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- W4385981505 abstract "The demand for electrical energy is rising every year. There is a need for more generation and transmission facilities which require huge investment in the erection of new power stations and lines. The conventional sources of energy are coal, oil, fossil fuel, and nuclear energy-based power plants which are fast depleting. The coal-based plants release different gases like nitrogen dioxide, sulfur dioxide, particulate matter (PM), mercury, and other substances which are very harmful to human life as these gases pollute the atmosphere. The erection of new power plants is costly and affects the environemnt too. The existing transmission line was overloaded which created stability problems in the system. Fixed capacitors (FC) were used in the transmission lines to meet this problem but there were problems like series resonance, wear and tear and slow response. Here comes the role of Power -Electronics based FACTS device Thyristor Controlled Series Capacitor (TCSC) to provide variable compensation and to mitigate these power problems and challenges without the need for investment in the construction of new lines. Traditionally Proportional Integral (PI) controllers have been used for power flow control by TCSC. In the present work the Artificial Neural Network (ANN) technique and Random Forest Machine Learning Algorithm (RFMLA) which comes under the umbrella of Artificial Intelligence have been applied to enhance the power flow transfer capacity of the Kanpur Ballabhgarh transmission line. Power obtained at the receiving end is more with an ANN-based controller. Power obtained is furthermore by the use of RFMLA. The existing power system is utilized more effectively, the efficiency of the system is enhanced, the losses are reduced with modern technology enabled/based TCSC. Thus the objective of meeting the shortage of power without harming the environment is achieved with ANN and RFMLA-based TCSCs. The TCSC based on ANN and RFMLA is successful in meeting various other power challenges and problems also like stability, power quality, low voltage profile of the system. The results of power flow are checked with MATLAB simulation." @default.
- W4385981505 created "2023-08-19" @default.
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- W4385981505 date "2023-01-01" @default.
- W4385981505 modified "2023-10-14" @default.
- W4385981505 title "Artificial Neural Network and Random Forest Machine Learning Algorithm Based TCSC Controllers for Mitigating Power Problems" @default.
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- W4385981505 doi "https://doi.org/10.1007/978-3-031-36118-0_24" @default.
- W4385981505 hasPublicationYear "2023" @default.
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