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All Journal Mechatronics, Electrical Power, and Vehicular Technology Indonesian Journal of Electrical Engineering and Computer Science
Chinda, Padmanabha Raju
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Electrical & Electronics Engineering

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A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices Cherukupalli, Kumar; Chinda, Padmanabha Raju
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1388-1396

Abstract

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.
Congestion management of power transmission line with advanced interline power flow controller Bhukya, Baddu Naik; Chinda, Padmanabha Raju; Rayapudi, Srinivasa Rao; Bondalapati, Swarupa Rani
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.795

Abstract

The growing reliance on renewable energy sources (RES), alongside the surge in electricity consumption, has intensified the challenges associated with congestion management in power transmission lines. This article investigates the use of an advanced interline power flow controller (AIPFC) combined with artificial intelligence (AI) and machine learning (ML) methods to tackle congestion management challenges. The aim is to establish a dependable and effective power system, all while reducing the costs associated with congestion management. Algorithms in AI and ML are utilized to create models aimed at predicting and managing congestion, whereas optimization techniques are applied to identify the most effective operation of AIPFC and strategies for alleviating congestion. The IEEE 30-bus system is utilized as a test case to assess the proposed methodology. A comparative analysis is performed, evaluating the effectiveness of the AI/ML-based approach in relation to traditional congestion management techniques. The findings demonstrate that the incorporation of AIPFC alongside AI/ML methodologies markedly alleviates congestion within the power transmission lines of the IEEE 30-bus system. The proposed combination of model predictive control (MPC) and AIPFC (MPC-AIPFC), integrated with chaotic fuzzy particle swarm optimization (CFPSO), achieves the lowest fuel cost of $798.81/h, the minimum total power loss of 0.0855 pu, and demonstrates congestion mitigation under overload conditions. These results underscore the approach’s significant advancements in reducing cost, optimizing power flow, and relieving congestion compared to traditional methods.
Co-Authors Bhukya, Baddu Naik Bondalapati, Swarupa Rani Cherukupalli, Kumar Srinivasa Rao Rayapudi, Srinivasa Rao