Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Niksa-Rynkiewicz, Tacjana; Witkowska, Anna; Głuch, Jerzy; Adamowicz, Marcin

doi:10.2478/pomr-2022-0050

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…-H-MLP (Hierarchical MLP) [31] -The hierarchical model based on MLP neural networksa type of DNNs that consists of multiple layers of interconnected MLPs. This model is hierarchical which means that higher layers learn increasingly complex features, which are built on the basis of the features learned in lower layers.…”

Section: Deep Learning Prediction Blockmentioning

confidence: 99%

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Niksa-Rynkiewicz,

Stomma,

Witkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

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Section: Deep Learning Prediction Blockmentioning

confidence: 99%

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Niksa-Rynkiewicz,

Stomma,

Witkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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“…[143] ORC Hybrid algorithms Niksa-Rynkiewicz et al [144] Gas turbine ANN Then, the Bayesian network [127], Computational Fluid Dynamic coupled with Computational Solid Dynamic [135], FEM and Duhamel's integral [130], probabilistic analysis and fracture mechanics considerations [131], and material testing and mechanical integrity calculations [96] are used for steam turbines. However, this requires the preparation of full geometries and time-consuming and expensive tests for which there are often no resources or time in everyday work.…”

Section: Badur Et Al [139]mentioning

confidence: 99%

Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

Ziółkowski,

Drosińska-Komor,

Głuch

et al. 2023

Energies

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This work is based on a literature review (191). It mainly refers to two diagnostic methods based on artificial intelligence. This review presents new possibilities for using genetic algorithms (GAs) for diagnostic purposes in power plants transitioning to cooperation with renewable energy sources (RESs). The genetic method is rarely used directly in the modeling of thermal-flow analysis. However, this assignment proves that the method can be successfully used for diagnostic purposes. The GA method was presented in this work for thermal-flow studies of steam turbines controlled from the central power system to obtain the stability of RESs. It should be remembered that the development of software using genetic algorithms to locate one-off degradations is necessary for a turbine that works sustainably with RESs. In this paper, against the background of the review, diagnostic procedures create an inverse model of a thermal power plant. Algorithms were used to detect fast global extremes through the convergence of simulated signatures with signs explaining degradation. In addition, statistical dependencies are used in the selection phase to accelerate fault detection. The created procedure allows obtaining a diagnosis in the form of a single degradation. This procedure turns out to be quite effective for the above example.

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“…The results provided by FNN were the most promising and the authors decided to pursue this approach. Artificial Neural Network ANN showcased high quality results also in [34,35]. A multilayer neural network was developed to train the prediction of the heat transfer coefficient during flow condensation.…”

Section: Neural Network Modelmentioning

confidence: 99%

Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur

Gluch,

Niksa-Rynkiewicz,

Mikielewicz

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Most of the literature models for condensation heat transfer prediction are based on specific experimental parameters and are not general in nature for applications to fluids and non-experimental thermodynamic conditions. Nearly all correlations are created to predict data in normal HVAC conditions below 40°C. High temperature heat pumps operate at much higher parameters. This paper aims to create a general model for the calculation of heat transfer coefficients during flow condensation which could be applied to a wide range of fluids and thermodynamical parameters up to the vicinity of the critical point. To achieve this goal authors present a model based on Feed Forward Neural Network. The designed neural network consists of 5 hidden layers and utilizes ReLu and linear activation functions. The first four layers consist of 50 neurons, and the last layer consists of 1 neuron. The network was trained on a consolidated database which consists of 4659 data points for 25 fluids and covers a range of reduced pressure from 0.1 to 0.9 for various mass velocities and diameters. Two input variants were considered. For randomly selected test data Mean Square Root achieved 0.1093 and Mean Absolute Error MAE achieved 0.2243 for the first configuration which consist of 4 parameters. For the second variant, which consists of 17 parameters, MSE achieved 0.0452 MAE achieved 0.1028.

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Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Cited by 5 publications

References 21 publications

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur

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