Control of Solar Power Systems: a survey

9th International Symposium on Dynamics and Controlof Process Systems (DYCOPS 2010)Leuven, Belgium, July 5-7, 20109This paper deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems.Ministerio de Ciencia y Tecnología DPI2008-05818Ministerio de Ciencia y Tecnología DPI2007-66718-C04-04Junta de Andalucía P07-TEP-0272

Detailed model for robust feedback design of main steam temperatures in coal fired boilers

Main steam temperatures play a significant role in large coal fired power plant operation. Ideally, main steam temperatures should be accurately controlled to protect the thick wall components against long term overheating and thermal stress while meeting the design conditions at the steam turbine inlet. Although high steam temperatures are beneficial for thermal efficiency, it accelerates creep damage in high temperature components which is detrimental to the life of components. Alternatively, low steam temperatures increase the moisture content at the last stage blades of the turbine, causing the blades to deteriorate and fail. Control of the outlet steam temperature according to design conditions at variable loads is maintained via a balance between heat input (flue gas temperature and mass flow rate), evaporator outlet steam mass flow and spray water. The present control philosophy accuracy of main steam temperatures at an Eskom coal fired power plant was evaluated and compared to the latest technology and control strategies. Improving and optimizing steam temperature controls ensures design efficiency while maintaining long term plant health. The level of spatial discretization applied in simplifying the real boiler for modelling purposes was approached at a relatively high level. The intention was to model normal operating conditions and certain transients such as variable heat input and load changes to see its effect on steam temperatures and to be able to evaluate the performance of different temperature control techniques. The main outcome of this project was to design a robust control system for a dynamic model of the boiler using sets of low order linear models to account for uncertainty. The main concepts, models and theories used in the development of this dissertation include: 1) A detailed thermo-fluid model developed using Flownex to have high fidelity models of the process under varying operating conditions. This model was used to test and evaluate the robust controller design. 2) System Identification in Matlab to construct mathematical models of dynamic systems from measured inputoutput data and identify linear continuous time transfer functions under all operating conditions [1]. 3) Quantitative Feedback Theory (QFT) to design controllers for an attemperator control system at various onload operating conditions. This design was used understand the engineering requirements and seeks to design fixed gain controllers that will give desired performance under all operating conditions. 4) The design of a valve position controller to increase the heat uptake in a convective pass, thereby improving efficiency: Excessive attemperation in the superheater passes is generally associated with high flue gas temperatures which decrease thermal efficiency. Therefore, robust control of the attemperation system leads to an increase in heat uptake between the flue gas and steam in the boiler, resulting in a reduction in the flue gas temperature leaving the boiler, thus improving efficiency. The robust QFT controllers were set up using the valve position control technique and were used to confirm the improvement of control performance. The theories mentioned above were used to understand the control performance under varying plant conditions using a standard cascaded arrangement. It incorporated robust control design and engineering requirements such as bandwidth, plant life, spray water and thermodynamic efficiency. The control effort allocated to each superheaterattemperator subsystem in the convective pass was designed as a multi-loop problem

Adaptive neural network cascade control system with entropy-based design

A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN-PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function (PDF) being controlled towards zero. The NN-controller design based on this new performance function is developed and the convergent conditions are. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN-PID controllers tuned with EEC and ECC criterions

Steam Temperature Control Based on Modified Active Disturbance Rejection

Tato práce je zaměřena na studium využitelnosti algoritmu aktivního odstranění vlivu neměřené poruchy a modifikovaného algoritmu aktivního odstranění vlivu neměřené poruchy aplikovaného na řízení teploty přehřáté páry v tepelné elektrárně. Studie byly prováděny na základě linearizovaného modelu přehříváku. Studium algoritmu aktivního odstranění vlivu neměřené poruchy je relevantní v souvislosti s možností jeho aplikace pro komplexní technologické procesy (procesy/soustavy s velkým počtem parametrů). zde je studovaným objektem přehřívák, který je součástí technologického uzlu přípravy přehřáté páry pro dodávku vysokotlaké páry do vysokotlakého stupně turbíny. Efektivnost obou algoritmů aktivního odstranění vlivu poruchy v porovnání s klasickým PID regulátorem je demonstrována na výsledcích simulací. Podrobnější analýza obou metod je nezbytná zejména v případě, kdy řídíme systém vyššího řádu jako například v případě přehříváku. Výsledky analýzy jsou také v práci uvedeny.This work is aimed at studying the applicability of active disturbance rejection algorithm and modified active disturbance rejection algorithm for use in controlling the superheated steam temperature in propulsion of thermal power plant. The studies were conducted on the basis of the linearized model of the superheater. The algorithm itself for active disturbance rejection is relevant to study in connection with the possibility of its application for complex technological objects (objects with a large number of parameters). These objects are the superheater, which is part of the superheated steam preparation object, for supplying high-pressure steam to the turbine high pressure stage. To demonstrate the effectiveness of this algorithm (within the framework of the problem of disturbance rejection) in comparison with the classical PID controller, the results of mathematical modeling are presented. The paper also presents the results of a study of a modified active disturbance rejection method. The need to study this method is due to the high order of the mathematical model of the control object under study. The results of these studies are also given in the work

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Modelling and predictive control of a drum-type boiler

Boilers generate steam continuously and on a large scale. Controlling the boiler process is extremely difficult - it is a highly nonlinear process, its dynamics vary with load and it is strongly multivariable. It is also inherently unstable due to the integrator effect of the drum. In addition, boilers are commonly used in situations where the load can change suddenly and without prior warning. Traditionally, boilers have been controlled by Single-Input, Single-Output (SISO) Proportional plus Integral (PI) controllers. This strategy does not take into account the interaction of the controlled variables or the effect of load on boiler dynamics. This work investigates whether boiler control can be improved by applying multivariable or nonlinear predictive control strategies. Predictive control is a model-based control strategy which is chosen for its ability to handle nonlinear, constrained and multivariable systems. Two nonlinear controllers are developed - a fuzzified linear predictive controller which is based upon several linearised models of the plant and and a nonlinear predictive controller, based upon a single nonlinear plant model. These controllers are compared both with each other and with the conventional PI control strategy. A detailed first-principles model of the boiler is developed for this work. This is used to simulate a boiler plant for controller testing. It is also used to derive a linear state-space model for the linear predictive controller. The nonlinear predictive controllers uses a neural network model

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Study of supercritical coal-fired power plant dynamic responses and control for grid code compliance

The thesis is concerned with the study of the dynamic responses of a supercritical coal-fired power plant via mathematical modelling and simulation. Supercritical technology leads to much more efficient energy conversion compared with subcritical power generation technology so it is considered to be a viable option from the economic and environmental aspects for replacement of aged thermal power plants in the United Kingdom. However there are concerns for the adoption of this technology as it is unclear whether the dynamic responses of supercritical power plants can meet the Great Britain Grid Code requirement in frequency responses and frequency control. To provide answers to the above concerns, the PhD research project is conducted with the following objectives: to study the dynamic responses of the power plant under different control modes in order to assess its compliance in providing the frequency control services specified by the Great Britain Grid Code; to evaluate and improve the performance of the existing control loops of the power plant simulator and in this regard a controller based on the Dynamic Matrix Control algorithm was designed to regulate the coal flow rate and another controller based on the Generalized Predictive Control algorithm was implemented to regulate the temperature of the superheated steam; to conduct an investigation regarding frequency control at the power plant level followed by an analysis of the frequency control requirements extracted from the Grid Codes of several European and non-European countries. The structure and operation of the supercritical power plant was intensively studied and presented. All the simulation tests presented in this thesis were carried out by the mean of a complex 600 megawatts power plant simulator developed in collaboration with Tsinghua University from Beijing, China. The study of the conducted simulation tests indicate that it is difficult for this type of power plant to comply with the frequency control requirements of the Great Britain Grid Code in its current control method. Therefore, it is essential to investigate more effective control strategies aiming at improving its dynamic responses. In the thesis, new Model Predictive Control power plant control strategies are developed and the performance of the control loops and consequently of the power plant are greatly improved through implementation of Model Predictive Control based controllers