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This paper proposes three practical strategies for the coordinated control (CC) of a thermal power plant using dynamic matrix control (DMC) that can be directly applied to industrial power plants. The three strategies are the replacement of conventional CC using DMC, the inclusion of disturbance variables, and a supplementary reference correction of the conventional CC. The performance during wide range operation of the three DMC–CCs is compared and discussed with the simulation results of a large-scale power plant model.

EKE, ibrahim/0000-0003-4792-238X WOS: 000399628600014 The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems. Scientific and Technical Research Council of Turkey (TUBITAK) TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1059B191300593]; Scientific Research Projects Coordination Unit (BAP) of Kirikkale University (TUBiTAK) [2012/112] I.Eke is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) Turkey, through the postdoctoral research program 2219, under application number 1059B191300593. and Scientific Research Projects Coordination Unit (BAP - project number 2012/112) of Kirikkale University (TUBiTAK).

Interface of a fuel cell plant to power grid is challenging because of the high nonlinearities of the fuel cell plant and the power conditioning system (PCS). This paper focuses on the control of grid-connected solid-oxide fuel cell (SOFC) power plant that is subject to varying load and uncertain network parameters. To this end, Active Disturbance Rejection Control (ADRC) is utilized to improve the performance of the PCS consisting of a dc-dc converter and a dc-ac inverter. ADRC is used in the dc-dc converter to stabilize the dc link voltage and yield a robust performance against the nonlinearity. Used in the dc-ac inverter, ADRC eliminates the steady-state error and is insensitive to the high-frequency noise. Simulation results show that, for grid current control, ADRC achieves a more robust performance than the conventional proportional-integral (PI) controller. Moreover, the total harmonic distortions (THDs) of the output current controlled by ADRC are always below 5% in spite of the variation in the load demand and network parameters.

Reliable Load frequency control (LFC) is crucial to the operation and design of modern electric power systems. However, the power systems are always subject to uncertainties and external disturbances. Considering the LFC problem of a multi-area interconnected power system, this paper presents a robust distributed model predictive control (RDMPC) based on linear matrix inequalities. The proposed algorithm solves a series of local convex optimization problems to minimize an attractive range for a robust performance objective by using a time-varying state-feedback controller for each control area. The scheme incorporates the two critical nonlinear constraints, e.g., the generation rate constraint (GRC) and the valve limit, into convex optimization problems. Furthermore, the algorithm explores the use of an expanded group of adjustable parameters in LMI to transform an upper bound into an attractive range for reducing conservativeness. Good performance and robustness are obtained in the presence of power system dynamic uncertainties.

Direct energy balance (DEB) coordinated control scheme is widely used by field engineers in coal-fired power plants while attracting little attention in the academic community. This paper aims to derive a mathematical model that is suitable for DEB research. To balance the model’s fidelity and simplicity, the power plant is divided into three transformation modules and, using conservation laws, a dynamic model is developed to describe each module. Within reasonable assumptions, numerous module equations are combined to yield a 6th-order nonlinear model. Time constants of the model are identified based on Pareto optimization. Model accuracy is confirmed using field measurements from a 300 MW coal-fired power plant. Based on the linearized model, the merits of the DEB control structure are analyzed. It is confirmed that the DEB control is sufficient to fulfill the fundamental goals of power plant regulation. An illustration of performance improvement is given by introducing gain scheduling techniques to the DEB structure. The proposed model can provide groundwork for future development of advanced control algorithms under the DEB structure.

Under present widespread automatic generation control (AGC) centered on regional power grid, a large-capacity coal-fired supercritical (SC) power unit often operates under wide-range variable load conditions. Since a SC once-through boiler unit is represented by a typical multivariable system with large inertia and non-linear, slow time-variant and time-delay characteristics, it often makes the coordinated control quality deteriorate under wide-range loading conditions, and thus influences the unit load response speed and leads to heavy fluctuation of the main steam pressure. To improve the SC unit’s coordinated control quality with advanced intelligent control strategy, the neural-network (NN) based expanded-structure inverse system models of a 600 MW SC boiler unit were investigated. A feedforward neural network with time-delayed inputs and time-delayed output feedbacks was adopted to establish the inverse models for the load and the main steam pressure characteristics. Based on the model, a neural network inverse coordinated control scheme was designed and tested in a full-scope power plant simulator of the given SC power unit, which showed that the proposed coordinated control scheme can achieve better control results compared to the original PID coordinated control.

Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC.

Bed temperature control, being of great significance for the safety, durability and NOx reduction of circulating fluidized bed (CFB) boiler, is nowadays becoming increasingly challenging due to the participation of CFB boiler in load regulation and the existence of various uncertainties such as the feed fuel quality variation and other unknown disturbances. To this end, this paper develops an engineering-friendly multi-model predictive sliding mode control (MM-PSMC) strategy for bed temperature regulation to attain a better adaption to wide load variation. A second-order plus time-delay (SOPDT) model based predictive sliding mode controller (PSMC) is designed to deal with the time-delay, model parameter uncertainty and external disturbances, and taken as local controller in MM-PSMC. An improved hysteresis switching logic is introduced to select a controller that is most suitable for the current operating condition and avoid system chattering caused by fast and unnecessary switching of controllers among local models. Simulation results on a 220t/h CFB boiler demonstrate the merits of the proposed MM-PSMC strategy over a wide operating range, depicting a promising prospect in industrial applications.

Motivation of this paper is to propose an engineering friendly control strategy to handle the various difficulties in fluidized bed combustor (FBC). The control objectives of FBC and the difficulties arisen from nonlinear dynamics, frequent disturbances and strong coupling are first formulated. The capability of the disturbance observer (DOB) to handle the nonlinearity and disturbances is analyzed and the decoupling effect of DOB is revealed in terms of equivalent transfer function. For the power loop, a robust loop shaping design method is proposed to balance the performance and robustness of DOB. For the temperature loop, the DOB filter is designed based on H ∞ optimization. Abilities of DOB are confirmed by numerical simulations. A water tank experiment is designed to show the simplicity of implementing DOB in an industrial Distributed Control System. Finally, a global simulation on the FBC process demonstrates that, compared with the conventional PI scheme, the DOB-enhanced PI strategy achieves overall improvement and can even be comparable with the complex Model Predictive Control in some aspects.

This paper develops a hierarchical control system structure based on the Takagi–Sugeno fuzzy model to achieve an optimal control of a boiler–turbine unit. In the upper layer of the hierarchy, an optimal reference governor is designed to find the optimal operating point. A disturbance term is introduced to the fuzzy model to lump the modeling mismatch and unknown disturbance. Thus, the effect of plant behavior variation can be removed and the operating point found can be feasible to control. In the lower layer, a stable model predictive controller is developed to track the optimal set-points while guaranteeing the input-to-state stability of the system. Fuzzy Lyapunov function and appropriate slack and collection matrices are used to reduce the conservatism of stability design and improve the performance. Through the estimation of the disturbance term using an observer, the two layers in the hierarchy are coupled and the integrated system can realize a dynamic optimal control of the boiler–turbine unit, even in the case of severe plant behavior variations.

Active Disturbance Rejection Control (ADRC) is emerging as a promising solution in dealing with the unmeasurable disturbances and unknown uncertainties, which are treated in a lumped manner and augmented as an extended state variable. Subsequently, an extended state observer (ESO) is designed to estimate and cancel the combined uncertain term in real time, modifying the uncertain plant to behave like a nominal model consisting of integrators. In the original ADRC formulation, the plant model is assumed to be of delay-free and its order is assumed to be equal to that of the real plant. However, a low-order ADRC is preferred and received a wide acceptance in practice because of its simplicity. Currently, the feasibility of such practice is not clearly revealed as well as its potential dangers. To this end, this paper analyzes the control mechanism from the perspective of the modified plant, which, in turn, would give guidance to parameter tuning. The effect of each parameter on the compensation efficiency and stability conditions of the modified plant is analyzed, based on which a complete tuning procedure for ADRC is developed where the initial settings is derived from the existing PI controller parameters. Finally, the proposed tuning method is experimentally used for a furnace pressure regulation of a 1000MW power plant, validating the feasibility of the low-order ADRC, even in the absence of both dynamic model and the information on the model order.

Power plants are being required to undertake large power variations to satisfy electricity demand, whereas their control systems comprise decentralized control loops and ad-hoc compensation schemes, to deal with loop interaction, for regulation and disturbance rejection at base load. This paper presents a compensation scheme that lessens control loop interaction, caused by the nonlinear coupled process dynamics, to ease power generation control throughout the power plant operating space. Compensation factors are systematically determined from a process gain matrix that conveys all interaction information. Numerical analysis and simulation experiments demonstrate the feasibility of power plant wide-range operation with the proposed compensator.

A mathematical process model for an internal reforming molten carbonate fuel cell power plant is discussed in this paper. The dominant thermal and chemical dynamic processes are modeled for the cell stack array and balance-of-plant, including cathode gas preparation, heat recovery, and fuel processing. Physical data is obtained from a 2 MW system design that was a precursor to a demonstration plant operated at the City of Santa Clara, CA, USA. Steady state validation for several load points is provided for the cell stack array and a load cycling control system is described and tested under ramping operation between load points. r 2002 Elsevier Science Ltd. All rights reserved.