Control Strategies for Wind Energy Systems and Motor Drives with Efficient Electric Machines

风能系统和高效电机电机驱动的控制策略

• 批准号: RGPIN-2014-04898
• 负责人: Uddin, Mohammad
• 金额: $ 2.26万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588087
• 关键词: Control; Strategies; Wind; Energy; Systems

As a sustainable and environmentally friendly energy source, recently the popularity of wind energy has experienced significant growth. According to World Wind Energy Association, worldwide wind energy capacity has reached 273 GW (gigawatts) by 2012 and is yet exponentially increasing. According to Canadian Wind Energy Association, Canada is now the ninth largest producer of wind energy in the world with current installed capacity at 6.5 GW. Ontario is expected to install more than 5,600 MW of new wind energy capacity by 2018, creating 80,000 person-years of employment, attracting $16.4 billion of private investments. The wind energy conversion system (WECS) involves wind turbine, generator, and digitally controlled power converter system, which are complex, nonlinear and are subject to parameter uncertainties and unknown disturbances. Considering the tremendous growth of wind energy, some challenging technical issues such as maximum power transfer from wind to the grid line and loss minimization of electric machines, and converters incorporating system uncertainties yet to be solved. The loss minimization in electric machines and control techniques can be equally applied for both wind generators and motor drives. Electric motors consume more than 50% of the total electrical energy produced in the world. Therefore, for efficient utilization of limited energy sources it is highly desirable to control the electric motors with high efficiency and high dynamic performance. To optimize the efficiency of WECS and motor drives research has been focusing on the development of loss minimization algorithms (LMAs) for electric machines. Most of the existing LMAs are based on machine models as the search based LMAs are slow in response. But the electric machine parameters change with operating conditions such as magnetic saturation, temperature variation, etc. The LMA which is fast but independent of machine parameters is yet to be developed. The high performance motor drives used in robotics, rolling mills, automotive industry, etc. require fast and accurate speed response, quick and smooth recovery of speed from any disturbances. In order to deal with the nonlinearities and uncertainties of electric machines and overcome the limitations of the existing controllers (e.g., PID, sliding mode, nonlinear adaptive controllers, etc.), in recent years, attention is being paid to intelligent algorithms (IA) such as, fuzzy logic, neural network, neuro-fuzzy and genetic algorithm to achieve high efficiency and high dynamic performance. Despite extensive research, successful applications of IA for industrial motor drives and wind generators are far from reality due to the lack of proper development. The main objective of this research program is to develop intelligent algorithms based new and advanced control schemes to achieve energy efficient and high performance WECS, converters and motor drives, while coping with system uncertainties. It is intended that two PhD and five Master's students will be involved with me in carrying out these technically challenging open-problems over the next five years. Thus, the proposed research will contribute towards the development of highly qualified personnel desperately needed by the Canadian power, oil, mine and automotive industries, thereby contributing towards Canada's economic growth.

风能作为一种可持续的、环境友好的能源，近年来的普及程度有了显著的增长。根据世界风能协会的数据，到2012年，全球风能容量已达到273吉瓦（千兆瓦），并且还在呈指数级增长。根据加拿大风能协会的数据，加拿大现在是世界上第九大风能生产国，目前装机容量为6.5吉瓦。到2018年，安大略省预计将安装超过5600兆瓦的新风能容量，创造8万人年的就业机会，吸引164亿美元的私人投资。风能转换系统（WECS）涉及风力发电机组、发电机和数控功率变换器系统，是一个复杂的、非线性的、受参数不确定性和未知干扰影响的系统。考虑到风能的巨大增长，一些具有挑战性的技术问题，如从风能到电网的最大功率传输和电机的损耗最小化，以及包含系统不确定性的变流器等问题尚未得到解决。