含有风电场的互联电力系统的滑模自动发电控制

发布时间：2019-06-16 12:21

【摘要】：自动发电控制(AGC)对于维持频率稳定具有重要的意义,是保证电力系统稳定运行的一种重要的控制手段。随着风能、太阳能等可再生能源的大规模开发利用,其间歇性为电力系统AGC问题带来了新的挑战。此外,发电机变化率约束(GRC)更加大了AGC问题的复杂性。本文在考虑GRC的条件下,针对含有风电场的互联电力系统的AGC问题展开了相关研究。首先,本文介绍了电力系统各环节的数学模型,以及风力机的简化模型,从而得到含有风电场的两区域互联电力系统的模型对象。分析了包括GRC在内的系统不确定性特性,将GRC非线性看作系统不确定性的一部分进行处理。然后,充分利用滑模变结构控制算法的鲁棒优势,提出了积分滑模控制(I-SMC)以及终端滑模控制(T-SMC)方法。分别设计了基于积分滑模以及终端滑模的自动发电控制器,并通过李雅普诺夫稳定性原理证明了滑模控制系统的稳定性。使用不同滑模控制方法分别实现含有风电场的电力系统的AGC。通过仿真研究验证了所提出控制器的有效性和灵活性。最后,由于风电场的间歇性以及GRC环节的线性化影响到电力系统的不确定性,这会加大系统不确定性的分析和处理难度,为补偿电力系统的不确定性所造成的误差,设计了基于滑模控制的神经网络,以逼近系统的整体不确定性,弥补了滑模控制器的设计过程中不确定性上界未知的缺陷。从闭环系统稳定性出发,设计了神经网络的权值更新律。从理论上,保证了闭环控制系统具备渐近稳。定性。滑模控制器和神经网络补偿器配合实现电力系统的AGC,并通过神经网络提高了控制系统的自适应能力,改善了控制器的控制性能。仿真结果验证了基于神经网络的自动发电控制器的优势,以及神经网络连接权的收敛性。
[Abstract]:Automatic generation control (AGC) is of great significance to maintain frequency stability and is an important control method to ensure the stable operation of power system. With the large-scale development and utilization of renewable energy such as wind energy and solar energy, intermission brings new challenges to the AGC problem of power system. In addition, generator change rate constraint (GRC) increases the complexity of AGC problem. In this paper, the AGC problem of interconnected power systems with wind farms is studied under the condition of GRC. First of all, this paper introduces the mathematical model of each link of the power system and the simplified model of the wind turbine, so as to obtain the model object of the two-area interconnection power system with wind farm. The uncertainty characteristics of the system, including GRC, are analyzed, and the GRC nonlinear is treated as a part of the system uncertainty. Then, taking full advantage of the robust advantages of sliding mode variable structure control algorithm, integral sliding mode control (I-SMC) and terminal sliding mode control (T-SMC) methods are proposed. The automatic power generation controller based on integral sliding mode and terminal sliding mode is designed respectively, and the stability of sliding mode control system is proved by Leonov stability principle. Using different sliding Mode Control methods to realize AGC. of Power system with Wind Farm The effectiveness and flexibility of the proposed controller are verified by simulation. Finally, because the intermittent wind farm and the linearization of GRC affect the uncertainty of power system, which will make it more difficult to analyze and deal with the uncertainty of the system. In order to compensate the error caused by the uncertainty of the power system, a neural network based on sliding mode control is designed to approximate the overall uncertainty of the system and make up for the unknown upper bound of uncertainty in the design process of sliding mode controller. Based on the stability of closed-loop system, the weight updating law of neural network is designed. In theory, the closed-loop control system is guaranteed to be asymptotically stable. Qualitative. The sliding mode controller and the neural network compensator cooperate to realize the AGC, of the power system, and the adaptive ability of the control system is improved by neural network, and the control performance of the controller is improved. The simulation results verify the advantages of automatic power generation controller based on neural network and the convergence of neural network connection weight.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM614;TP273

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