3results about How to "Improve voltage regulation" patented technology

The application provides a DC energy storage system anti-sampling drift current sharing control method and system, comprising the following steps: S1: based on a DC network type energy storage system containing multiple energy storage converters, an equivalent circuit model of the multiple parallel energy storage converters is constructed; S2: based on an active voltage disturbance method, load impedance of the DC network type energy storage system is estimated according to the equivalent circuit model of the multiple parallel energy storage converters; S3: each energy storage converter respectively calculates a current reference value of DC network type energy storage system current sharing according to a local estimated value of the load impedance, and respectively calculates a line voltage drop value that needs to be compensated based on the current reference value; and S4: based on the calculated line voltage drop value that needs to be compensated, the influence of the line impedance on the DC bus voltage is offset.

The invention relates to a hydropower station bus voltage regulation control method based on big data self-learning, and the method comprises the steps: calculating the average value of the voltage regulation coefficients of a hydropower station in the same period in the previous year, and the average value of the voltage regulation coefficients in the current time period; searching a bus voltage regulation control process in a similar control scene from the big data, and calculating to obtain an average value of voltage regulation coefficients of the hydropower station in the same type of control scene; calculating to obtain a voltage regulation coefficient for real-time regulation and control of the bus voltage of the hydropower station; calculating a reactive power target value by using an automatic voltage control system of the hydropower station according to the obtained voltage regulation coefficient; and distributing the reactive power target value to each starting-up unit through an automatic voltage control system of the hydropower station, and carrying out closed-loop control adjustment on the bus voltage. According to the method, a big data analysis method and an AI model are combined, the accurate voltage regulation coefficient is obtained through real-time calculation, the bus voltage can be quickly regulated to the target value under the current working condition, and the response speed of the bus voltage is effectively improved.

This application discloses an active distribution network optimization method and system that considers energy storage degradation and deep reinforcement learning in the field of active distribution network operation optimization technology. The optimization method first constructs an active distribution network model including a photovoltaic inverter and an energy storage system, and sets its active and reactive power regulation capabilities. Then, it introduces the state of charge, calendar aging, and cyclic aging of the energy storage system to establish a degradation model reflecting battery life loss. Based on this, the voltage and reactive power optimization process is represented as a reinforcement learning decision structure, achieving joint regulation of the photovoltaic inverter and energy storage system by setting states, actions, and rewards. The SAC algorithm is used to train the control strategy, forming a regulator that can be used for online operation. Finally, control commands are generated based on real-time measurements to achieve voltage stability and degradation suppression in the active distribution network. This application solves the technical problem of accelerated degradation of energy storage in traditional regulation methods, achieving a technical effect that balances voltage control objectives and energy storage life management.