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Due to the strong nonlinearities of the boiler-turbine system, feasibility and high response capability are hard to realize using traditional linear controllers. Advanced controllers that can ensure stability and feasibility without violating system constraints, and enhance the system’s dynamic output performance, are needed. Therefore, this paper proposes a stable input/output feedback linearization (IOFL) model predictive controller (MPC) technique for the boiler-turbine system. This nonlinear system is decoupled using the IOFL method into a novel linearized model, which is then used for constituting the proposed stable IOFL MPC problem. The proposed scheme uses a constraint mapping method that converts actual input limits into limits on the basis of the control output variable to ensure a feasible solution over the whole prediction horizon. Moreover, a min-max MPC technique in the form of linear matrix inequality with the realization of the input rate-of-change constraints is utilized to ensure the boiler-turbine unit’s stability. The process model and proposed control scheme are executed using MATLAB and the simulation results demonstrate that the proposed controller has an enhanced dynamic output performance compared with an advanced control scheme under various load variations. [ABSTRACT FROM AUTHOR]

In the variable rotor speed and variable blade pitch wind-turbines system, the pitch controller is crucial in the high wind speed range to adjust the generator speed so as to produce the rated power. The main challenges in designing the pitch controller are the wind turbine's nonlinearities, constraints on pitch-angle, the variations in wind-speed, and the unstructured model dynamics. From this perspective, a new pitch controller is proposed by employing partial offline quasi-min-max fuzzy model-predictive control to investigate the variable-speed wind turbine performance. Based on the fuzzy modeling, the online optimization problem is simplified as a partial offline optimization problem (offline design and online synthesis). The key advantage of this controller is the guaranteed stability with actuator constraints using LMI constraints and less computational burden. Also, this controller is compared with standard gain scheduled-PI (proportional integral) controller, which has been utilized profusely in the wind-turbine industry. Furthermore, a typical 5 MW benchmark wind-turbine is employed to validate the results from the nonlinear mathematical model. Several case studies are made to prove the proposed controller effectiveness. The results show the superiority of the proposed controller over the gain scheduled-PI controller and two other advanced control techniques. • The quasi-min-max fuzzy model predictive control (FMPC) strategy has been improved. • Fuzzy modeling is involved with MPC design to cope with wind-turbine nonlinearity. • The wind-turbine system stability has been guaranteed using the quasi-min-max FMPC. • The partial offline strategy has been utilized to alleviate the computation burden. • A typical 5 MW wind turbine is used to test the partial offline quasi-min-max FMPC. [ABSTRACT FROM AUTHOR]

The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes. • A stable economic model predictive control is proposed for boiler-turbine unit. • The nonlinear model is transferred to a new linear form via feedback linearization. • Linear matrix inequalities are constructed to guarantee the system stability. • The proposed scheme enhances dynamic economic performance for boiler-turbine unit. [ABSTRACT FROM AUTHOR]

• An accurate nonlinear model of DFIG is established for DFIG-based wind powergeneration system under unbalanced grid conditions. • A high-efficiency MPC scheme is constituted for DFIG, in which different objective functions under unbalanced grid conditions are defined for realizing different control targets. • The realization of the linear MPC is facilitated by incorporating the input-output feedback linearization technique. • Simulation results using the proposed controller show that power pulsations are effectively suppressed under unbalanced grid conditions while fulfilling good tracking performance. In a modern power system with a high wind power penetrating rate, the control of the doubly-fed induction generator (DFIG) is quite important to ensure high load-following capability. It can be quite challenging when grid voltage unbalance conditions happened. Model predictive control (MPC) is an advanced control strategy that can optimize the DFIG operation, particularly under unbalanced grid voltage conditions. The detailed modeling in the positive and negative synchronous reference frames resulting from the grid unbalance is presented in nonlinear state-space form. An effective linearized technique, i.e., the input-output feedback linearization method, is adopted in the MPC design. The control law is derived by the optimization of two targets that aim to eliminate the pulsations in the active or reactive power. The simulation results prove that power pulsations are effectively suppressed under unbalanced grid voltage conditions while ensuring good track performance. [ABSTRACT FROM AUTHOR]

The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is difficult to work. Regarding the large-scale, geographically dispersed wind farm, an efficient distributed economic model predictive control strategy is proposed, which integrates the power tracking and economic optimization of the wind farm into one optimal control framework. By adopting the global economic cost function, the Nash optimal solutions under distributed framework approach the Pareto optimum. Thus, the reference power from the transmission system operator is accurately tracked, while the global dynamic economic optimality is guaranteed. The simulation results under step wind speed and practical wind speed variations verify the efficiency and reliability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

With the rapid advancement of wind power technology, the importance of lower operating costs and improved real-time control capabilities for offshore wind turbines (OWT) has increased. Nonlinear Economic Model Predictive Control (NEMPC) has gained attention due to its effective balancing of economic objectives. However, implementing real-time control for OWTs faces challenges due to their strong nonlinearity. This study proposes a convex NEMPC (CNEMPC) by incorporating variable transformation to account for the instantaneous energy stored in OWTs. The CNEMPC includes a set of convex constraints and aims to maximize power generation while minimizing fatigue loads on system components like the tower and gearbox. A newly designed moving horizon estimator provides an initial state estimation for the CNEMPC. Real-time iteration of the CNEMPC is achieved by leveraging the similarity of nonlinear programs between adjacent sampling moments. The effectiveness of the proposed control strategy is verified using a 5 MW OWT as the simulation target. • A real-time convex nonlinear economic model predictive control for offshore wind turbines is proposed. • The optimization problem of the nonconvex nonlinear economic model predictive control is convexed from an energy perspective. • Initialization and actual state variables' unmeasurability of the model predictive control are solved. • Real-time iterative solution of optimization algorithms is implemented. [ABSTRACT FROM AUTHOR]

• Parallel PV/BSSs are integrated with the grid using a VSG control. • The VSG control is utilizing an ADC for the system frequency regulation. • GA optimization is used to find the optimized values for VSG control parameters. • Three different case studies including an IEEE 14 bus system is utilized. The future power system is developing to an inverter-based system from a machine dominated power system due to a large integration of renewable energy sources (RESs). Lack of inertial part and intermittency of the RESs causes frequency instability in the power system. The virtual synchronous generator (VSG) development has been made to add inertial control to the power system for controlling the frequency deviation caused by the integration of the photovoltaic system. The VSG system comprised of power electronics components having a droop control mechanism like the conventional synchronous generator, which provides the necessary frequency stability to the power system. In this paper, three parallel VSG based photovoltaic systems integrated with battery storage systems are used to analyze the frequency response and its stability. Moreover, an advanced genetic algorithm optimization method is used to get the optimal values of parameters of the droop controller used in VSG. Furthermore, two different cases with respect to variation in supply and load power of the system are analyzed in detail. Additionally, an IEEE 14 bus system is utilized to validate the proposed system in MATLAB/Simulink. Evident improvements are observed in the simulation results presented through the suggested GA based VSG control technique. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power in the modern power system, the economic model predictive control (EMPC) has been widely employed in the control of the offshore wind power generation system for promoting the dynamic economy. A robust EMPC strategy, aiming to minimizing damage to the turbine while maximizing the electric power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A linear model is utilized to approximate the original nonlinear behavior of the wind power generation system, while the consequent linearization error is taken as the external disturbance of the control system. A linear feedback controller with a robust control invariant set is designed to restrict the deviation between the nominal linear system and the actual nonlinear wind power generation system. Plant simulations, as well as NREL FAST code testing, are given to demonstrate the proposed controller's effectiveness throughout the overall operating region. • Develop a robust economic model predictive control for wind power generation system. • Take the uncertainty of wind speed into theoretical account. • Tackle the model mismatch through the tube-based disturbance rejection scheme. • Design the linear feedback controller to ensure the robust control invariant property. • Validate the controller effectiveness utilizing the NREL FAST and MATLAB simulations. [ABSTRACT FROM AUTHOR]

With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can improve the dynamic economy performance of the system. However, the variable-speed wind turbine (VSWT) system widely used is generally nonlinear and highly coupled nonaffine systems, containing multiple economic terms. Therefore, a nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper. Three groups of simulations verify the effectiveness and reliability/practicability of the proposed nonlinear EMPC strategy. [ABSTRACT FROM AUTHOR]