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Transmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

Fault diagnosis is essential for high energy systems such as liquid rocket engines (LREs) due to harsh thermal and mechanical working environment. In this study, a novel method based on one-dimension Convolutional Neural Network (1D-CNN) and interpretable bidirectional Long Short-term Memory (LSTM) is proposed for intelligent fault diagnosis of LREs. 1D-CNN is responsible for extracting sequential signals collected from multi sensors. Then the interpretable LSTM is developed to model the extracted features, which contributes to modeling the temporal information. The proposed method was executed for fault diagnosis using the simulated measurement data of the LRE mathematical model. The results demonstrate the proposed algorithm outperforms other methods in terms of accuracy of fault diagnosis. Through experimental verification, the method proposed in this paper was compared with CNN, 1DCNN-SVM and CNN-LSTM in terms of LRE startup transient fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (97.39%).

In this study, a practical method is proposed for determining the distance and the section of fault in power distribution system (PDS). In the suggested method, at first the possible fault points are determined using a novel impedance-based fault location method. Since the number of these points may be more than one, thus two methods are proposed for determining the real location of fault. In the first method, the measured and recorded samples of voltages at the beginning of feeder for actual fault are compared with the stored samples of voltages which are obtained from simulating of fault at the possible fault points. The one with highest matching is the real location of fault. In the second method, frequency spectrum (FS) of voltage is defined as a suitable criterion for this purpose. Therefore the real fault point is determined by comparing and matching the FS of voltages obtained from the simulated faults and the recorded voltage for actual fault. The performance of the proposed method is evaluated in a real feeder in distribution network of Iran considering different types of faults, fault resistances, fault inception angles, real instrument transformer models and X/R ratio changes of upstream PDS network. The obtained results show that the performance of the proposed method is quite satisfactory and its accuracy is very high.

This study investigates regional ground-motion characteristics in Yunnan Province using 1,065 recordings from 60 small-to-moderate earthquakes (3.0 ≤  M w ≤ 6.0) at 65 stations. A generalized inversion technique (GIT) using empirical reference site was applied to four tectonically distinct subregions (A–D). The inversion results reveal stress drops (Δ σ ) ranging from 0.3 to 7.34 MPa, with the highest averages in Regions C and D (2.21 MPa), followed by Region A (2.07 MPa) and Region B (1.16 MPa). Δ σ shows no clear dependence on moment magnitude ( M w ) or focal depth but correlates with surface heat flow. Reverse-faulting events exhibit a higher average Δ σ than strike-slip and normal-faulting events. Quality factor models for the four study areas (A–D) are 118.41 f 0.691 , 120.26 f 0.638 , 77.20 f 0.598 and 72.19 f 0.861 , respectively. The eastern regions (C and D) exhibit lower Q 0 and stronger S-wave attenuation, along with higher tectonic activity and lower surface heat flow. The predominant frequencies of average site amplification for GMX-A, GMX-B, GMX-C and GMX-D are 20.00 Hz, 10.83 Hz, 3.97 Hz, and 1.92 Hz, with corresponding amplification factors of 2.84, 4.31, 5.45, and 7.69. Finally, the inversion parameters were validated through stochastic finite-fault simulations of the 2009 M S 6.3 Yao’an mainshock.

Squirrel-cage induction motors are increasingly displaying a broken rotor bar fault, which represents both a technical problem and an economic problem. After confirming that the broken rotor bars do not affect the normal start-up and basic working performance of the squirrel-cage induction motor, this paper focuses on the loss and efficiency changes of the motor brought about by the broken rotor bar fault. Using finite element simulation and experimentation, various losses like stator copper loss, iron loss, rotor copper loss, mechanical loss and additional losses, total loss and efficiency are obtained. By combining price and cost factors, the cost-effective measures that can be taken after the occurrence of different degrees of broken bars are evaluated here to provide guidance for correctly handling this problem.

To solve the problem of high cost and low efficiency of measuring equipment in traditional distribution network fault location, a fault section location and line selection strategy combining dynamic binary particle swarm optimization (DBPSO) configuration and fuzzy C-means (FCM) clustering is proposed in this paper. Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. When a fault occurs in the distribution network, the sensor device based on optimal configuration collects fault feature data, combines it with the FCM clustering algorithm to classify nodes according to fault feature similarity, and divides the most significant fault-affected section as the core fault area. Further, by calculating the Euclidean distance between each node in the fault section and the cluster center, the fault line is accurately identified. Finally, a fault simulation model based on an IEEE 11-node system is constructed to verify the effectiveness of the proposed method. The results show that, compared with the traditional fault section location and route selection strategy, this method can reduce the number of measurement devices optimally configured by 19–36% and significantly reduce the number of algorithm iterations. In addition, it can realize rapid fault location and precise line screening at a low equipment cost under multiple fault types and different fault locations, which significantly improves fault location accuracy while reducing economic investment.

In order to reveal the migration and evolution of gas molecules in the actual oil–paper insulation composite system of transformer from the molecular level, the diffusion behavior of seven gas molecules (H2, CO, CO2, CH4, C2H2, C2H4, C2H6) generated during the operation and aging of oil-immersed transformers in the oil–paper composite insulation system is studied by molecular dynamics. Firstly, based on the molecular dynamics software, the model of the oil–paper composite insulation system and the gas molecule model is constructed. In order to compare and analyze the diffusion properties of gas molecules in a single medium, a single model of insulating oil and cellulose is also constructed. Then, the diffusion coefficients of gas molecules in the insulating oil, cellulose, and oil–paper insulating composite system are simulated and calculated. And the differences in the diffusion properties of gas molecules in the three insulating mediums are discussed. Finally, the microscopic mechanism of diffusion of different gas molecules in the three mediums is analyzed. The simulation results show that among the three mediums, the diffusion coefficient of H2 is the largest, while the diffusion coefficients of the other gas molecules are not very different. The diffusion coefficients of the seven gas molecules are the smallest in the oil-immersed paper composite insulation system, followed by cellulose, while the diffusion coefficients are the largest in mineral oil. It indicates that the diffusion of gas molecules is inhibited in oil–paper insulation systems where the insulating paper is completely immersed in oil. This is mainly due to the fact that the insulating oil completely penetrates into the paper, filling the pores and voids between the fibers, resulting in a reduction in the transition vacancies of the intermediate gas molecules, which hinders the diffusion of the gas molecules.

It is extremely dangerous for a turbocharged boiler to have a leakage fault in its vaporization tube. However, early detection and fault diagnosis of micro-leakage faults are very difficult. On the one hand, there are few fault samples that lead to a difficult and intelligent diagnosis. On the other hand, the system fault response characteristics of the characterization parameters in the process are complex and easily confused with the load-changing characteristics. In order to obtain fault samples and identify fault characteristics, a fault simulation model for the micro-leakage of the boiler evaporation tube is established based on the dynamic mathematical model of all working conditions. The model’s effectiveness is verified by typical fault experiments. The dynamic simulation experiments of three kinds of micro-leakage and four kinds of load changing were carried out. Through the analysis of combustion equilibrium and vapor-liquid equilibrium of 14 groups of characterization parameters, it is found that: (1) The reason for the poor discriminability in micro-leakage faults is that most of the characterization parameters tend to balance after 300 s and the dynamic response characteristics are similar to those of load increase. (2) There are four highly distinguishable parameters: the speed of the turbocharger unit, the air supply flow, the flue gas temperature at the superheater outlet, and the furnace pressure. When the micro-leakage fault is triggered, the first three parameters have a large disturbance. They show a trend of decreasing first and then increasing in short periods, unlike normal load-changing conditions. The fourth parameter (furnace pressure) rises abnormally fast after failure. (3) Under the normal working condition of varying loads, the main common parameters take 300 s to stabilize; the common stability parameter values should be recorded because when the micro-leakage fault of evaporation occurs, the steady-state increment of failure is larger than the normal steady increment under variable load conditions, by 2 to 3 times. (4) As the leakage fault increases, the disturbance amplitude of the characteristic parameters becomes larger. In addition, the stability of the steam system becomes worse, and fault discrimination becomes more obvious.

This article brings out a Fault-tolerant BLDC motor drive for aerospace applications using the redundancy concept. In a way, it brings out a fault-tolerant strategy that can be used to continue the regular operation of a BLDC motor drive even after the occurrence of faults. As BLDC motors are used in critical and dangerous control areas like military services and space vehicles, a fault-tolerant drive is essential to maintain drive operation and provide desirable output. This article compares fault simulation results in the software model of a BLDC motor drive to those of fault simulation results in hardware for three main types of faults. Fault simulation is carried out for three types of faults, viz. inverter device open circuit fault, motor winding open circuit fault, and rotor position sensor (hall sensor) open-circuited fault. Fault tolerance is ensured by introducing a redundant drive (drive-2), which operates the complete drive at the advent of any of the faults mentioned above in the main (healthy) drive-1. A fault-tolerant (redundant) hardware comprising dual stator BLDC motor and redundant controllers is realized and operationalized. Fault simulation is carried out in this hardware, and these results are validated with the results of fault simulation in the MATLAB SIMULINK model. Software and hardware results are comparable and form a basis for developing fault-tolerant electro-mechanical actuation systems for high-reliability, high-cost applications, mainly aerospace.

This study addresses the problem of efficient fault simulation and test generation in circuits using multi-output combinational logic cells. A symbolic fault simulation algorithm is proposed to exploit bit-level parallelism in order to represent the propagation of the output value of faulty cells throughout the circuit, thus accounting for different faulty behaviours in a single simulation step. A satisfiability (SAT)-based test generation procedure is also provided and it early discovers sets of undetectable behaviours. Results for a set of combinational benchmarks show the feasibility of the proposed approach.