A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large‐scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re‐dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large‐scale wind farms. Copyright © 2012 John Wiley & Sons, Ltd.

No abstract

Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs.

No abstract

Tian, X. and Zhao, R., 2015. Energy network flow model and optimization based on energy hub for big harbor industrial park. 0749-0208.To model and optimize the energy network flow for the energy conservation and emissions reduction in big harbor industrial park by analyzing the characteristics of harbor energy system, this paper presents a universal framework for the modeling of energy systems comprising multiple-energy carriers, such as electricity, heat, gas, etc. The modeling framework is based on the energy hub concept model and the idea of "energy flow network". Then the system optimization to minimize energy consumption with multi-agent distributed control is put forward in this paper, which is executed with parallel processing method. After that, this paper takes Lingang industrial park in Shanghai as an engineering example to verify the whole energy model and optimization method proposed. In conclusion, the proposed approach can realize rational utilization of multiple-energy carriers for reducing energy consumption and carbon emissions, which can enhance the regulation level of energy center and make the energy system of the harbor run efficiently and orderly, that is to say, this paper can provide a technical support for the construction of resource saving and environment friendly harbor. ADDITIONAL INDEX WORDS:Network flow model, energy hub, optimization, multiple-energy carriers, harbor industrial park. _________________________________________________________________________________________