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

Application of Intelligent Computational Techniques in Power Plants:A Review

Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool

Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurat

Parametric investigation of combustion process optimization for Gas Turbines at SJ Putrajaya

Gas Turbine (GT) power plants have been represented as essential assets of energy units because of their numerous advantages compared to conventional coal power plants. However, their low thermal efficiency may make the continuous baseload operations a lossmaking alternative and threaten to continue. This fact is raising the importance of performing thermodynamic investigation according to the current operations’ conditions. This paper aims to conduct a thermodynamic investigation for two Siemens V94.2 gas turbine (GT) units based on current operations’ conditions. The reason for selecting these units is because they are operating at a much lower thermal efficiency than the designed thermal efficiency, and due to the age factor, the GTs are not suitable for major retrofitting due to poor return on investments. A numerical model is designed to simulate the overall thermodynamic process in the gas turbine using MATLAB SIMULINK.The obtained numerical results are validated by comparing them with the operational data collected from the stations. The thermal efficiency is increased by 30%, with a maximum output power equal to 140MW. The power output had decreased by 0.2% when the ambient temperature was increased by about 6.0 oC. A graphical optimization, where various conditions are plotted as graphs, is also carried out to achieve the maximum thermal efficiency and power output. Finally, a number of recommendations are made to address decreased thermal efficiency and output power

Computational Study of Coal Particle Distribution in Coal Pulverizer: Effect of Air Flow Rate and Coal Particle Flow Rate

Complete combustion of coal fuel in thermal power plant is often achieved, by ensuring output of fine coal particle (< 75μm) is as high as possible. This is due to the fact that same mass of coal particle in smaller sizes, has higher surface exposed to combustion. Hence, the objective of the study is to determine the effect of air flow rate and coal particle flow rate on coal fineness output. Computational fluid dynamics (CFD) modelling and validation with experimental coal fineness test in real plant are made. The optimum range of air flow rate and coal particle flow rate in pulverizer are selected, by considering relevant air/fuel ratio of 1.5 to 2.0 and turbulence intensity

Computational Study of Coal Particle Distribution in Coal Pulverizer: Effect of Air Flow Rate and Coal Particle Flow Rate

Complete combustion of coal fuel in thermal power plant is often achieved, by ensuring output of fine coal particle (< 75μm) is as high as possible. This is due to the fact that same mass of coal particle in smaller sizes, has higher surface exposed to combustion. Hence, the objective of the study is to determine the effect of air flow rate and coal particle flow rate on coal fineness output. Computational fluid dynamics (CFD) modelling and validation with experimental coal fineness test in real plant are made. The optimum range of air flow rate and coal particle flow rate in pulverizer are selected, by considering relevant air/fuel ratio of 1.5 to 2.0 and turbulence intensity

Experimental and Simulation Investigation of Performance of Scaled Model for a Rotor of a Savonius Wind Turbine

Renewable energy sources are preferred for many power generation applications. Energy from the wind is one of the fastest-expanding kinds of sustainable energy, and it is essential in preventing potential energy issues in the foreseeable future. One pertinent issue is the many geometrical alterations that the scientific community has suggested to enhance rotor performance features. Hence, to address the challenge of developing a model that resolves these problems, the purpose of this investigation was to determine how well a scaled-down version of a Savonius turbine performed in terms of power output using a wind tunnel. Subsequently, the effect of the blockage ratio produced in the wind tunnel during the chamber test on the scaled model was evaluated. This study discusses the influences of various modified configurations on the turbine blades’ torque and power coefficients (Cp) at various tip speed ratios (TSRs) using three-dimensional (3D) unsteady computational fluid dynamics. The findings showed that the scaled model successfully achieved tunnel blockage corrections, and the experimental results obtained can be used in order to estimate how the same turbine would perform in real conditions. Furthermore, numerically, the new models achieved improvements in Cp of 19.5%, 16.8%, and 12.2%, respectively, for the flow-guiding channel (FGC at Ⴔ = 30°), wavy area at tip and end (WTE), and wavy area on the convex blade (WCB) models in comparison to the benchmark S-ORM model and under identical wind speed conditions. This investigation can provide guidance for improvements of the aerodynamic characteristics of Savonius wind turbines