一种基于规则和学习算法设计的电力系统智能PID控制器

提出了一种基于规则和学习算法设计的电力系统智能ＰＩＤ控制器的设计方法。通过对固定参数电力系统ＰＩＤ控制器性能的研究，验证并获得了一些关于电力系统电压和稳定性控制协调与鲁棒性的结论。在此基础上，研制出一种智能ＰＩＤ控制器，它由基于规则的开关控制和基于学习控制的算法组成。在单机无穷大电力系统中应用的非线性仿真表明，这种智能ＰＩＤ控制器满足电力系统电压和稳定性协调控制的要求，且具有较强的鲁棒性。     

电力系统Transputer自适应稳定控制

本文提出了一种基于多个快速平行处理器--Transputer而实现的电力系统自适应稳定 控制器.文中介绍了这种自适应稳定器实现的硬件、软件系统及实验室动态模拟试验结果.     

Adaptive Controller Based Unified Power Flow Control for Low Power Oscillation Damping

Low frequency oscillation (LFO) within a power system is an important issue. Loss of inter‐connected machine synchronism, reactive load increase, and faults and disturbances lead to a LFO in a range between 0.1–2 Hz. LFOs limit power transfer over long transmission lines, thus degrading the quality of supply. Moreover, conventional local control, namely, the power system stabilizer, is unable to mitigate LFOs. Furthermore, continuation of LFOs will lead to complete system collapse (blackouts). Considering the above issues, there is a pressing need to incorporate advanced control systems to damp inter‐area LFOs. We propose an adaptive‐supplementary unified power flow control (UPFC) for two inter‐connected areas of a power system. Our work is novel in its design of a supplementary control system using a neural network based on a feedback linearization auto‐regression average model. With the above proposed control scheme, the stability of power system is enhanced in terms of: (1) effective LFO damping; (2) power transfer; (3) improvement in the dynamic parameters of the system; (4) active and reactive power support; (5) loss minimization; and (6) demand–supply management. To justify these claims, we implement our proposed controller on a two‐area (four machine) system. Critical analysis of the system is conducted under symmetrical grid faults. The result of the proposed control scheme justifies the performance enhancement of above parameters, as per grid‐code requirements, compared with conventional a proportional integral (PI) controller.     

Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

Smart control techniques for design of TCSC and PSS for stability enhancement of dynamical power system

This paper deals with the simultaneous application of thyristor controlled series capacitor based damping controller and power system stabilizer for stability improvement of dynamic power system. The adaptive neuro-fuzzy inference system and Levenberg–Marquardt artificial neural network algorithm are used to develop the control strategy for thyristor controlled series capacitor based damping controller and power system stabilizer. The power system stabilizer generates appropriate supplementary control signal to an excitation system of synchronous generator to damp the frequency oscillations and improves the performance of the power system dynamic. The performance of power system affected due to the system configuration and load variation. In order to achieve the appreciable damping, the series capacitor is suggested in addition to the power system stabilizer. Nonlinear simulations of single machine infinite bus system are carried out using the individual application of power system stabilizer and simultaneous application of power system stabilizer and thyristor controlled series capacitor. The comparison analysis between conventional and smart control strategies based controllers is demonstrated. Single machine infinite bus system is tested under various operating conditions and disturbances to show the effectiveness of proposed control schemes.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

智能控制在火电站锅炉燃烧系统中的应用

火电站燃烧系统是一个具有非线性、大滞后、参数时变性和不确定性特点的系统,利用常规控制难以达到理想控制效果.该文介绍几种智能控制方法在电站锅炉燃烧控制系统中的应用,并对其发展前景提出见解.     

基于自组织模糊神经网络电力系统稳定器的设计

采用一种自组织模糊神经网络设计电力系统稳定器,该稳定器能通过结构和参数的学习,克服传统模糊控制器设计过程吕存在的盲目性及拚养伤性,避免模糊控制器中模糊逻辑规则的冗余成欠缺。仿夫表明该电力系统稳定器具有良好控制性能。     

电动汽车非车载快速充电系统

为了满足电动汽车快速充电需求,设计了一款大功率非车载电动汽车充电系统.系统采用较为先进数字控制技术、电力电子器件、控制部件、PWM控制方法、参数与积分分离相结合的PI算法,并附加各种控制、保护和通讯电路,实现电动汽车的智能充电功能,并达到了输出大电流、高电压的技术指标,满足了电动汽车的快速充电要求,对电动汽车的普及具有一定的推动作用.