Circuit Fault Research Articles

A novel fault diagnosis method for battery energy storage station based on differential current

Nowadays, an increasing number of battery energy storage station (BESS) is constructed to support the power grid with high penetration of renewable energy sources. However, many accidents occurred in BESSs threaten the development of the BESS, so it is important to develop a protection method for the BESS. In this work, a novel fault diagnosis method based on differential current is proposed, which can identify the short circuit fault rapidly and effectively. Firstly, in order to simulate the short circuit fault characteristic of a BESS, a linear varying parameter battery equivalent circuit model (ECM) which can demonstrate the short circuit current are established based on the manta ray foraging optimization (MRFO) algorithm. Secondly, the fault diagnosis method based on differential current is proposed and analyzed through the calculation of short circuit current (SCC) in BESS. Finally, different working state data of battery are used to verify the fault diagnosis method. The results show that the proposed method can effectively diagnose the short circuit fault. In summary, the proposed fault diagnosis method based on differential current is efficient in protecting the BESS.

Study on short circuit effect of silicon carbide power devices

SiC can provide better material properties when the performance of Si - based power devices is almost developed to the limit. But compared with Si material, the reliability of SiC device is poor under ultimate stress. The short circuit capability of SiC MOSFET has been the main area of attention in this papers study. Through the analysis of SiC MOSFET short-circuit capability, it is mainly represented by the time that the device can withstand short-circuit stress and the time it takes for the device to be safely turned off in the event of a short circuit fault. Secondly, the detection circuit and protection circuit under the SiC MOSFET short-circuit fault are briefly studied, and the optimization ideas and working principles are summarized.

Enhancing the Dynamic Stability of Integrated Offshore Wind Farms and Photovoltaic Farms Using STATCOM with Intelligent Damping Controllers

To build a large-scale renewable energy integrated system in the power system, power fluctuation mitigation and damping measures must be implemented during grid connection. PID damping controllers and traditional intelligent controllers with pole configuration are usually used for improving damping. Integration of large wind power plants and photovoltaic power plants into the power system faces transient power oscillation and fault ride-through (FRT) capability under fault conditions. Therefore, this paper proposes a static synchronous compensator (STATCOM) damper based on a recurrent Petri fuzzy probabilistic neural network (RPFPNN) to improve the transient stability of the power system when large offshore wind farms and photovoltaic power plants are integrated into the power system, suppress power fluctuation, and increase FRT capability. To verify the effectiveness of the proposed control scheme, a three-phase short circuit fault at the connected busbar is modeled in the time domain as part of a nonlinear model. From the comparison of simulation results, the proposed control scheme can effectively slow down the transient fluctuation of power supply to the grid-connected point when the grid is faulty, reach steady-state stability within 1–1.5 s, and reduce overshoot by more than 50%. It can also provide system voltage support at an 80% voltage drop and assist in stabilizing the system voltage to increase FRT capability. It also improves stability more than PID controllers when disturbances are present. Therefore, it maximizes the stability and safety of the power grid system.

Diagnostic Method for Short Circuit Faults at the Generator End of Ship Power Systems Based on MWDN and Deep-Gated RNN-FCN

Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit faults at the generator terminal in the ship’s power system to ensure the safe and stable operation of modern ships. In this study, a generator terminal short-circuit fault diagnosis method is proposed based on a hybrid model that combines the Multi-Level Wavelet Decomposition Network, Deep-Gated Recurrent Neural Network, and Fully Convolutional Network. Firstly, the Multi-Level Wavelet Decomposition Network is used to decompose and denoise the collected electrical signals, thus dividing them into sub-signals and extracting their time-domain and frequency-domain features. Secondly, synthetic oversampling based on Gaussian random variables is employed to address the problem of imbalance between normal data and fault data, resulting in a balanced dataset. Finally, the dataset is fed into the hybrid model of the Deep-Gated Recurrent Neural Network and Fully Convolutional Network for feature extraction and classification of faults, ultimately outputting the fault diagnosis results. To validate the performance of the proposed method, simulations and comparative analysis with other algorithms are conducted on the fault diagnosis method. The proposed algorithm’s accuracy reaches 96.82%, precision reaches 97.35%, and the area under curve reaches 0.85, indicating accurate feature extraction and classification for identifying short-circuit faults at the generator terminals.

Analysis on Rotor Vibration Characteristics under Dynamic Rotor Interturn Short Circuit Fault in Synchronous Generators

A dynamic rotor interturn short circuit fault may occur during synchronous generator operation. However, there is no method to analyze the generator’s characteristics under the dynamic rotor interturn short circuit fault and identify the fault. In order to solve this problem, a new dynamic rotor interturn short circuit fault analysis model is creatively proposed through mathematical derivation. Firstly, the rotor vibration characteristics and the mechanical response under magnetic pull excitation are theoretically analyzed. In addition, a finite element modeling method for the dynamic rotor interturn short circuit fault is proposed, and an experimental device is designed. The model of a synchronous generator under the dynamic rotor interturn short circuit fault is established to obtain the rotor’s unbalanced magnetic pulls and mechanical responses by finite element calculation. Finally, the rotor vibrations are tested on a CS-5 prototype generator. The validity of the model is verified by finite element calculation and an experiment. This paper provides theoretical support for the diagnosis of a synchronous generator’s dynamic rotor interturn short circuit fault.

Real-time mode of operation data analysis to catch the thread-tip denotes the failure cause of the grid-tie PV central inverter

The inverter is considered the core of the PV power plant. The inverter’s failure leads to generation loss and decreases plant availability. So, it is required to investigate a clear Root Cause Analysis (RCA) to deduce the failure causes and implement the required corrective action in addition to the preventive action to avoid more inverter failure, hereby maintaining the plant available to a certain value. This paper discusses real-time mode operation data analysis of the PV grid-connected inverter due to real central inverter incidents in Benban solar park located in Egypt.The central inverter plays an important role in the Mega-Scale PV power plant. The main function of this inverter is to convert the DC power produced by the PV modules to AC power to be injected into the utility grid by considering specific characteristics based on the grid code. The availability of any PV power plant directly depends on the healthy inverter’s operation. The more increases for the installed inverters, the less availability loss in the case of inverter partial or catastrophic failures. So, it is required to focus on the failure causes of the central inverter by implementing a technical analysis using the available operational data. The monitored data of the central inverter in the PV power plant is classified into two types. The first type is the continuous time data stored in the memory. It represents the waveforms of inverter outputs like voltage, current, frequency, …. etc. Unfortunately, in case of a catastrophic failure, the central inverter is completely charred, and the continuous time data is lost due to storage memory damage. The second type is the operation data that is recorded by the SCADA system (per one-minute interval). Hereby, the operation data is the sole data in the case of the completely charred inverter. The representation of the operational data in curves indicates symptoms that can be used for the RCA processes. The investigation outcomes include three results. The first result is detecting the signature of the IGBT thermal stress on the voltage balance of the DC link capacitor. The second result is verifying a scenario for the cause of the IGBT failure by implementing a technical mathematical model based on the detected symptoms that denote the fault signature which is considered the thread-tip for detecting the failure cause. The third result is the simulating scenario for the interpretation of a DC link capacitors explosion due to the short circuit fault that occurred due to IGBT failure. The investigation in this paper is performed based on operation data analysis of the PV grid-connected inverter (central type) due to a real incident. The analysis methodology is based on mathematical calculation for the IGBT junction temperature using the measured heatsink temperature. The study concludes that after the IGBT failure occurred, it was a short circuit for a while and closed the terminals of the DC link capacitors. So, the DC link capacitors exploded and produced heavy sparks that led to enough fire to burn the inverter container completely.

Inverter open circuit fault diagnosis based on residual performance evaluation

AbstractBecause of uncertain random noise and unknown disturbance, the observer‐based inverter fault diagnosis meets the problems of large deviation of current observation value and high false alarm rate. An inverter open‐circuit fault diagnosis method is proposed via residual performance evaluation. Firstly, the interval sliding mode observer is constructed by combining the interval observer with the sliding mode observer. Three phase current estimates are obtained and current residuals are calculated. Secondly, the residual performance indicators are calculated using the evaluation function. The fault detection is derived by comparing residual performance with the threshold. Thirdly, a residual information table is established based on the fault information contained in the residual for fault isolation. Finally, the proposed algorithm is validated on simulation and physical platforms. Compared with traditional observer‐based diagnostic methods, the introduction of residual performance evaluation improves the diagnostic speed by more than 50%. The interval sliding mode observer has higher estimation accuracy and anti‐interference ability in the presence of noise and external disturbances.

PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach.

Research on Cable Head Monitoring Technology for Transmission Lines

Buried cables are located in harsh environments with high corrosion, uneven soil acidity and alkalinity, and groundwater immersion, resulting in complex structures. Especially for long-distance 10 kV transmission cables, the path planning is relatively long and cannot be connected at once, requiring a connection at the middle end of the cable. The production of cable joints is generally carried out in the foundation pit or well, with complex processes and manual production throughout the entire process, which cannot avoid manual technical deviations. Therefore, cable line faults mostly occur in the intermediate joints. Due to the uneven distribution of the temperature field caused by the accumulation of the electric field, the heat is transferred outward from the peeling chamfer point, resulting in uneven heating. In addition to the skin effect and proximity effect of current, the accumulation of electric field at the chamfer process point of the main insulation and outer semiconducting layer is intensified, and then the insulation is gradually broken down, causing a short circuit or ground fault. This article first studies the temperature and electric field distribution at the cable joint, and considers installing a small temperature sensor at the middle joint of the cable. The sensor is powered by a small electromagnetic energy coil, which can monitor abnormal temperature and electric field changes at the joint in real-time.

Research on the Vibration Characteristics of Pumped Storage Unit Stator Core Based on Fiber Optic Sensing

Stator core vibration (SCV) response of pumped storage units (PSU) can effectively reflect its operation status and provide reference for excitation winding inter-turn short circuit (ITSC) fault diagnosis. Considering that traditional piezoelectric acceleration sensors (PAS) are difficult to be installed on the stator core surface, this study proposes an online monitoring method based on fiber optic vibration sensing (FOVS). Firstly, this study derives the SCV response equations for the normal and ITSC states of the excitation winding, and obtains the vibration characteristic harmonics corresponding to each state. Secondly, a three-dimensional finite element simulation model of PSU with a generating capacity of 278 MVA is built, and the acceleration amplitude and characteristic frequency harmonics of the stator core are calculated to provide a reference for fiber optic vibration detection. Thirdly, this study focuses on the principle of modulation and demodulation of optical phase, builds an optical frequency-domain reflectometer (OFDR) - based FOVS system, and calibrates its sensitivity, amplitude and frequency characteristics as well as the lower detection limit. Finally, FOVS probes are installed on the surface of synchronous generator stator core to capture the vibration signals under different degrees of ITSC, and compared with the collected signals of PAS. The experimental results prove that the proposed method has obvious advantages in vibration monitoring in the low frequency band below 100 Hz.

Enhancing EV charger resilience with reinforcement learning aided control

This study aims to improve the performance of sustainable electric vehicle chargers in the face of unpblackictable/unpreventable disturbances. Over the past few years, Dual Active Bridge (DAB) DC-DC Converters are procuring substantial recognition for electric vehicle charging applications due to their superior characteristics such as higher power density, bidirectional mode of operation, and higher efficiency. Unexpected disturbances and fault scenarios at both source and load sides can deteriorate DAB converters’ performance. In this study, the performance of a single-phase shifted DAB converter is enhanced to achieve desiblack output current under several disturbance conditions for electric vehicle (EV) charging applications. A Reinforcement Learning (RL) based Deep Deterministic Policy Gradient (DDPG) algorithm is deployed to proactively tune control parameters when the DAB undergoes certain unexpected disturbances including short circuit faults at the source and battery sides. Results show that the RL-tuned PI controller improves the rate of current overshoot significantly compablack with the manually-tuned PI controller. The method and results are validated through simulations in MATLAB/Simulink environment.

Transient stability enhancement in renewable energy integrated multi-microgrids: A comprehensive and critical analysis

Multi-microgrids offer various benefits including the decreased overloading of a single microgrid, more options during faulty conditions, and more utilization of renewable energy resources. However, the implementation of a multi-microgrid brings the challenges such as power system complexity, interconnection issues, bidirectional power flow management, and power flow balancing. In the presence of these challenges, the power flow stability of the multi-microgrids is a challenging problem. In this context, this study evaluates a transient stability analysis model in multi-microgrids using solar photovoltaics, wind power, and a unified power flow controller (UPFC). UPFC offers a more robust power flow control strategy compared with other flexible alternating current transmission systems (FACTS) devices. First, a multi-microgrid structure consisting of the two microgrids was designed in DIgSILENT PowerFactory software. Second, the load flow calculation was performed in the absence and presence of UPFC, short circuit fault, and grid connection. Third, the electromagnetic transients (EMT) simulation was performed for all these situations. The results exhibited that the UPFC would offer significant power flow stability in the multi-microgrids. It was observed that the UPFC resulted in more transient stability in the microgrid where it was located. However, it improved the power flow quality at all the locations in the multi-microgrids. In addition, UPFC offered significant transient stability during the fault occurrence. The results offer various insights into power flow management in multi-microgrids.

Fault-Tolerant Architectures With Enhanced Bus Protection for Electric/Hybrid Aircraft Systems

This paper introduces two electrical architectures for electric/ hybrid-electric aircraft propulsion systems to address the issues of the radial baseline architecture, where a single bus feeds the four propulsion motors. A fault on this bus results in complete isolation of the bus to which propulsion motors are connected. By using the proposed architectures, the fault can be isolated without having to disconnect all the propulsion motors. This would increase the reliability, redundancy, and robustness of the electrical system, and/or avoid oversizing the components present in the architecture. In this paper, the protection strategy and action of circuit breakers are discussed for different fault conditions. The enhanced fault-tolerant operation of the proposed architectures over the existing radial baseline architecture for the electrified aircraft propulsion (EAP) system is validated for both open circuit and short circuit faults at different locations in the system, using controller hardware in the loop results obtained using typhoon HIL testbed. Furthermore, the feasibility analysis of interconnecting the individual propulsion channels to reduce the over-sizing of components is also discussed.

Monitoring Method for Off-grid Capacity of Direct Drive Wind Farm

A direct drive wind farm (DDWF) is composed of multitudinous permanent magnet synchronous generators (PMSG), with a small single-unit capacity. When the power grid mal-functions, some units in the DDWF may enter a fault traversal state, and some units may be disconnected from the grid. At this time, the DDWF exhibits a decrease in active output. Due to the large number of units in DDWFs, it is difficult to directly monitor each off-grid unit, resulting in the inability of the power grid to timely and accurately determine the load shedding amount. This article proposes a monitoring method for the disconnection capacity of DDWFs, which can calculate the disconnection capacity of DDWFs after a fault occurs by only monitoring the electric quantity at the connection point of the DDWF. And a DDWF grid connection model is built using PSCAD simulation software. The reliability of the monitoring is authenticated through simulation using a single-phase grounding short circuit fault as an example.

Voltage Correlation-Based Principal Component Analysis Method for Short Circuit Fault Diagnosis of Series Battery Pack

This paper concerns the issue of data-driven fault diagnosis for series lithium-ion (Li-ion) battery pack. A voltage correlation based statistical analysis method is proposed. First, the voltage of each cell within the battery pack is measured independently, and the correlation coefficient (CC) between the voltages of adjacent cells is calculated. Then, all the CC signals under normal conditions are used to train a principal component analysis (PCA) model, on the basis of which, a synthetic statistic and a kernel density estimation (KDE)-based threshold are designed for simultaneously monitoring all the measured CC signals at each sampling instant. Once a fault is detected, an accumulative relative contribution plot (ARCP) algorithm is immediately used to isolate which CC signal has a problem and locate the faulty cell through a cross-positioning strategy. The experimental results on a realistic test platform for series battery pack show that the new method provides accurate and reliable assessments for different fault specifics, and it performs better than the state-of-the-art CC methods in terms of parallel processing capability, sensitivity to weak short-circuit faults and robustness to window width.

Analysis of Inter-Turn Short Circuit Faults in Dual Three-Phase PMSM for Electromechanical Actuator

Dual three-phase permanent magnet synchronous machine (DTPM) is an attractive choice for electromechanical actuator owing to its advantages of double redundancy, high power density and high reliability. However, the winding insulation is susceptible to the high thermal and mechanical stress, which will lead to the inter-turn short circuit faults (ITSCF) and poses threat to the safety of the machine system. Moreover, the ITSCF of DTPM is not only limited to single-phase, but also occurs between adjacent phases. Therefore, in this paper, the characteristics of single-phase and phase-to-phase ITSCFs are investigated and compared while the short-circuit contact resistance is considered. Firstly, a general mathematical model for single-phase and phase-to-phase ITSCFs are established, and the expressions of short circuit current (SCC) and output torque are derived. Then, the characteristics of flux density, back-EMF, SCC, fault phase current and output torque of the different fault models are compared by finite element analysis. It is demonstrated that when the contact resistance is not zero ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_f</i> ≠0), the amplitude of SCC and the third harmonic content of fault phase current are inversely proportional to the contact resistance and proportional to the number of short-circuit turns. In addition, the amplitude of SCC, torque ripple and braking torque caused by a phase-to-phase ITSCF will be larger than those of the single-phase ITSCF under the same fault conditions, especially when the contact resistance is small. Finally, an experimental prototype is made and tested to validate these analyses.

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Fault Diagnosis in Analog Circuits Using Swarm Intelligence.

Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all potential component faults. Consequently, diagnosing faults in analog circuits requires three key aspects: identifying faulty components, determining faulty element values, and considering circuit tolerance constraints. To tackle this problem, a methodology is proposed and implemented for fault diagnosis using swarm intelligence. The investigated optimization techniques are Particle Swarm Optimization (PSO) and the Bat Algorithm (BA). In this methodology, the nonlinear equations of the tested circuit are employed to calculate its parameters. The primary objective is to identify the specific circuit component that could potentially exhibit the fault by comparing the responses obtained from the actual circuit and the responses obtained through the optimization process. Two circuits are used as case studies to evaluate the performance of the proposed methodologies: the Tow-Thomas Biquad filter (case study 1) and the Butterworth filter (case study 2). The proposed methodologies are able to identify or at least reduce the number of possible faulty components. Four main performance metrics are extracted: accuracy, precision, sensitivity, and specificity. The BA technique demonstrates superior performance by utilizing the maximum combination of accessible nodes in the tested circuit, with an average accuracy of 95.5%, while PSO achieved only 93.9%. Additionally, the BA technique outperforms in terms of execution time, with an average time reduction of 7.95% reduction for the faultless circuit and an 8.12% reduction for the faulty cases. Compared to the machine-learning-based approach, using BA with the proposed methodology achieves similar accuracy rates but does not require any datasets nor any time-demanding training to proceed with circuit diagnostic.

Circuit Integrity Assessment of Fire-Resistant Cables

In the electrical industry, fire accidents may happen due to overheating of electrical equipment, short circuit faults and any external source of fire. Polymeric Electrical Cables are prone to spread fire along the cable run as the insulation and sheathing materials are of flammable nature. Hence various flame retardants and low-smoke polymers are being used so that the flame spread and toxic gas release can be controlled. There are some special application cables called Fire Resistant cables which need to maintain the circuit integrity of the cable even during fire conditions. The construction of these fire-resistant cables requires fire-resistant insulation to achieve the fire-resistance property. They are such as Glass- Mica tape, Silicone Rubber, mineral insulation, metal insulation and fire retardant coating. There is various International and National standard assessing methods to assess these special cables. In this paper, the fire-resistant insulating materials and their performance during the assessment in achieving the required fire-resistant property are discussed.

SHORT CIRCUIT FAULT ANALYSIS IN THE ELECTRIC POWER DISTRIBUTION SYSTEM AT PT PLN (Persero) UP3 SOUTH MAKASSAR ULP KALEBAJENG USING ETAP

Short circuit fault analysis is needed to determine the amount of short circuit current flowing in a system. This study discusses the single-phase short-circuit current to the ground at the point where the disturbance occurs from the base of the network to the end of the Malewang feeder network. The purpose of this study was to determine the amount of short circuit fault current in the Electric Power Distribution System at PT PLN (Persero) UP3 South Makassar ULP Kalebajeng. This study used the ETAP program and manual calculations. The results of the study found that the contribution of the largest single-phase short-circuit fault current to the ground in the Malewang feeder occurred on Buses located at the base of the network close to the Substation, namely Bus 1 (Exim-Rec Kalaserena) of 0.311 kA, while thesmallest Buscontribution occurred on Buses located at the end of the network far from the Substation, namely Buses 14 (Bilonga) of 0.292 kA. From the results of the study, it can be concluded that the farther the interference bus is from the Substation, the greater the channel impedance. The greater the impedance, the smaller the short-circuit current.