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

作为一种可持续发展和环境友好的能源，风能的普及最近经历了显着的增长。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，目前装机容量为6.5吉瓦。预计到2018年，安大略将安装超过5，600兆瓦的新风能容量，创造8万人年的就业机会，吸引164亿美元的私人投资。风力发电系统包括风力涡轮机、发电机和数字控制的功率变换器系统，具有复杂、非线性、易受参数不确定性和未知干扰影响等特点。考虑到风能的巨大增长，一些具有挑战性的技术问题，如从风到电网的最大功率传输和电机损耗最小化，以及包含系统不确定性的转换器尚未解决。 电机和控制技术中的损耗最小化可以同等地应用于风力发电机和电动机驱动器。电动机消耗了世界上总电能的50%以上。因此，为了有效地利用有限的能源，非常希望以高效率和高动态性能来控制电动机。为了优化WECS和电机驱动器的效率，研究一直集中在电机损耗最小化算法（LMA）的发展。大多数现有的LMA都是基于机器模型的，因为基于搜索的LMA响应缓慢。但电机参数随运行条件如磁饱和、温度变化等而变化，快速但与电机参数无关的LMA尚待开发。用于机器人、米尔斯、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，以及从任何干扰中快速平稳地恢复速度。为了处理电机的非线性和不确定性并克服现有控制器的局限性（例如，PID、滑动模式、非线性自适应控制器等），近年来，为了实现高效率和高动态性能，智能算法（IA）如模糊逻辑、神经网络、神经模糊和遗传算法受到关注。尽管进行了广泛的研究，但由于缺乏适当的开发，IA在工业电机驱动器和风力发电机上的成功应用还远未实现。 该研究计划的主要目标是开发基于智能算法的新型先进控制方案，以实现节能和高性能的WECS，转换器和电机驱动器，同时应对系统的不确定性。打算在未来五年内，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性问题。因此，拟议的研究将有助于培养加拿大电力、石油、采矿和汽车工业迫切需要的高素质人才，从而促进加拿大的经济增长。

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.