Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Quantifying the Impact of Load-Following on Gas-fired Power Plants

Due to rapid penetration of renewables into the grid, natural gas combined cycle (NGCC) power plants are being forced to cycle their loads more frequently and rapidly than for which they were designed. However, the impact of load-following operation on plant efficiency and equipment health are currently poorly understood. The objective of this work is to quantify the impact of load-following on the gas-fired plants by developing high-fidelity multi-scale dynamic models. There are four main tasks in this project. First, dynamic model of an NGCC power plant has been developed. The main components of the NGCC plants are the gas turbine (GT), heat recovery steam generator (HRSG), and steam turbine (ST). The second task focuses on one of the undesired phenomena known as ‘spraying to saturation’ being faced by the NGCC plants during load-following, where the attemperator spray leads to saturation at the inlet of superheater and/or reheater causing damage and eventual failure of the superheater and/or reheater tubes due to two-phase flow. Different configurations of NGCC plants and operation strategies that can not only eliminate ‘spraying to saturation’ but can maximize the plant efficiency have been developed and evaluated. The third task focuses on modeling the unprecedented damages to the boiler components due to rapid load-following, which is leading to higher operation and maintenance (O&M) costs. Stress and wear models have been developed by accounting for creep and fatigue damages in key HRSG components. Multiple locations at the component junctions have been monitored and the most stressed part has been identified as the constraint in the dynamic optimization of the load-following operation. A multi-objective dynamic optimization algorithm has been developed for maximizing plant efficiency and minimizing deviation from desired ramp rates while satisfying operational constraints such as those due to stress and wear. The fourth task focuses on developing reduced order models. Since the modeling domain of interest includes multiple time scales and multiple spatial scales, it can be computationally intractable to use the iii detailed models for optimization/scheduling/control. Therefore, reduced order dynamic models have been developed for the NGCC system including the health models so that they can be computationally tractable for being used in dynamic optimization while providing desired accuracy

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

Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production

A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability

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

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

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few