浅谈智能控制技术在电力系统自动化中的运用

经济的快速发展,人们的用电量不断的增加,这对电力系统各方面工作都提出了更高的要求,在新的机遇和挑战面前,电力企业只有不断的提高供电可靠性的水平,保证供电的稳定性,从而确保为用户提供安全、高效的电能。所以电力企业在当前电网快速发展的情况下,需要利用智能的控制技术,来保证电力系统的自动化运行,从而确保电网运行的安全和可靠。文章分析了电网的智能控制,并进一步对智能控制法在电力系统中的应用进行了阐述。

Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

High Precision Motion Control of Hybrid Five-Bar Mechanism with an Intelligent Control

Hybrid mecihanism is a new type of planar controllable mechanism. Position control acouracy of system determines the output aconracy of the mechanism. In order to achieve the desired high acowacy, nonlinear factors as friction nmst be accurately compensated in the real-time servo control algoritinn. In this paper, the model of a hybrid five-bar mechanism is introduced. In terms of the characteristics of the hybrid mechanism, a hybrid intelligent control algorithm based on proportional-integral-derivative （PID） control and cerebellar model articulation control techniques was presented and used to perform control of hybrid five-bar mechanism for the lust time. The sinmulation results show that the hybrid control method can improve the control effect remarkably, compared with the traditional PID control strategy.

Intelligent anti-swing control for bridge crane

A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.

Low energy consumption depth control method of self-sustaining intelligent buoy

Aiming at the contradiction between the depth control accuracy and the energy consumption of the self-sustaining intelligent buoy,a low energy consumption depth control method based on historical array for real-time geostrophic oceanography(Argo)data is proposed.As known from the buoy kinematic model,the volume of the external oil sac only depends on the density and temperature of seawater at hovering depth.Hence,we use historical Argo data to extract the fitting curves of density and temperature,and obtain the relationship between the hovering depth and the volume of the external oil sac.Genetic algorithm is used to carry out the optimal energy consumption motion planning for the depth control process,and the specific motion strategy of depth control process is obtained.Compared with dual closed-loop fuzzy PID control method and radial basis function(RBF)-PID method,the proposed method reduces energy consumption to 1/50 with the same accuracy.Finally,a hardware-in-the-loop simulation system was used to verify this method.When the error caused by fitting curves is not considered,the average error is 2.62 m,the energy consumption is 3.214×10^(4)J,and the error of energy consumption is only 0.65%.It shows the effectiveness and reliability of the method as well as the advantages of comprehensively considering the accuracy and energy consumption.

Flatness intelligent control via improved least squares support vector regression algorithm

To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.

A research on an energy-saving software for pumping units based on FNN intelligent control

An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.

A Fuzzy Control Based Intelligent Control System for Flower Greenhouse

In order to realize intelligent control of flower greenhouse＇ s parameters of atmospheric temperature and humidity, lighting intensity, CO2 concentration and soil water content, it carries out design with ZigBee network, embedded controller and intelligent fuzzy control algorithm as core. With advantages of high precision and stability, the design of sensor circuit mainly employs digital module sensors. In order to save energy, the sensor circuit is controlled by relay switch to work at the proper time. The gateway node is designed by employing high performance 32-digit embedded controller and WinCE6.0 embedded OS is self customized. And embedded SQlite database is realized on WinCE6.0 for effectively managing data. The closed loop control is realized by employing fuzzy control algorithm and the test result shows that the deviation of atmospheric temperature is controlled within ± 0.5° C, the deviation of illumination intensity is controlled within ± 283 LUX, the deviation of CO2 concentration is controlled within ± 24 PPM, the deviation of atmospheric humidity is controlled within ± 13% and that of soil water content is controlled within ± 0.9%, thus all parameters fully meet practical requirements of flower greenhouse.

Placement optimization of actuator and sensor and decentralized adaptive fuzzy vibration control for large space intelligent truss structure

Large space truss structure is widely used in spacecrafts.The vibration of this kind of structure will cause some serious problems.For instance,it will disturb the work of the payloads which are supported on the truss,even worse,it will deactivate the spacecrafts.Therefore,it is highly in need of executing vibration control for large space truss structure.Large space intelligent truss system(LSITS) is not a normal truss structure but a complex truss system consisting of common rods and active rods,and there are at least one actuator and one sensor in each active rod.One of the key points in the vibration control for LSITS is the location assignment of actuators and sensors.The positions of actuators and sensors will directly determine the properties of the control system,such as stability,controllability,observability,etc.In this paper,placement optimization of actuators and sensors(POAS) and decentralized adaptive fuzzy control methods are presented to solve the vibration control problem.The electro-mechanical coupled equations of the active rod are established,and the optimization criterion which does not depend upon control methods is proposed.The optimal positions of actuators and sensors in LSITS are obtained by using genetic algorithm(GA).Furthermore,the decentralized adaptive fuzzy vibration controller is designed to control LSITS.The LSITS dynamic equations with considering those remaining modes are derived.The adaptive fuzzy control scheme is improved via sliding control method.One T-typed truss structure is taken as an example and a demonstration experiment is carried out.The experimental results show that the GA is reliable and valid for placement optimization of actuators and sensors,and the adaptive fuzzy controller can effectively suppress the vibration of LSITS without control spillovers and observation spillovers.