Loadability Maximisation in Bilateral Network for Real-Time Forecasting System Using Cuckoo Search Algorithm

  • Venkatasivanagaraju S. Jawaharlal Nehru Technological University Anantapur, India
  • M. Venkateswara Rao Jawaharlal Nehru Technological University Anantapur, India

Abstract

This manuscript proposes an optimal power flow (OPF) solution in a coordinated bilateral power network. The primary goal of this project is to maximise the benefits of the power market using Newton–Raphson (NR) and cuckoo search algorithm CSA methodologies. The global solution is found using a CSA-based optimisation approach. The study is conducted on real-time bus system. To avoid this, creative techniques have lately been used to handle the OPF problem, such as loadability maximisation for real-time prediction systems employing the CSA. In this work, cuckoo search (CS) is used to optimise the obtained parameters that help to minimise parameters in the predecessor and consequent units of each sub-model. The proposed approach is used to estimate the power load in the local area. The constructed models show excellent predicting performance based on derived performance. The results confirm the method’s validity. The outcomes are compared with those obtained by using the NR method. CSA outperformed the other methods in this investigation and gave more accurate predictions. The OPF problem is solved via CSA in this study. Implementing a real-time data case bus system is recommended to test the performance of the established method in the MATLAB programme.

Keywords

optimal power flow, NR method, short-term and long-term load forecasting, cuckoo search algorithm, optimisation and loss minimisation,

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Published
Sep 23, 2022
How to Cite
S., Venkatasivanagaraju; RAO, M. Venkateswara. Loadability Maximisation in Bilateral Network for Real-Time Forecasting System Using Cuckoo Search Algorithm. Computer Assisted Methods in Engineering and Science, [S.l.], sep. 2022. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/475>. Date accessed: 08 dec. 2022. doi: http://dx.doi.org/10.24423/cames.475.
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