the dc power system and can cause severe hazards if not isolated. Since a DCMG is a capacitor-dominated grid, the proposed scheme utilizes these filter capacitor current dynamics. An LV-DCMG system is considered for building the application to realize the proposed fault detection scheme. Active sources such as solar photovoltaic and battery are connected to the passive loads through a dc-dc converter, dc cable, and solid-state relays. The proposed method considers an average of the capacitor current as a detection parameter. An iteration-based isolation configuration is also suggested for a zonal type of distributed network. Furthermore, a selection procedure of the thresholds is also presented in detail. The validity of the proposed approach is verified through both digital simulation studies and DSP-based experimental studies. The proposed approach is capable of detecting both low and high impedance faults.

A Soft Short-Circuit Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft short-circuit (SC) faults in lithium-ion battery packs is critical to enhance electric vehicle safety and prevent catastrophic hazards. This article proposes a battery fault diagnosis method that achieves joint soft SC fault detection and estimation. Specifically, based on an augmented state-space battery model, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> nonlinear observer is constructed to estimate state of charge (SOC) and soft SC current in the presence of model parameter variations. Then, the asymptotic stability of the estimation error system under the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance is formally proved and a tractable observer design criterion is derived. Furthermore, a diagnosis algorithm is developed to detect soft SC faults via checking the difference between the estimated SOC from the observer and the calculated SOC from Coulomb counting. Once a soft SC fault is detected, the algorithm also allows the soft SC resistance to be calculated from the estimated soft SC current. Finally, soft SC experiments of a series-connected battery pack under different working conditions and various SC resistance values are conducted to verify the effectiveness of the proposed method.