Tripathi Sandeep, Shrivastava Ashish, Jana Kartick C
Indian Institute of Technology (ISM) Dhanbad, Jharkhand, India.
Manipal University Jaipur, Rajasthan, India.
ISA Trans. 2020 Jun;101:50-59. doi: 10.1016/j.isatra.2020.01.012. Epub 2020 Jan 13.
The demand of electric power consumption is increasing very rapidly worldwide and to fulfill this requirement, solar energy is one of the most viable solution as renewable energy source. Photovoltaic (PV) cell based sun-tracker system (STS) produces maximum current when sunlight vertically incident on its surface. Hence, there is a need of optimized continuous axis position control of STS to achieve maximum output current. This task can be done on the basis of the fuzzy control system. Usually, in the traditional fuzzy control system (FCS), tuning of designed fuzzy parameter is done by trial and error method. However, this type of FCS parameter tuning approach may or may not give optimal solution. Thus, in presented work, an optimal tuning technique with Takagi, Sugeno and Kang (TSK) fuzzy controller (TFC) using Gray Wolf Optimization (GWO) for STS has been proposed. In order to validate the proposed work, different objective functions have been employed to carry out fuzzy controller parameter optimization. A comparative analysis has been performed on the basis of three parameters: settling time, maximum-overshoot and optimal fuzzy parameter on different constrain set. The results obtained with the GWO optimization algorithm were also compared with other popular population algorithms, i.e. Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO) algorithms.
全球范围内电力消耗需求增长极为迅速，为满足这一需求，太阳能作为可再生能源是最可行的解决方案之一。基于光伏（PV）电池的太阳跟踪器系统（STS）在阳光垂直入射其表面时产生最大电流。因此，需要对STS进行优化的连续轴位置控制以实现最大输出电流。此任务可基于模糊控制系统完成。通常，在传统模糊控制系统（FCS）中，设计的模糊参数调整是通过试错法进行的。然而，这种FCS参数调整方法可能会也可能不会给出最优解。因此，在本文提出的工作中，提出了一种使用灰狼优化（GWO）算法对STS进行优化调整的技术，该技术采用了高木、菅野和康（TSK）模糊控制器（TFC）。为了验证所提出的工作，采用了不同的目标函数来进行模糊控制器参数优化。基于三个参数进行了对比分析：调节时间、最大超调量以及在不同约束集下的最优模糊参数。还将通过GWO优化算法获得的结果与其他流行的群体算法，即鲸鱼优化技术（WOT）和粒子群优化（PSO）算法进行了比较。
