Manonmani N, Subbiah V, Sivakumar L
Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India.
Department of EEE, PSG College of Technology, Coimbatore 641004, India.
ScientificWorldJournal. 2015;2015:746017. doi: 10.1155/2015/746017. Epub 2015 Oct 1.
The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.
风力涡轮机发展的关键目标是确保输出功率持续增加。经证实，风力涡轮机在故障期间及故障后向电网提供必要的无功功率，以辅助电网电压的稳定。此时，本文介绍了一种基于新颖启发式算法的控制器模块，该模块采用差分进化和神经网络架构，以提高与双馈感应发电机（DFIG）相连的并网风力涡轮机的低电压穿越率。传统的基于撬棒的系统主要用于在电网故障发生时保护转子侧变流器。发现这种传统控制器无法满足预期要求，因为在连接撬棒期间，双馈感应发电机的行为类似于鼠笼式电机，并从电网吸收无功功率。本文通过引入启发式控制器解决了这一限制，该控制器无需使用撬棒，并确保风力涡轮机在故障期间向电网提供必要的无功功率。本文设计的控制器旨在在电网故障期间增强双馈感应发电机变流器，并且该控制器无需使用任何其他硬件模块即可处理故障穿越。本文介绍了一种双小波神经网络控制器，该控制器采用差分进化进行适当调整。为了验证所提出的控制器模块，通过仿真对一个风电场进行了案例研究，该风电场有1.5兆瓦的风力涡轮机连接到25千伏的配电系统，通过一条30公里长的25千伏馈线向120千伏电网输送电力。
