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All Journal International Journal of Renewable Energy Development Mechatronics, Electrical Power, and Vehicular Technology
Srinivasa Rao Rayapudi, Srinivasa Rao
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Chemistry Electrical & Electronics Engineering Energy

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Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition Cherukuri, Santhan Kumar; Rayapudi, Srinivasa Rao
International Journal of Renewable Energy Development Vol 6, No 3 (2017): October 2017
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.6.3.203-212

Abstract

Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-212
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 Chinda, Padmanabha Raju Santhan Kumar Cherukuri, Santhan Kumar