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: The method includes: determining required virtual inertia based on a frequency change rate monitored at a grid connection point of a photovoltaic grid-connected system; obtaining values of indexes that affect virtual inertia provided by each virtual synchronous generator (VSG) control unit in a photovoltaic cluster of the photovoltaic grid-connected system; determining, by using a predetermined function equation, a function value corresponding to each index; and determining an allocation proportion of the required virtual inertia in each VSG control unit based on the function value corresponding to each index. A problem of difficult conversion between a plurality of kinds of performance during operation of a power system is resolved, the frequency change rate is suppressed to a certain extent, and a stable state is recovered quickly.

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.