A power margin tracking control method and system for a multi-terminal high-voltage direct current converter station are provided. A power adjustment factor is introduced on the basis of a droop coefficient, to realize a self-adaptive regulation of a converter station operation mode to a real-time fluctuation of a wind and solar power. In this way, the system operation stability and the power fluctuation allocation capability in a grid-connected system are improved. Furthermore, a DC voltage deviation in the multi-terminal high-voltage direct current grid is reduced.

Even when a fault occurs on an electric power system, a command signal based on appropriate calculation is given to a distributed power supply. Provided is a control apparatus configured to control a plurality of distributed power supplies connected to an electric power system. The control apparatus comprises: a first calculation unit configured to calculate in advance reactive electric power to be output by each of the distributed power supplies in the event of a fault on the electric power system; and a command output unit configured to output, to each of the distributed power supplies, a command signal for causing each of the distributed power supplies to output the reactive electric power calculated in advance by the first calculation unit when it is detected that a fault has occurred on the electric power system.

The following relates generally to calculating estimated power outages during a natural disaster event. In this regard, some embodiments create a synthetic network of a power infrastructure of a geographic area by: determining a location of a power substation; determining a location of a customer; determining a location of a power line linking the power substation to the customer; and determining if the power line is overhead or underground. Some embodiments then use the created synthetic network to simulate an event to calculate the estimated power outages during the event.

The present disclosure provides systems and methods for integrating an energy control system with an electrical system having a utility meter connected to a utility grid, a photovoltaic (PV) system, an energy storage system, and a plurality of electrical loads. The systems and methods include determining a site condition of the electrical system, determining a type of backup configuration for the electrical system based on the determined site condition, and determining a location of at least one of a main circuit breaker, the PV system, a subpanel, and a site current transformer with respect to the energy control system based on the determined site condition and the determined type of backup configuration.

A micro grid system comprises a secondary energy source and a power controller. The secondary energy source is associated with the micro grid, and the secondary energy source is configured to generate first DC power signal. The power controller is in communication with the secondary energy source and an electric grid, and configured to receive first AC power signal from the electric grid and the first DC power signal from the secondary energy source and to output a second AC power signal to loads in communication with the power controller. The power controller comprises a frequency converter configured to change frequency of the second AC power signal, a processor, and a memory configured to store instructions that, when executed, cause the processor to control the frequency converter to change the frequency of the second AC power signal.

Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.

A method and apparatus for identifying a fault in an alternating current electrical grid is provided.

The disclosure discloses a commutation failure prediction method, device and storage medium based on energy accumulation features of inverter. The method includes the following steps: collecting instantaneous values of three-phase valve side current and calculating the derivatives of the three-phase valve side current according to the instantaneous values of three-phase valve side current; the derivative includes positive, negative and zero states; according to the derivatives of the three-phase valve side current, determining the locations of incoming valve and ongoing valve; based on the valve side current of the incoming valve and ongoing valve, calculating energy accumulation features of the 12-pulse inverter; predicting whether the commutation failure from the incoming valve to the ongoing valve will happen according to the states of the derivatives of the three-phase valve side current and the energy accumulation features of the 12-pulse inverter.

Exemplary methods and systems for building an electrical grid topology and detecting faults in an electrical grid are disclosed herein. In an exemplary embodiment, a method for building an electrical grid topology of an electrical grid comprising a plurality of grid elements, the method comprises sending, from a first signaling module of a plurality of signaling modules of the electrical grid, a mapping signal; receiving, at a second signaling module of the plurality of signaling modules, the mapping signal; and deriving, from the mapping signal, grid characteristics of the electrical grid; wherein the grid characteristics are derived from the mapping signal based on the influence that one or more of the plurality of grid elements has on the mapping signal.

A system and method for automated shutdown, disconnect, or power reduction of solar panels. A system of solar panels includes one or more master management units (MMUs) and one or more local management units (LMUs). The MMUs are in communication with the LMUs with the MMUs and LMUs “handshaking” when the system is in operation. The MMUs are connected to one or more controllers which in turn are connected to emergency detection sensors. Upon a sensor detection of an emergency, the associated MMU is notified which in turn instructs associated LMUs to take appropriate action. In the event that communication with the MMUs has been cut off, the LMUs take the initiative to shut down, disconnect, or reduce the output of associated string(s) of solar panels.