Creation of Predictive Analytics System for Power Energy Objects

Аракелян, Эдик Койрунович; Пащенко, А.Ф.; Shcherbatov, Ivan; Tsurikov, Grigory; Titov, Fedor

doi:10.1109/mlsd.2019.8911082

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…To realize automatic control of the power supply system in SMLs, a hardware and software complex (Selivanov et al, 2021) is developed that implements the concept of predictive control of energy efficiency based on power output forecast (Shcherbatov, 2019;Arakelian et al, 2019;Shcherbatov et al, 2019). This concept is in line with the trends in infrastructure solutions for the Internet of Things (Yudin et al, 2017;Grigoriev et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Vlasov

Artemiev

Selivanov

et al. 2022

IJEEP

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Harmonious integration of renewable energy sources into current energy systems has taken on increasing importance amid the scarcity of carbon resources. Among the key problems is the imbalance in power consumption, power generation, and significant peak overloads. To deal with this issue, an intelligent software and hardware system is needed, which will effectively implement predictive control algorithms for various energy sources. The research examines the fundamental provisions of the concept of predictive control over a variable load hybrid power system on the basis of power output forecast. The analysis performed has allowed developing a method of predictive control over the power system in a small locality based on machine learning algorithms. The method was tested using an electric power complex simulation, which included four energy sources (solar panel, wind turbines, small hydrogenerator, and standard carbon-fueled generator). The proposed predictive control method has proved to be productive. The algorithms have allowed diversifying the reliability of power supply by ensuring the sustainability of the power grid.

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Section: Resultsmentioning

confidence: 99%

Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Vlasov

Artemiev

Selivanov

et al. 2022

IJEEP

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“…In accordance with the previous research [16][17][18][19][20][21], the construction of a modern predictive analytics system that predicts defects and failures of power equipment requires the implementation of the following sequence of stages:…”

Section: Introductionmentioning

confidence: 88%

“…In this case, the predicted time is calculated as the time until the occurrence of the limiting value (corresponding to the state of defect or failure) of these parameters in the future. Since such predicted parameter characterizes the current technical condition, the probability of the current technical condition belonging to the state of defects can be used [16,17].…”

Section: Predicting the Time Of Occurrence Of Defects In Power Equipm...mentioning

confidence: 99%

Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

et al. 2021

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Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

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Subsystem for Building a Digital Twin of the Main and Auxiliary Equipment of Thermal Scheme of Thermal Power Plant

Shcherbatov

Agibalov

Dolgushev

et al. 2022

Studies in Systems, Decision and Control

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Creation of Predictive Analytics System for Power Energy Objects

Cited by 6 publications

References 6 publications

Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

Subsystem for Building a Digital Twin of the Main and Auxiliary Equipment of Thermal Scheme of Thermal Power Plant

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