When large-scale wind power is connected to the power grid, the fluctuation and uncertainty in the wind power reduce the stability and accuracy of the grid's reactive voltage division results based on the electrical distance matrix and affect the grid's reactive power regulation. This paper proposes a grid reactive voltage partitioning method that considers the wind power stability and accuracy in a comprehensive manner. The wind power uncertainty and zoning results are characterized by the distribution of wind power forecast error intervals and changes in the zoning result nodes at different moments when the wind power is connected. Regarding volatility, according to the discretization of the probability distribution of the active power output at a certain time based on the wind power prediction, a calculation interval of the wind power output under a single cross-section is formed, and multiple sequential power flow sections within a long time scale are clustered and partitioned by an agglomeration hierarchical clustering method. Finally, an optimal zoning model of reactive voltage is established over a long time scale with the minimum comprehensive stability serving as the objective function. A simulation analysis of the improved IEEE39 node system shows that the partition combination can effectively increase the stability and accuracy of the reactive partitioning.
When a large amount of wind power is connected to the power grid, certain control methods need to be taken for wind turbines (WTs) so that WTs can respond to system frequency changes and maintain system frequency stability. Based on the frequency control method of a single doubly fed induction generator, a deloading power coordinated distribution method for frequency regulation by wind farms considering wind speed differences is proposed to better utilize the wind farm frequency regulation ability. A reasonable distribution of the deloading power of a wind farm for participation in grid frequency regulation to better utilize the wind farm frequency regulation ability. A quantitative calculation formula for the wind speed, total deloading power and single WT deloading power is established by using a variable parameter deloading power coordinated distribution coefficient based on the wind speed. The deloading power is distributed according to the wind speed of the WT units to adapt to the wind speed distribution differences between WTs in cluster wind farms and fully utilize the wind farm frequency regulation ability. A typical doubly fed induction generator (DFIG) wind farm is used as a simulation case. The experimental results show that the control strategy can distribute the output of each WT, improve the frequency response of the wind farm and enhance the frequency stability of the system. INDEX TERMS Frequency regulation, wind farm, deloading power coordinated distribution, rotor kinetic energy control, deloading control.
Online fault diagnosis systems have recently been applied in power grids. However, the complex modeling and high-quality requirements for power grid fault diagnosis have restricted the wide application of online systems. This paper proposes an automatic method for mapping alarm information to a fault diagnosis model. First, we automatically extract the basic logic variables from the alarm information. These can be used in power grid fault diagnoses by employing a key character-matching algorithm. We then build the associative relationship between electrical devices and circuit breakers based on connection analysis. Finally, an associative matrix of all electrical devices is designed to express the cooperative relationship between different devices in a fault event based on the short-circuit power mark. Based on these modules, cause-effect events expressing the associated relationship between alarms are established according to the protection configuration principle and protective relay setting principle. Expressing the associated relationship between alarms according to the logical requirements of a fault diagnosis model enables the automatic mapping from alarm information to fault diagnosis model to be realized. The validity of the proposed method for online fault diagnosis is verified using a real fault case that occurred in a power grid.
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