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

风能作为一种可持续、环境友好的能源，近年来得到了长足的发展。根据世界风能协会的数据，到2012年，全球风能装机容量已达到273吉瓦(吉瓦)，而且还在呈指数级增长。根据加拿大风能协会的数据，加拿大现在是世界第九大风能生产国，目前的装机容量为6.5千兆瓦。预计到2018年，安大略省新增风能装机容量将超过5600兆瓦，创造8万人年就业机会，吸引164亿美元的私人投资。风能转换系统(WECS)涉及风电机组、发电机和数字功率变流器系统，这些系统是复杂的、非线性的，受参数不确定性和未知扰动的影响。考虑到风能的巨大增长，一些具有挑战性的技术问题仍有待解决，如最大限度地将风电功率转移到电网线路、电机损耗最小化以及包含系统不确定性的变流器。*电机和控制技术中的损耗最小化同样适用于风力发电机和电机驱动。电动马达消耗的电能占世界发电量的50%以上。因此，为了有效地利用有限的能源，对电机进行高效率、高动态性能的控制是非常有必要的。为了优化WECS和电机驱动的效率，电机损耗最小化算法(LMAS)的研究一直是研究的重点。现有的大多数LMA都是基于机器模型的，因为基于搜索的LMA响应速度很慢。但是电机参数会随着运行条件的变化而变化，如磁场饱和、温度变化等，这种快速但不依赖于电机参数的直线电机尚未开发出来。用于机器人、轧钢厂、汽车工业等的高性能电机驱动器需要快速准确的速度响应，从任何扰动中快速而平稳地恢复速度。为了处理电机的非线性和不确定性，克服现有控制器(如PID、滑模、非线性自适应控制器等)的局限性，近年来，模糊逻辑、神经网络、神经模糊和遗传算法等智能算法受到了人们的关注，以实现高效率和高动态性能。尽管进行了广泛的研究，但由于缺乏适当的开发，工业智能在工业电机驱动和风力发电机中的成功应用还远远不够。*这项研究计划的主要目标是开发基于智能算法的新的和先进的控制方案，以实现节能和高性能的WEC、转换器和电机驱动，同时应对系统的不确定性。在接下来的五年里，两名博士生和五名硕士生将与我一起完成这些具有技术挑战性的开放问题。因此，拟议的研究将有助于培养加拿大电力、石油、矿山和汽车行业迫切需要的高素质人才，从而为加拿大的经济增长做出贡献。

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.