https://eng.uok.ac.ir/bevrani/Bevrani/102.pdf

Application of GA optimization for automatic generation control design in an interconnected power system

This paper proposes coordinated control of blade pitch angle of wind turbine generators and plug-in hybrid electric vehicles (PHEVs) for load frequency control of microgrid using model predictive controls (MPCs). The MPC is an effective model-based predictive control, which calculates future control signals by optimization method using plant model, current, and past signals of the system. The MPC-based PHEVs' power control can be used to reduce frequency fluctuation of microgrid effectively. However, for large system, large number of PHEVs is needed to produce satisfying frequency deviation. In order to reduce the number of PHEVs, the smoothing of wind power production by pitch angle control using MPC method is proposed and is coordinated with PHEVs control in this paper. The simulation results confirm that the coordinated control of pitch angle and PHEVs using MPCs is able to reduce the number of PHEVs and the frequency fluctuation can be maintained significantly. In addition, the proposed method is robust to the system parameters variation over proportional–integral derivative controllers.

Coordinated Control of Wind Turbine Blade Pitch Angle and PHEVs Using MPCs for Load Frequency Control of Microgrid

A model predictive controller (MPC) for load frequency control (LFC) of an interconnected power system is investigated. The MPC is based on a simplified system model of the Nordic power system, and it takes into account limitations on tie-line power flow, generation capacity, and generation rate of change. The participation factors for each generator are optimization variables, and suggestions are made as to how one can ensure tie-line power transfer margins through slack-variables, and pricing information through the objective function. The solution of MPC for LFC is completed by including a Kalman filter for state estimation. The presented MPC is compared against a conventional LFC/AGC scheme with proportional integral (PI) controllers. Simulations show that the MPC gives better frequency response while using cheaper resources. This paper illustrates that MPC could be a realistic solution to some of the LFC problems power systems are facing today.

/pdf/model-predictive-load-frequency-control-2jee1o2isu.pdf

Model Predictive Load-Frequency Control

In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern.

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems.

Robust sensor fault detection and isolation scheme for interconnected smart power systems in presence of RER and EVs using unknown input observer

This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the

T. Hiyama

Papers

Decentralized model predictive based load frequency control in an interconnected power system Research Area: Faculty of Engineering Year: