Thermal power plant analysis using artificial neural network

Deshpande, Purva; Warke, Nilima; Khandare, Prakash; Deshpande, V.K.

doi:10.1109/nuicone.2012.6493290

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…Using the artificial intelligent technique, the complexity of steam turbine can be studied and analysed which considers the high, medium and low-pressure stage of steam turbine. Using the model prepared by artificial neural network can be used for designing, synthesis, generating simulation and to monitor the power plant control system [12][13][14][15][16][17]. 160-Megawatt steam power plant has different motives like steam extraction, heater for feeding water and separator for moisture.…”

Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

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

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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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.

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Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

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

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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“…In [1] different neural networks architectures were evaluated on the task of predicting heat rate and boiler efficiency from actual plant data. The paper is not specifically dealing with water level prediction in steam boilers, but it provides a comparison of machine learning models that are trained on a real plant data.…”

Section: Related Workmentioning

confidence: 99%

Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

Mugweni¹,

Harb²

2021

IJEM

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“…The major problem with hardware redundancy is the cost (including the sensor cost and maintenance cost). In this context, approaches based on analytical redundancy are proposed in the literature, including artificial neural networks (ANN) [7][8][9], independent component analysis (ICA) [10,11], support vector machine (SVM) [12,13], fuzzy logic [14][15][16], partial least-squares regression (PLSR) [17], and PCA [18][19][20][21][22][23][24]. A study conducted by Hines and Seibert concluded that the simplicity of analytical 2 Science and Technology of Nuclear Installations redundancy techniques and the tractability of their uncertainty calculations could favor them for acceptance by regulatory bodies [25].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Sensors in a NPP Using Optimized PCA

Peng

Liu

et al. 2018

Science and Technology of Nuclear Installations

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An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model's performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

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Thermal power plant analysis using artificial neural network

Cited by 12 publications

References 4 publications

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

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

Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

Condition Monitoring of Sensors in a NPP Using Optimized PCA

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