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.

Li-ion Battery Failure Warning Methods for Energy-storage Systems

Energy-storage technologies based on lithium-ion batteries are advancing rapidly.However,the occurrence of thermal runaway in batteries under extreme operating conditions poses serious safety concerns and potentially leads to severe accidents.To address the detection and early warning of battery thermal runaway faults,this study conducted a comprehensive review of recent advances in lithium battery fault monitoring and early warning in energy-storage systems from various physical perspectives.The focus was electrical,thermal,acoustic,and mechanical aspects,which provide effective insights for energy-storage system safety enhancement.

Digital twin model of gas turbine and its application in warning of performance fault

The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.

Weak characteristic information extraction from early fault of wind turbine generator gearboxKeywords wind turbine generator gearbox, B-singular value decomposition, local mean decomposition, weak characteristic information extraction, early fault warning

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.