Abstract: A method for evaluating the mechanical state of a high-voltage shunt reactor based on vibration characteristics is disclosed, relating to the technical field of electrical equipment fault diagnosis. The method includes: based on historical state data and real-time vibration and noise signal data of the high-voltage shunt reactor and through an LSTM neural network time series prediction method, comparing deviation between predicted characteristic value and actual characteristic value, and determining whether the high-voltage shunt reactor has mechanical defects or failures. By using the historical state data and the real-time vibration and noise signal data of the high-voltage shunt reactor, an LSTM neural network time series prediction method, as well as comparison of the deviation between the predicted characteristic value and the actual characteristic value, etc., the evaluation of the mechanical state of the high-voltage shunt reactor is realized.

Abstract: Disclosed is a discharge streamer simulation method considering offset and bifurcation. In the method, an insulating medium is arranged in an electric field environment, the number range of discharge streamer bifurcation points in the insulating medium and the number range of effective electron avalanches generated by each bifurcation are measured, a control equation and a boundary condition for streamer simulation are constructed based on a hydrodynamic drift diffusion model and a bipolar charge carrier model, model parameters corrected based on an electron avalanche development probability theory are introduced in the equation solving process, mathematical description and simulation calculation of a streamer offset and a bifurcation phenomenon are realized, and the electric field distribution and charge distribution accompanying the discharge streamer development process considering offset and bifurcation can be finally obtained.

Abstract: A method for evaluating the mechanical state of a high-voltage shunt reactor based on vibration characteristics is disclosed, relating to the technical field of electrical equipment fault diagnosis. The method includes: based on historical state data and real-time vibration and noise signal data of the high-voltage shunt reactor and through an LSTM neural network time series prediction method, comparing deviation between predicted characteristic value and actual characteristic value, and determining whether the high-voltage shunt reactor has mechanical defects or failures. By using the historical state data and the real-time vibration and noise signal data of the high-voltage shunt reactor, an LSTM neural network time series prediction method, as well as comparison of the deviation between the predicted characteristic value and the actual characteristic value, etc., the evaluation of the mechanical state of the high-voltage shunt reactor is realized.

Abstract: A frequency-sweep experimental method for predicting noise of a power capacitor, comprises steps of: (1) loading the capacitor with a sine voltage excitation with a loading frequency value of ½ to 50 times of a power frequency in sequence, at an increase of half of the power frequency for each time, and measuring vibration velocity at various points on a case of the capacitor; (2) under each loading frequency, dividing vibration velocity by a square of each voltage applied; (3) calculating frequency spectrum of a square of the voltage of the capacitor according to the voltage and a current of the capacitor; (4) multiplying the frequency spectrum of the square of the voltage of the capacitor by the electromechanical vibration frequency response function value of the capacitor to obtain a vibration velocity spectrum of the case of the capacitor; (5) calculating acoustical power of the noise.

Abstract: A frequency-sweep experimental method for predicting noise of a power capacitor, comprises steps of: (1) loading the capacitor with a sine voltage excitation with a loading frequency value of ½ to 50 times of a power frequency in sequence, at an increase of half of the power frequency for each time, and measuring vibration velocity at various points on a case of the capacitor; (2) under each loading frequency, dividing vibration velocity by a square of each voltage applied to obtain an electromechanical vibration frequency response function value corresponding to a double frequency which has a double value of each loading frequency mentioned above; (3) calculating frequency spectrum of a square of the voltage of the capacitor according to the voltage and a current of the capacitor; (4) multiplying the frequency spectrum of the square of the voltage of the capacitor by the electromechanical vibration frequency response function value of the capacitor to obtain a vibration velocity spectrum of the case of the c