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

Design of water level controller using fuzzy logic system

Water level control is highly important in industrial applications such as boilers in nuclear power plants. In this work a simple water level indicator and a water level controller based on fuzzy logic is proposed. The fabricated electronic level indicator defines 2 levels minimum and maximum through LEDs. The fuzzy logic controller is based on Mamdani type Fuzzy Inference System. The fuzzy controller has two inputs, error in level and rate of change of error and one output, valve position. The fuzzy controller is implemented in MATLAB and then simulated in Simulink to test the behavior of the system when inputs change. The response of the fuzzy controller is then compared with a conventional PID controller. The results are shown sequentially and the effectiveness of the controller is illustrated

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

Integrated process and control modelling of water recirculation in once-through boilers during low load and transient operation

Power plant stability at lower loads is becoming ever more important, highlighting the increasing requirement for the development of advanced models and tools to analyse and design systems. Such tools enable a better understanding of the thermo-fluid processes and their dynamics, which improves the ability to specify and design better control algorithms and systems. During low load operation and transients, such as start-up and shutdown, the required water flow rate through the evaporator tubes of once-though boilers must be significantly higher than the evaporation rate to protect against overheating of the tubes until once-through operation is reached. Controlling the minimum required water flow rate through the evaporator and economiser is notoriously difficult. Within industry, strong emphasis is placed on maintaining the minimum required flow through the economiser and evaporator without adequate consideration of the potential thermal fatigue damage on the economiser, evaporator and superheater components and the risk of turbine quenching incidents. The purpose of this study was to develop an integrated process and control model that can be used to study transient events. The model developed in Flownex can simulate the complex thermo-fluid processes and associated controls of the feedwater start-up system. This includes the waterrecirculation loop, and allows for detailed transient analysis of the complete integrated system. The model was validated using data from an actual power plant in steady state as well as a transient cold start-up, up to once-through operation. Transient results from the model are also compared to the power plant unit during start-up for the addition or loss of mills using the existing control strategy. The model results compare well with the actual process behaviour. A new control strategy was then proposed and tested using the model. The results indicated significant improvement in control performance and overall controllability of the start-up system, and the large temperature fluctuations currently experienced at the economiser inlet during transients were significantly reduced. The new control strategy was also implemented on a real power plant unit undergoing commissioning. During all modes of start-ups (cold, warm and hot), as well as transients, the performance of the control system showed significant improvement, with a notable decline in instabilities of the feedwater flow. As predicted in the model, the large temperature fluctuations are significantly reduced. The new model therefore enabled the development of an improved control strategy that reduces damaging thermal fatigue. The general controllability of transients is also significantly improved, thereby minimizing risks of water carry-over, quenching and unit trips during start-up

Temperature Control via Affine Nonlinear Systems for Intermediate Point of Supercritical Once-Through Boiler Units

For the operation of the supercritical once-through boiler generation units, the control of the temperature at intermediate point (IPT) is highly significant. IPT is the steam temperature at the outlet of the separator. Currently, PID control algorithms are widely adopted for the IPT control. However, PID cannot achieve the optimal performances as the units’ dynamic characteristic changes at different working points due to the severe nonlinearity. To address the problem, a new control algorithm using affine nonlinear system is adopted for a 600 MW unit in this paper. In order to establish the model of IPT via affine nonlinear system, the simplified mechanism equations on the evaporation zone and steam separator of the unit are established. Then, the feedback linearizing control law can be obtained. Full range simulations with the load varying from 100% to 30% are conducted. To verify the effectiveness of the proposed control algorithm, the performance of the new method is compared with the results of the PID control. The feed-water flow disturbances are considered in simulations of both of the two control methods. The comparison shows the new method has a better performance with a quicker response time and a smaller overshoot, which demonstrates the potential improvement for the supercritical once-through boiler generation unit control

Impact of a redox flow battery on the frequency stability of a five-area system integrated with renewable sources

Energy storage devices are imperative to damp out the oscillations caused by sudden magnified disturbances occurring in a power system. The presence of a small rating of storage device in each area can alleviate the system oscillations effectively. Therefore, in this work, redox flow batteries (RFBs) have been integrated in each area of a five-area interconnected system for effective load frequency control (LFC). The RFB pumps up the active power into the system quickly to meet the short-time overload; in turn, the efficacy of the LFC in the system is boosted. Despite the presence of the RFB in the power system, a secondary controller is necessary to quench the deviation of frequency and tie-line power caused by the power mismatch between demand and generation. In this perspective, a cascade controller incorporated with a fractional operator (FO) has been endorsed and designed through a nascent selfish herd optimizer technique to evaluate the transient response of the system. Besides this, the unprecedented performance of fractional-order cascade controllers has been compared with one-stage classical controllers with and without a fractional operator. Further, the robustness of the proposed controller has been inspected through subjecting it to a random load in the presence/absence of an RFB and parametric variation. Finally, the proposed model has been simulated in the OPAL-RT-4510 platform to validate the performance of the proposed controller that has produced in the MATLAB environment.Web of Science1614art. no. 554

Neural network-based data-driven modelling of anomaly detection in thermal power plant

The thermal power plant systems are one of the most complex dynamical systems which must function properly all the time with least amount of costs. More sophisticated monitoring systems with early detection of failures and abnormal behaviour of the power plants are required. The detection of anomalies in historical data using machine learning techniques can lead to system health monitoring. The goal of the research is to build a neural network-based data-driven model that will be used for anomaly detection in selected sections of thermal power plant. Selected sections are Steam Superheaters and Steam Drum. Inputs for neural networks are some of the most important process variables of these sections. All of the inputs are observable from installed monitoring system of thermal power plant, and their anomaly/normal behaviour is recognized by operator’s experiences. The results of applying three different types of neural networks (MLP, recurrent and probabilistic) to solve the problem of anomaly detection confirm that neural network-based data-driven modelling has potential to be integrated in real-time health monitoring system of thermal power plant