Wind farm advanced modeling and control for smart-grid support

Abstract

As the share of wind power in the electric system is increasing, wind turbines must participate in primary frequency control (PFC). PFC is the act of a generator in the adjustment of the grid frequency either by increasing or decreasing active power production. The increase in the number of wind farm installations, and a decrease in conventional generators, reduces the inertia of the power system especially for islanded grids. This raises the need to modify the control loop of the wind turbine to behave like a conventional synchronous generator during frequency deviation. The main objective of this dissertation is to develop a comprehensive controller for a wind farm in order to support grid stability while optimizing the operation of individual wind turbine. It focuses on participation of the wind farm in primary frequency regulation in the power system. First, a centralized controller is designed for a wind farm to distribute the total reserved power among all wind turbines so that the line losses in the wind farm are reduced. The second contribution is based on designing an algorithm for every wind turbine to reduce the machine and conduction losses while maintaining the required reserve power using coordination control of the pitch angle and rotor speed. In addition, a cooperative dynamic droop controller is proposed to support grid frequency during disturbances. A centralized control scheme is proposed for a wind farm to maintain a certain power reserve of every individual wind turbine to be utilized during PFC. The controller is designed to minimize the line losses within the wind farm by optimizing the share of each individual wind turbine. Instead of using equal shares, each wind turbine is assigned a weighted power reserve based on its operating condition and its location on the feeder. The wake effect of the wind on the downstream wind turbines is taken into account. Then, a control algorithm is designed to maintain the power reserve of the wind turbine by controlling both pitch angle and rotor speed simultaneously to optimize the operation of the wind turbine system. The wind turbine system is based on a permanent magnet synchronous generator (PMSG). The algorithm uses the wind forecast to predict a consistent operating point of pitch angle. To ensure wind turbine participation in PFC, a dynamic droop controller based on fuzzy logic is developed to respond to frequency deviation. The controller is designed to identify the participation factor of each wind turbine based on its reserved power specified by the proposed de-loading approach. All algorithms proposed in this dissertation are incorporated to form a comprehensive control structure in order to efficiently achieve the wind farm’s required reserve. A small power system is developed to test the response of the wind farm to the frequency deviation.