An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

Architecture for intelligent power systems management, optimization, and storage.

The management of power and the optimization of systems generating and using power are critical technologies. A new architecture is developed to advance the current state of the art by providing an intelligent and autonomous solution for power systems management. The architecture is two-layered and implements a decentralized approach by defining software objects, similar to software agents, which provide for local optimization of power devices such as power generating, storage, and load devices. These software device objects also provide an interface to a higher level of optimization. This higher level of optimization implements the second layer in a centralized approach by coordinating the individual software device objects with an intelligent expert system thus resulting in architecture for total system power management. In this way, the architecture acquires the benefits of both the decentralized and centralized approaches. The architecture is designed to be portable, scalable, simple, and autonomous, with respect to devices and missions. Metrics for evaluating these characteristics are also defined. Decentralization achieves scalability and simplicity through modularization using software device objects that can be added and deleted as modules based on the devices of the power system are being optimized. Centralization coordinates these software device objects to bring autonomy and intelligence of the whole power system and mission to the architecture. The centralization approach is generic since it always coordinates software device objects; therefore it becomes another modular component of the architecture. Three example implementations illustrate the evolution of this power management system architecture. The first implementation is a coal-fired power generating station that utilized a neural network optimization for the reduction of nitrogen oxide emissions. This illustrates the limitations of this type of black-box optimization and serves as a motivation for developing a more functional architecture. The second implementation is of a hydro-generating power station where a white-box, software agent approach illustrates some of the benefits and provides initial justification of moving towards the proposed architecture. The third implementation applies the architecture to a vehicle to grid application where the previous hydro-generating application is ported and a new hybrid vehicle application is defined. This demonstrates portability and scalability in the architecture, and linking these two applications demonstrates autonomy. The simplicity of building this application is also evaluated

Intelligent monitoring interfaces for coal fired power plant boiler trips: A review

A major source of contemporary power is a Coal-fired Power Plant. These power plants have the capacity to continuously supply electricity to almost 500,000 residential and business units. An essential component of a Coal-fired Power plant is automation. A feature of this automation is an Intelligent System developed for the Power Plant. These Intelligent Systems have different configurations and design. This research studies the various Intelligent Monitoring Interfaces developed for Coal-fired Power Plant Trips, their advantages, disadvantages and proposes a new Intelligent Monitoring Interface that would alleviate the disadvantages of the existing systems. Current systems that use Neural Network models are investigated. The improved Intelligent Monitoring Interface as proposed in this paper is a modification of the existing monitoring system for the Coal-fired Power Plant Boiler Trips. It is expected to improve the overall system by implementing remote accessibility and interactability between the plant operator and the control system interface. The interface will also assist the operator by providing guidelines to troubleshoot the identified trips and the remote server application will allow data collected to be viewed anytime, anywhere

Study of power plant, carbon capture and transport network through dynamic modelling and simulation

The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks.The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network.Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC

Tampa Electric Neural Network Sootblowing

Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NO{sub x} formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent soot-blowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, online, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce {sub x} emissions and improve heat rate around the clock

DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MA TLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant integrated acquisition system of JANAMANJUNG coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the proposed artificial intelligent systems. The feedforward neural network methodology has been adopted as a major computational intelligent tool in both systems. The root mean square error has been widely used as a performance indicator of the proposed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general neural network topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The proposed artificial intelligent systems could be adopted on-line as a reliable controller of the thermal power plant boiler