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

Dynamic modelling, validation and analysis of coal-fired subcritical power plant

Coal-fired power plants are the main source of global electricity. As environmental regulations tighten, there is need to improve the design, operation and control of existing or new built coal-fired power plants. Modelling and simulation is identified as an economic, safe and reliable approach to reach this objective. In this study, a detailed dynamic model of a 500 MWe coal-fired subcritical power plant was developed using gPROMS based on first principles. Model validations were performed against actual plant measurements and the relative error was less than 5%. The model is able to predict plant performance reasonably from 70% load level to full load. Our analysis showed that implementing load changes through ramping introduces less process disturbances than step change. The model can be useful for providing operator training and for process troubleshooting among others

The application of a new PID autotuning method for the steam/water loop in large scale ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

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

Operating Point Optimization of a Hydrogen Fueled Hybrid Solid Oxide Fuel Cell-Steam Turbine (SOFC-ST) Plant

This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST) system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC) based on a steam turbine (ST). In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels.This work has been carried out in the Intelligent Systems and Energy research group of the University of the Basque Country (UPV/EHU) and has been supported by the UFI11/28 research grant of the UPV/EHU and by the IT677-13 research grant of the Basque Government (Spain) and by DPI2012-37363-CO2-01 research grant of the Spanish Ministry of Economy and Competitiveness

Development of a Data Driven Multiple Observer and Causal Graph Approach for Fault Diagnosis of Nuclear Power Plant Sensors and Field Devices

Data driven multiple observer and causal graph approach to fault detection and isolation is developed for nuclear power plant sensors and actuators. It can be integrated into the advanced instrumentation and control system for the next generation nuclear power plants. The developed approach is based on analytical redundancy principle of fault diagnosis. Some analytical models are built to generate the residuals between measured values and expected values. Any significant residuals are used for fault detection and the residual patterns are analyzed for fault isolation. Advanced data driven modeling methods such as Principal Component Analysis and Adaptive Network Fuzzy Inference System are used to achieve on-line accurate and consistent models. As compared with most current data-driven modeling, it is emphasized that the best choice of model structure should be obtained from physical study on a system. Multiple observer approach realizes strong fault isolation through designing appropriate residual structures. Even if one of the residuals is corrupted, the approach is able to indicate an unknown fault instead of a misleading fault. Multiple observers are designed through making full use of the redundant relationships implied in a process when predicting one variable. Data-driven causal graph is developed as a generic approach to fault diagnosis for nuclear power plants where limited fault information is available. It has the potential of combining the reasoning capability of qualitative diagnostic method and the strength of quantitative diagnostic method in fault resolution. A data-driven causal graph consists of individual nodes representing plant variables connected with adaptive quantitative models. With the causal graph, fault detection is fulfilled by monitoring the residual of each model. Fault isolation is achieved by testing the possible assumptions involved in each model. Conservatism is implied in the approach since a faulty sensor or a fault actuator signal is isolated only when their reconstructions can fully explain all the abnormal behavior of the system. The developed approaches have been applied to nuclear steam generator system of a pressurized water reactor and a simulation code has been developed to show its performance. The results show that both single and dual sensor faults and actuator faults can be detected and isolated correctly independent of fault magnitudes and initial power level during early fault transient

U-tube Steam Generator Fault Detection Using Fuzzy Model

In safety critical system like power plant system,the problem of detecting the occurrence of faults is of paramountimportance due to their disastrous consequences. To allowefficient performance under different operating conditions, apower plant system requires the integration of many subsystems.This results in a complex system which will inevitably besubjected to faults caused by actuators, sensors or subsystemsfaults during operation. One of the major subsystems, in powerplant is U-tube Steam Generator (UTSG). This paper isconcerned with Fault Detection and Isolation (FDI) of UTSGsystem using fuzzy model. These methods aims at checking theconsistency between observed and predicted behaviour byresiduals. When an inconsistency is detected between themeasured and predicted behaviours obtained using a faultlesssystem model, a fault can be indicated. Simulation resultspresented in the final part of the paper confirm the effectivenessof this approach