Core Fault Research Articles

Optimization of support vector machine for transformer core and winding looseness fault diagnosis based on improved grey wolf algorithm

To diagnose transformer core and winding looseness faults more timely and effectively, a complementary ensemble empirical mode decomposition (CEEMD) and improved grey wolf optimization-based support vector machine (IGWO-SVM) method for transformer core and winding looseness fault diagnosis are proposed. Firstly, the vibration signal is decomposed into multiple intrinsic mode functions (IMFs) through CEEMD. Secondly, the energy entropy of different IMFs is calculated, and the energy entropy of different states is formed into a feature dataset. Then, the improved GWO-optimized SVM model is used to classify and recognize the feature dataset. Finally, we establish an experimental platform for experimental verification. The results show that the proposed method can accurately and effectively diagnose transformer core and winding looseness faults, and has a high diagnostic accuracy, which is at least 3.5% higher than the existing optimal diagnostic models. The proposed method provides a theoretical reference for the development of transformer fault diagnosis strategies.

Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

The role of transformers in power distribution is crucial, as their reliable operation is essential for maintaining the electrical grid’s stability. Single-phase transformers are highly versatile, making them suitable for various applications requiring precise voltage control and isolation. In this study, we investigated the fault diagnosis of a 1 kVA single-phase transformer core subjected to induced faults. Our diagnostic approach involved using a combination of advanced signal processing techniques, such as the fast Fourier transform (FFT) and Hilbert transform (HT), to analyze the current signals. Our analysis aimed to differentiate and characterize the unique signatures associated with each fault type, utilizing statistical feature selection based on the Pearson correlation and a machine learning classifier. Our results showed significant improvements in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, with 83.89% accuracy and a computational cost of 0.2963 s. For future studies, our focus will be on using deep learning models to improve the effectiveness of the proposed method.

A novel hybrid magnetic material based on three-phase saturated core fault current limiter

Installing a three-phase saturated core fault current limiter (TSFCL) is an effective method to limit the increasing short-circuit current in high-voltage alternating current (HVAC) systems. However, the traditional TSFCL uses silicon steels as its working material for the iron core, resulting in poor current-limiting effects, requiring a large DC excitation current, and having a slow response speed. Nanocrystalline alloys have advantages such as a lower saturation point, higher permeability in the unsaturated zone, and a steep B–H curve, making them highly suitable for TSFCL’s applications. However, their poor capability which withstands fault hindered their large-scale commercialization. Therefore, this paper proposes a novel hybrid magnetic material based on TSFCL (HMTFCL), where the materials of the working limbs are replaced by nanocrystalline alloys. Compared to TSFCL, the DC excitation current of HMTFCL decreases by 60.5%. And, the HMTFCL reduces the peak short-circuit current by 23%. Last, the magnetic flux intensity inside the working limb of HMTFCL is reduced to zero more quickly.

The Research of Novel Saturated Core Fault Current Limiter Considering the Short-Circuit Current Transient Component

The Research of Novel Saturated Core Fault Current Limiter Considering the Short-Circuit Current Transient Component

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Diagnosis of Loose Core Fault in Saturable Reactor of Thyristor Valve Based on Vibration Signal Time-Frequency Analysis and CNN

The saturable reactor is one of the core components of the thyristor valve, which plays a role in suppressing current changes and balancing voltage distribution. Due to long-term operation under complex working conditions, the vibration of the reactor can cause the core to loosen. To detect faults in time, a fault diagnosis method is proposed for loose cores in saturable reactors of thyristor valves based on time-frequency analysis of vibration signals and convolutional neural networks. The two-dimensional time-frequency spectrum of the vibration signal is obtained using the short-time Fourier transform method. A convolutional neural network model structure is designed to extract time-frequency spectrum features and diagnose different levels of core looseness faults through learning and training. An experiment was carried out to collect core fault information under variable working conditions. The effectiveness of the method proposed in this paper was verified based on experimental data validation. Through comparative experiments, it was found that the proposed method is superior to existing fault diagnosis methods.

A Compact Saturated Core Fault Current Limiter Magnetically Integrated with Decoupling Windings

Traditional saturated core fault current limiters (SCFCLs) have fast rising fault state inductance that can limit faults automatically. However, the current limiting performance of traditional SCFCLs is insufficient. In order to improve this, some novel SCFCLs use additional air core reactors, resulting in a large volume and floor area. A compact saturated core fault current limiter magnetically integrated decoupling windings (CSFCL) is proposed in this study, which has the advantages of a smaller volume and floor area. The principle of the non-orthogonal decoupling method is firstly introduced. The core body of the CSFCL is then magnetically integrated with the decoupling windings in the form of non-orthogonal decoupling. Equivalent magnetic circuit and electric circuit analysis is carried out. An inductance calculation method for decoupling windings is proposed. Following this, a 220 kV simulation model is conducted based on the finite element method. Finally, a small-capacity prototype of the CSFCL and a test platform are designed to test it. The experimental results verify the correctness and feasibility of the proposed structure.

Early Warning Method of Transformer Core Clamp Disconnection Based on Random Forest Algorithm

Transformer core fault is one of the common faults, and core fault monitoring and early warning is an important research topic. An early warning method of transformer core clamp disconnection based on a random forest algorithm is proposed. Firstly, the values of transformer core clamp parameters under different working conditions are measured, and the parameter-state data set is established. Secondly, the features are processed and reduced in dimension, and the parameters with high correlation are selected. Finally, a random forest classification model training parameter-state training set is established to identify the fault of the iron core clamp.

The Performance Analysis of PSO-ResNet for the Fault Diagnosis of Vibration Signals Based on the Pipeline Robot.

In the context of pipeline robots, the timely detection of faults is crucial in preventing safety incidents. In order to ensure the reliability and safety of the entire application process, robots' fault diagnosis techniques play a vital role. However, traditional diagnostic methods for motor drive end-bearing faults in pipeline robots are often ineffective when the operating conditions are variable. An efficient solution for fault diagnosis is the application of deep learning algorithms. This paper proposes a rolling bearing fault diagnosis method (PSO-ResNet) that combines a Particle Swarm Optimization algorithm (PSO) with a residual network. A number of vibration signal sensors are placed at different locations in the pipeline robot to obtain vibration signals from different parts. The input to the PSO-ResNet algorithm is a two-bit image obtained by continuous wavelet transform of the vibration signal. The accuracy of this fault diagnosis method is compared with different types of fault diagnosis algorithms, and the experimental analysis shows that PSO-ResNet has higher accuracy. The algorithm was also deployed on an Nvidia Jetson Nano and a Raspberry Pi 4B. Through comparative experimental analysis, the proposed fault diagnosis algorithm was chosen to be deployed on the Nvidia Jetson Nano and used as the core fault diagnosis control unit of the pipeline robot for practical scenarios. However, the PSO-ResNet model needs further improvement in terms of accuracy, which is the focus of future research work.

Optimization Design of a Saturated Iron Core Fault Current Limiter Using a GA and PSO Algorithms Coupled With Finite Element Method

A dc-bias high-temperature superconducting (HTS) coil is the main part of the Saturated Iron Core Fault Current Limiter (SIC-SFCL). Another relevant part of the SIC-SFCL is the iron core, which represents the main volume of the device. Therefore, optimizing the iron core volume decreases the equipment cost significantly. Also, it is important to find an optimized geometry for the dc HTS coil that allows extracting the maximum potential of the superconducting material. This article proposes a full design optimization of a SIC-SFCL, combining the GA and the particle swarm optimization (PSO) methods. For the optimization procedure, the authors consider the superconducting tape and the ferromagnetic material. The COMSOL software is used to implement the finite element model and the Python language coupled to the COMSOL software codes the optimization algorithms. The authors study different cognitive, social, and inertial parameters in PSO, in order to evaluate the impact of those parameters in the optimization. The results present the ability of the algorithm to provide adequate SIC-SFCL design parameters without any expert intervention. To validate the proposed methodology, the authors perform experimental tests and compare them with the simulation results. The steady-state regime presents an error less than 1% and the short-circuit regime shows a maximum error equal to 10.3%.

Self-Adaptive Air-path Health Management for a Heavy Duty-Diesel Engine

This paper presents the air-path health management strategy with the ability to estimate the mass-flows and mitigate (adapt to) the air-path faults in the exhaust system of a heavy-duty diesel combustion engine equipped with a twin-scroll turbine. Based on the engine component models applied in the quasi-steady-state mass-balancing approach, two main engine mass-flow quantities are estimated: the Air mass-flow (AMF) and the Exhaust gas recirculation (EGR) mass-flow. The health management system is monitoring for three kinds of air-path faults that can occur through the combustion engine operation, related either to the after-treatment system, EGR valve, or to the turbine balance valve hardware. For each fault, a fault-mitigation strategy based on in-observer-reconfigurable mass-balance equations with excluded faulty component model and utilized exhaust pressure sensor is proposed. The applied observer is using the iterated Kalman filter (IKF) as the core fault mitigating solver for the quasi-steady-state mass-balancing problem. It is further demonstrated how the individual faults are robustly isolated using the Sequential Probability Ratio Test (SPRT). The strategy and results are validated using the test cycle driving data.

An Enhanced Analysis of Inter-Laminar Faults in Magnetic Cores With Grain Oriented Electrical Steels for Fault Diagnosis and Condition Monitoring: Theoretical Background and Experimental Verification

Fault diagnosis and condition monitoring of electromagnetic devices are the strategies of monitoring and processing parameters of the device for fault identification and failure prediction. In this respect, core faults or inter-laminar faults (ILFs) is a key objective, with an important contribution into the whole process. This paper tends to propose a new experimental-analytical approach to fault diagnosis and condition monitoring of magnetic cores. The proposed technique is in line with the phenomenology of the magnetic hysteresis and interpreting dynamic characteristics of the magnetic cores. The distinct feature of this approach is its ability to model additional magnetic field and associated energy loss caused by ILFs. The studies were carried out on stacks of standard Epstein size strips of 3 wt % SiFe Grain Oriented (GO) steels, subjected to artificial faults. Validation of the developed approach was undertaken through experimental work and analytical modelling.

Hybrid Multifunctional Saturated-Core Fault Current Limiter

Saturated core fault current limiters are effective means to limit short-circuit currents in high voltage power systems. To solve its problems of a low rate of utilization and large loss, this study proposes a multifunctional saturated-core fault current limiter (MFFCL) with a hybrid excitation system. The MFFCL can limit the short-circuit current and control power flow to reduce losses. The composition and topological structure of the MFFCL are provided, an equivalent electromagnetic circuit model of the MFFCL is then established, and its working mechanisms of limiting current and regulating power flow are analyzed. Following this, a finite-element simulation model of the MFFCL is built to investigate its electromagnetic characteristics and verify its performance in terms of limiting current. The power flow regulation function of the MFFCL was verified through the field-circuit coupling method. Finally, a laboratory-scale prototype of the MFFCL and a test platform was designed to test it. The results of the experiments verified the correctness and feasibility of the proposed structure. It can adequately control power flow in its normal state, and can limit the short-circuit current to 60% of the original in case of a fault.

A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment

Maritime autonomous surface ships (MASS) are proposed as a future technology of the maritime industry. One of the key technologies for the development of MASS is condition-based maintenance (CBM) based on prognostics and health management (PHM). The CBM technology can be used for early detection of abnormalities based on the database and for a prediction of the fault occurring in the future. However, this technology has a problem that requires a high-quality database that reproduces the operation state of the actual ships and quantitatively and systematically indicates the characteristics for the various fault state of the device. To solve this problem, this paper presents a study on the development method of the fault database based on the reliability. Firstly, the reliability analysis of the target device was performed to select five types of the core fault modes. After that, a fault simulation scenario that defined the fault simulation test methodology was drawn. A land-based testbed was built for the fault simulation test. The fault simulation database was developed with a total of 109 sets through the fault simulation test. Additionally, a fault classification algorithm based on deep learning is proposed. The classification performance was evaluated with a confusion matrix. The developed database will be expected to serve as the basis for the development CBM technology of MASS in the future.

A Thermographic Method to Evaluate Different Processes and Assembly Effects on Magnetic Steels

Ferromagnetic materials may be affected by the presence of local losses due to defects or magnetic anomalies caused by machining processes. To highlight such anomalies is not easy; a noninvasive thermographic method has been refined to allow a proper comparison of different machining processes’ impact on the iron losses. Specimens obtained with punching, wire erosion, and laser cut have been analyzed using a high-speed IR camera when subjected to alternate magnetization at different frequencies. Also, the same technique has been adopted to the assembled stacks to investigate more simultaneous phenomena. The possibility to point out localized anomalies should be exploited to foresee and avoid electrical machines core faults.

Calibration analysis of gas relay

—Gas relay is the main protective element of transformer internal fault, which can sensitively respond to transformer short-circuit fault, core fault, internal turn to turn short-circuit of winding, insulation deterioration and oil level drop. Therefore, it is necessary to regularly compare and verify the gas relay. This paper briefly introduces the operation principle and calibration method of gas relay, analyzes in detail the reason why there is no specific standard for the calibration of gas relay setting value, and analyzes the factors affecting the flow rate setting value.

A 3-D Finite-Element Method Approach for Analyzing Different Short Circuit Types in a Saturated Iron Core Fault Current Limiter

In several cases, substations have become incapable of supporting faults due to increase in short-circuit levels. Fault current limiters (FCLs) are a potential solution for this problem. Among several FCL topologies presented in the literature, the saturated iron core superconducting fault current limiter (SIC-SFCL) has shown promising results in real tests in substations. In this context, this article presents the impacts of different kinds of short circuit on the SIC-SFCL device. To reproduce various types of fault, the SIC-SFCL is modeled in a 3-D framework considering both ferromagnetic and superconducting nonlinearities. Moreover, the proposed 3-D finite-element method model can couple the SIC-SFCL to the electric power system, also representing the electrical grid and the protection systems. For this investigation, the dc-bias superconducting coil’s normalized current density and the iron cores’ magnetic flux density are examined in light of different kinds of short circuit. The simulations are compared with measurements in a bench prototype, showing a maximum error of 16.5%. Furthermore, different types of short circuit are investigated and compared, which is possible only using a 3-D model. The highest limitation occurs in the phase-to-phase short-circuit case. In the dc circuit of the SIC-SFCL, the highest transient period happens in the single-phase short circuit. Finally, the phase-to-phase fault presents the highest dc current during the transient.

The Vibroacoustic Characteristics Analysis of Transformer Core Faults Based on Multi-Physical Field Coupling

Power transformers play an important role in the safe and reliable operation of the whole power grid. Once a fault occurs, it will endanger the normal operation of the transformer, and even result in power grid accidents. Accurate and practical methods of transformer fault monitoring and type identification have attracted extensive attention in the field of electrical engineering. However, it is difficult to obtain a large number of measurement data for different fault types on a large power transformer. The vibroacoustic characteristics of transformer faults have significant asymmetry. For the power transformer in service, it is complex and uneconomic to obtain the vibroacoustic signals under different fault conditions. To handle this problem, this paper proposes simulation methods of several common transformer core faults, based on multi-physical field coupling, and then analyzes the vibroacoustic signals generated by the operating transformer. Finally, it verifies the results of acoustic and vibration signals under several faults, through physical experiments. The results show that the transformer fault simulation method is reasonable and accurate. Furthermore, a change in the transformer core state will cause a change in the transformer vibroacoustic characteristics, and different types of core faults can be distinguished by the analysis of vibroacoustic characteristics.

Core looseness fault identification model based on Mel spectrogram-CNN

In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Single core configurations of saturated core fault current limiter performance of laboratory test models

Economic growth with industrialization and urbanization lead to an extensive increase in power demand. It forced the utilities to add power generating facilities to cause the necessary demand-generation balance. The bulk power generating stations, mostly interconnected, with the penetration of distributed generation result in an enormous rise in the fault level of power networks. It necessitates for electrical utilities to control the fault current so that the existing switchgear can continue its services without up-gradation or replacement for reliable supply. The deployment of fault current limiter (FCL) at the distribution and transmission networks has been under investigation as a potential solution to the problem. A saturated core fault current limiter (SCFCL) technology is a smart, scalable, efficient, reliable, and commercially viable option to manage fault levels in existing and future MV/HV supply systems. This paper presents the comparative performance analysis of two single-core SCFCL topologies impressed with different core saturations. It has demonstrated that the single AC winding configuration needs more bias power for affecting the same current limiting performance with an acceptable steady-state voltage drop contribution. The fault state impedance has a transient nature, and the optimum bias selection is a critical design parameter in realizing the SCFCL applications.