Optimum Generation Scheduling for Thermal Power Plants using Artificial Neural Network

2011 ◽  
Vol 1 (2) ◽  
Author(s):  
Nagaraj Mudukpla Shadaksharappa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Thermal Power Plants ◽  
Generation Scheduling ◽  
Artificial Neural

scholarly journals Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

2021 ◽  
Vol 54 (6) ◽  
pp. 891-895
Author(s):  
Fawaz S. Abdullah ◽  
Ali N. Hamoodi ◽  
Rasha A. Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Machine Learning Algorithms ◽  
Circuit Board ◽  
Electric Circuits ◽  
The Neural Network ◽  
Artificial Neural

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
P. Ilamathi ◽  
V. Selladurai ◽  
K. Balamurugan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Field Experiments ◽  
Thermal Power ◽  
Bottom Ash ◽  
Unburned Carbon ◽  
Optimum Level ◽  
Boiler Load ◽  
Artificial Neural

An approach to model coal combustion process to predict and minimize unburned carbon in bottom ash of a large-capacity pulverized coal-fired boiler used in thermal power plant is proposed. The unburned carbon characteristic is investigated by parametric field experiments. The effects of excess air, coal properties, boiler load, air distribution scheme, and nozzle tilt are studied. An artificial neural network (ANN) is used to model the unburned carbon in bottom ash. A genetic algorithm (GA) is employed to perform a search to determine the optimum level process parameters in ANN model which decreases the unburned carbon in bottom ash.

scholarly journals Artificial Neural Network Based Economic Generation Scheduling in Nigeria Power Network

2014 ◽  
Vol 3 (4) ◽  
pp. 206
Author(s):  
Omorogiuwa Eseosa ◽  
Onohaebi S.O
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Operating Cost ◽  
Inequality Constraints ◽  
Minimum Cost ◽  
Power Network ◽  
Load Demand ◽  
Generation Scheduling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Economic generation scheduling determines the most efficient and economic means of dispatch of generated energy to meet the continuously varying load demand at the most appropriate minimum cost, while meeting all the units equality and inequality constraints in  power network. This is currently not applicable in Nigeria power network. The network under study consists of seventeen (17) generating stations (Existing Network, National Integrated Power Projects and the Independent Power Producers). This work investigates economic generation and scheduling in Nigeria 330KV integrated power network at minimum operating cost using the classical kirmayer’s method and Artificial Neural Network (ANN) for its optimization in Matlab environment. ANN is trained to adopt its pattern at different load demands and acquires the ability to give load demand as soon as the set target and goal tends to equality. Cost function for each generating unit as well as a model for economic generation scheduling was developed.</p>

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