A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

Fault location method for petal-shaped distribution network with inverter-interfaced distributed generators

In this paper,a fault location method for the petal-shaped distribution network(PSDN)with inverter-interfaced distributed generators(IIDGs)is proposed to shorten the time of manual inspection.In order to calculate the fault position,the closed-loop structure of the PSDN is skillfully exploited,and the common control strategies of IIDGs are considered.For asymmetrical faults,a fault line identification formula based on the negative-sequence current phase differences is presented,and a fault location formula only utilizing the negative-sequence current amplitudes is derived to calculated the fault position.For symmetrical faults,the positive-sequence current at both ends of lines and the current output from IIDGs are used to identify the fault line,and the positive-sequence current on multiple lines are used to pinpoint the fault position.In this method,corresponding current phasors are separated into amplitudes and phases to satisfy the limitation of communication level.The simulation results show that the error is generally less than 1%,and the accuracy of the proposed method is not affected by the fault type,fault position,fault resistance,load current,and the IIDG penetration.

Fault Diagnosing System of Steam Generator for Nuclear Power Plant Based on Fuzzy Neural Networks

All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.

Experimental Analysis of A Cooling System for Wind-Driven Generator Stator

A novel cooling system with cooling channels is proposed for the stator of 3MW wind-driven generator.An experimental platform is built to investigate the performance of the cooling system with different loads.At30%,50% or 80% generator loads,the temperatures meet the design requirement.However,it is a little over the requirement at 100%load,duo to experimental errors and some unknown thermal resistances.In the test at 100%load,the developing trends of the parameters of these two generators are similar and only minor differences occurs when they reach steady state our work can be benefit for the design and improvement of MW wind-driven generator cooling solutions.

Fault Attack on the Balanced Shrinking Generator

Fault analysis, belonging to indirect attack, is a cryptanalysis technique for the physical implementation of cryptosystem. In this paper, we propose a fault attack on the Balanced Shrinking Generator. The results show that the attacker can obtain the secret key by analyzing faulty output sequences which is produced by changing control clock of one of Linear Feedback Shift Registers （LFSR）. Therefore, the balanced shrinking generator has a trouble in hardware implementation.

Study on Vibration Detection System of Wind-driven Generator based on Virtual Instrument

This study analyzes the structural characteristics of wind-driven generator,concludes its comment malfunctions and proposes effective methods by general fault analysis methods,so as to design online detection and fault diagnosis system of wind-driven generator in virtual instrument.This work will realize real-time detection,help engineers to proceed remote fault diagnosis,reduce maintenance time and increase production efficiency.This study is meaningful and practical to develop a fault diagnosis system for wind-driven generators,which shows professionalization of fault diagnosis system.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Improved design of online fault diagnoser for partially observed Petri nets with generalized mutual exclusion constraints

This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, an improved online fault diagnosis algorithm that integrates generalized mutual exclusion constraints (GMECs) and integer linear programming (ILP) is proposed. Assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. GMEC is used for elementary diagnosis of the system behavior, then the ILP problem of POPN is solved for further diagnosis. Finally, an example of a real DES to test the new fault diagnoser is analyzed. The proposed algorithm increases the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP is verified.

Molecular dynamics simulation on generalized stacking fault energies of FCC metals under preloading stress

Molecular dynamics(MD) simulations are performed to investigate the effects of stress on generalized stacking fault(GSF) energy of three fcc metals(Cu, Al, and Ni). The simulation model is deformed by uniaxial tension or compression in each of [111], [11-2], and [1-10] directions, respectively, before shifting the lattice to calculate the GSF curve. Simulation results show that the values of unstable stacking fault energy(γusf), stable stacking fault energy(γsf), and unstable twin fault energy(γutf) of the three elements can change with the preloaded tensile or compressive stress in different directions.The ratio of γsf/γusf, which is related to the energy barrier for full dislocation nucleation, and the ratio of γutf/γusf, which is related to the energy barrier for twinning formation are plotted each as a function of the preloading stress. The results of this study reveal that the stress state can change the energy barrier of defect nucleation in the crystal lattice, and thereby can play an important role in the deformation mechanism of nanocrystalline material.

First-principles study of the effects of selected interstitial atoms on the generalized stacking fault energies, strength, and ductility of Ni

We analyze the influences of interstitial atoms on the generalized stacking fault energy （GSFE）, strength, and ductility of Ni by first-principles calculations. Surface energies and GSFE curves are calculated for the （112） （111） and / 101） （ 1 1 1） systems. Because of the anisotropy of the single crystal, the addition of interstitials tends to promote the strength of Ni by slipping along the （10T） direction while facilitating plastic deformation by slipping along the （115） direction. There is a different impact on the mechanical behavior of Ni when the interstitials are located in the slip plane. The evaluation of the Rice criterion reveals that the addition of the interstitials H and O increases the brittleness in Ni and promotes the probability of cleavage fracture, while the addition of S and N tends to increase the ductility. Besides, P, H, and S have a negligible effect on the deformation tendency in Ni, while the tendency of partial dislocation is more prominent with the addition of N and O. The addition of interstitial atoms tends to increase the high-energy barrier γmax, thereby the second partial resulting from the dislocation tends to reside and move on to the next layer.

Application of General Fractal Dimension to Coupling Fault Diagnosis

This paper presents the coucept of general and sensitive dimension, and also proposes the calculation formula of the general dimension least squares method. By calculating and analyzing the power spectrum and general dimension from the fault sample, the relationship is achieved between sample status and the general dimension from vibration signals of the equipment so as to provide reference to fault diagnosis. Furthermore, a correlation function of general dimension is proposed, and calculations are carried out for a monitor signal and samples signal. The diagnosis method based on fractal theory is effective through the concrete examples of the steam electric generating set fault diagnosis, and the correlation coefficient of general dimension between a monitor signal and samples signal can improve the accuracy for fault diagnosis.

Overview and Analysis of PM Starter/Generator for Aircraft Electrical Power Systems

More Electrical Aircraft(MEA)which replaces the hydraulic and pneumatic power by electrical power leads to reducing emissions and fuel consumption.The MEA concept has led to a growing use of the starter/generator(S/G)system.Permanent magnet(PM)machines have been gaining interests for aircraft S/G system application over the last few years.This is mainly due to the several advantages,including high power density,high efficiency and high speed ability.The shortcoming of the PM machines is the de-­excitation problem in case of a failure,which is a main issue for the aircraft application.However,by using a PM machine with high reactance or multiphase configuration,the fault-­tolerant ability can be improved.In terms of the aircraft S/G system,this paper is going to present a comprehensive analysis of PM machines.Firstly,the state-­of-­the-­art of PM starter/generator(PMS/G)is summarized and the basic structure of PMS/G system is analyzed.Next,key technologies of the PMS/G system are summarized and analyzed.Finally,a flux weakening fault protection strategy that is used to suppress the turn-­to-­turn short circuit(SC)current is studied,simulated and verified.With the breakthrough of key technologies based on the development of high temperature electromagnetic material and high temperature power electronics,the PMS/G will be a potential candidate for aircraft S/G system including the embedded power generation system.

The generalized planar fault energy, ductility, and twinnability of Al and Al–RE( RE= Sc, Y, Dy, Tb, Nd) at different temperatures:A first-principles study

The genearlized planar fault energies of Al and Al-RE （RE = Sc, Y, Dy, Tb, Nd） alloys have been investigated using first-principles methods combined with a quasiharmonic approach. The stacking fault energies, unstable stacking fault energies, and unstable twinning energies decrease slightly with increasing temperature. The ductility parameter D, the relative barrier difference Sut, and the twinnability τa of Al and Al-RE alloys at different temperatures have been determined. It is found that the ductilities of Al and Al alloys are nearly the same and the ductilities increase slightly with increasing temperature. The RE alloying elements make twinning more likely and the twinnabilities of Al and Al alloys decrease with increasing temperature.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.

A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

Many kinds of electrical equipment are used in civil and building engineering.The motor is one of the main power components of this electrical equipment,which can provide stable power output.During the long-term use of motors,various motor faults may occur,which affects the normal use of electrical equipment and even causes accidents.It is significant to apply fault diagnosis for the motors at the construction site.Aiming at the problem that signal data of faulty motor lack diversity,this research designs a multi-layer perceptron Wasserstein generative adversarial network,which is used to enhance training data through distribution fusion.A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals.These are used to train themulti-layer perceptron Wasserstein generative adversarial model.Then,the trainedmodel is applied to generate fake current wavelet coefficients with the fused distribution.A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron.The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset.The classification model trained on a real dataset,fake dataset and combined dataset achieves 21.5%,87.2%,and 90.1%prediction accuracy on the unseen real data,respectively.The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution.The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset.

Transient Stability Analysis of Synchronous Generator in Power System with Renewable Power Sources Installed

The impact of large-scale grid-connected renewable power sources, such as wind generators and solar photovoitaic systems, on transient stability of synchronous generators is discussed in this paper. The permanent magnet synchronous generator with variable speed wind turbine is used in the simulation analysis as a wind generator model. The transient stability analysis is performed for IEEE 9-bus system model with high-penetration renewable power sources. The effect of FRT （fault ride-through） capability implemented for each power source on the transient stability is investigated.