This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.

Microgrids, as individual controllable entities that can operate either islanded from or interconnected to main power grid, have emerged as a promising solution to improving energy efficiency and resilience to disturbance. When linked together in a self-organized manner, a cluster of microgrids can significantly enhance the reliability and power quality for critical load. With this insight, we study the self-organization and decentralized energy management of a microgrid cluster islanded from main grid after a disruptive event. In the self-organization stage, depending on the available generation resources, each microgrid decides on whether to connect to the cluster; and the microgrid energy management systems then "negotiate" on the optimal power exchange with each other in the cluster. Once the power exchange is determined, the generation and storage resources of each microgrid are managed to guarantee the energy reliability of critical loads and overall energy efficiency, through a scheduling procedure followed by a dispatch procedure. The effectiveness of the proposed method is revealed via case studies.

Microgrid combines with Distributed Energy Resources (DERs), energy storage, and controllable loads. It is connected to the maingrid through the point of common coupling (PCC). Economic dispatch using reduced gradient method is implemented for the optimization of energy in the microgrid using MATLAB. The optimization is obtained by minimizing the cost function of the system while meeting the load demand. The operation & maintenance cost, and investment costs are considered in the cost functions of the microsources. The minimum total cost of the system is obtained by comparing different scenarios of the microsources in the microgrid.

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This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach.