Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Review on data-driven modeling and monitoring for plant-wide industrial processes

Application of support vector machine models for forecasting solar and wind energy resources: A review

Model Predictive Control — Status and Challenges

Power systems are the most complex systems that have been created by men in history To operate such systems in a stable mode, several control loops are needed Voltage frequency plays a vital role in power systems which need to be properly controlled To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance Likewise, the power exchanges between different control areas are controlled by LFC approaches In recent decades, many control approaches have been suggested for LFC in power systems This paper presents a comprehensive literature survey on the topic of LFC In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

Load frequency control (LFC) is crucial for the operation and design of modern electric power systems. This becomes quite challenging, as more wind power is included into the power system. This paper proposes a coordinated distributed model predictive control (DMPC) for the LFC of a power system that includes inherently variable wind-power generations. This DMPC communicates power system measurement and prediction data, and considers the information of other controllers for their local objective to realize effective coordination. The controllers solve the optimization problem while considering given constraints, e.g., generation rate constraints, wind speed, pitch angle, and load input constraints for each area. Since the wind-power output depends largely on the wind speed, different optimization modes for the DMPC were used. Both simulation and experimental tests of a four-area interconnected power system LFC, which consists of thermal plants, hydro units, and a wind farm, demonstrate the improved efficiency of the coordinated DMPC.

Coordinated Distributed MPC for Load Frequency Control of Power System With Wind Farms

Wind speed prediction using reduced support vector machines with feature selection

Reliable control and optimal operation of the doubly fed induction generator (DFIG) is necessary to ensure high efficiency and high load-following capability in modern wind power plants. This is often difficult to achieve using conventional linear controllers, as wind power plants are nonlinear and contain many uncertainties. Furthermore, unbalanced conditions often exist on the power network, which can degrade DFIG system performance. Considering the nonlinear DFIG dynamics, this paper proposes a nonlinear modeling technique for DFIG, meanwhile taking into account unbalanced grid conditions. Then, a nonlinear model predictive controller is derived for power control of DFIG. The prediction is calculated based on the input-output feedback linearization (IOFL) scheme. The control is derived by optimization of an objective function that considers both economic and tracking factors under realistic constraints. The simulation results show that the proposed controller can effectively reduce wear and tear of generating units under normal grid conditions, and reduce the rotor over-current under unbalanced grid conditions, thereby improving the ability of grid-connected wind turbines to withstand grid voltage faults.

Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

A coordinated control strategy is often used to ensure a thermal power plant to have a higher rate of load change, but without violating the thermal constraints. Although model predictive control has been widely used for controlling power plant, handling input constraints is a major problem especially as these plants are nonlinear. Two alternative methods of exploiting the nonlinear predictive control are presented in this paper. One is the input-output feedback linearization technique based on a suitably chosen approximated linear model. The other is based on neuro-fuzzy networks to represent a nonlinear dynamic process using a set of local models. From the criteria based on the integral absolute errors and the relative optimization time for completing the simulation, it is shown that the performance of the coordinated control of a steam-boiler generation plant using these two nonlinear predictive methods are better than the conventional predictive method.

Xiangjie Liu

Papers

Coordinated Distributed MPC for Load Frequency Control of Power System With Wind Farms

Wind speed prediction using reduced support vector machines with feature selection

Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

Nonlinear Multivariable Power Plant Coordinate Control by Constrained Predictive Scheme

Modeling of a 1000MW power plant ultra super-critical boiler system using fuzzy-neural network methods