The increase of wind power permeability in modern power grid has turned rapid and accurate transient stability (TS) prediction into a more challenging issue. To accurately and promptly perform online TS prediction for power system with doubly fed induction generator (DFIG)-based wind farms, an integrated model-driven and data-driven method is proposed in this paper. The influence of DFIGs is considered in the transformation to guarantee the accuracy of the equivalent one machine infinite bus (OMIB) model transformed from the target system. The P-δ trajectory of the OMIB is fitted with the generator-terminal information to predict TS. To improve the prediction speed, an extreme learning machine (ELM)-based method is utilized to process the other DFIG and system information and evaluate the system status immediately after failure. The simulation results verify that the proposed method can reduce the dependence of the data-driven method on the data sample size and improve the speed and accuracy of online prediction.INDEX TERMS Extreme learning machine (ELM), doubly fed induction generators (DFIG), transient stability, trajectory fitting.
As the proportion of new energy sources in the grid increases and the structure of the ultra-highvoltage grid becomes more complicated, the variability of the external environment and the complexity of the grid itself bring many new challenges to the power system reserve configuration problem. The multilevel and multitime scale characteristics of the grid reserve are analyzed in this paper, and the reserve is classified according to the response time characteristics combined with the system function. We establish a demandside response participation system reserve configuration optimization model, including synchronous generator generation and reserve models, demand-side scheduling and reserve models, grid power generation planning and reserve optimization mathematical models. Based on the idea of decomposition coordination, the current power generation plan and reserve optimization problem are decomposed into a finite fault state safety constraint unit combination subproblem, Monte Carlo secondary negative reserve constraint check subproblem, frequency safety constraint checking subproblem based on large-disturbance long-process time domain simulation analysis, and fault state static safety constraint check subproblem based on minimum load shedding. Considering the demand side response to the reserve configuration of the system, combined with the problem of previous unit association, the optimal allocation of the reserve coal consumption units is realized by determining the start and stop status of the unit and the unit output.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.