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All Journal International Journal of Electrical and Computer Engineering
Raghvin, Manju Jayakumar
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Computer Science & IT Electrical & Electronics Engineering

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Optimized reactive power management system for smart grid architecture Raghvin, Manju Jayakumar; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3707-3716

Abstract

The Indian power grid is an extensive and mature power system that transfers large amounts of electricity between two regions linked by a power corridor. The increased reliance on decentralized renewable energy sources (RESs), such as solar power, has led to power system instability and voltage variations. Power quality and dependability in a smart grid (SG) setting can be enhanced by the careful tracking and administration of solar energy generated by panels. This study proposes a number of reactive power regulation algorithms that take smart grids into account. When developing a kernel, debugging is a must in optimal reactive power management. In this research, a debugging primitive called physical memory protection (PMP), a security feature, is considered. Debugging in the kernel domain requires specialized tools, in contrast to the user space where we have kernel assistance. This research proposes an optimal reactive power management in smart grid using kernel debugging model (ORPM-SG-KDM) for managing the reactive power efficiently. This research achieved 98.5% accuracy in kernel debugging and 99.2% accuracy in optimal reactive power management. Kernel debugging accuracy is increased by 1.8% and 3% of reactive power management accuracy is increased.
Energy management in smart grids using internet of things and price-based demand response with a hybrid EVO-PDACNN approach Raghvin, Manju Jayakumar; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp699-716

Abstract

Network control systems for energy distribution play an essential role when renewable energy sources (RES) expand and the smart grid (SG) infrastructure increases. A new approach to energy management (EM) in SGs combines energy valley optimizer (EVO) with pyramidal dilation attention convolutional neural network (PDACNN) to achieve its objectives. Through EVO-PDACNN, the system performs accurate energy consumption forecasting with PDACNN, while the EVO algorithm supports systematic scheduling capabilities. Due to its use, this method reduces the peak-to-average ratio (PAR) by 22% also the cost of electricity (COE) by 12%. This method performs better than the wind-driven bacterial forging algorithm (WBFA), genetic algorithm (GA), particle swarm optimization (PSO), modified elephant herd optimization algorithm (MEHOA), and ant colony optimization (ACO) because it has a new prediction ability and quick response. EVO-PDACNN establishes better performance through lower root mean square error (RMSE), together with mean squared error (MSE) and mean absolute error (MAE), which indicates enhanced cost efficiency and resource management capabilities for SGs. The method strengthens both energy forecasting and operational scheduling operations while effectively dealing with changes in supply and demand, which helps build resilient power systems.
Co-Authors Bharamagoudra, Manjula R. Dash, Ritesh