Showing 5 results for Amani
R Subramanian, K Thanushkodi, A Prakash,
Volume 9, Issue 4 (December 2013)
Abstract
The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.
F. Amanifard, N. Ramezani,
Volume 12, Issue 3 (September 2016)
Abstract
The article presents the transients analysis of the substation grounding systems and transmission line tower footing resistances which can affect to the back-flashover (BF) or overvoltage across insulator chain in an HV power systems by using EMTP-RV software. The related transient modeling of the grounding systems is based on a transmission line (TL) model with considering the soil ionization. In addition, different configuration of grounding system have been simulated to calculated the BF, including number of vertical grounding rod, length of rod, point of lightning current injection into the grounding grid and using two depth design of grounding system where the surface of substation under consideration is very small orit is necessary to bury the grounding grid in the rocky media, occasionally. The simulation results have shown that how the mentioned parameters can considerably affect inception of BF, and suitable design of grounding system can reduce damages caused by lightning.
S. Sivasakthi, R. K. Santhi, N. Murali Krishnan, S. Ganesan, S. Subramanian,
Volume 13, Issue 2 (June 2017)
Abstract
The increasing concern of global climate changes, the promotion of renewable energy sources, primarily wind generation, is a welcome move to reduce the pollutant emissions from conventional power plants. Integration of wind power generation with the existing power network is an emerging research field. This paper presents a meta-heuristic algorithm based approach to determine the feasible dispatch solution for wind integrated thermal power system. The Unit Commitment (UC) process aims to identify the best feasible generation scheme of the committed units such that the overall generation cost is reduced, when subjected to a variety of constraints at each time interval. As the UC formulation involves many variables and system and operational constraints, identifying the best solution is still a research task. Nowadays, it is inevitable to include power system reliability issues in operation strategy. The generator failure and malfunction are the prime influencing factor for reliability issues hence they have considered in UC formulation of wind integrated thermal power system. The modern evolutionary algorithm known as Grey Wolf Optimization (GWO) algorithm is applied to solve the intended UC problem. The potential of the GWO algorithm is validated by the standard test systems. Besides, the ramp rate limits are also incorporated in the UC formulation. The simulation results reveal that the GWO algorithm has the capability of obtaining economical resolutions with good solution quality.
A. Karimpour, A. M. Amani, M. Karimpour, M. Jalili,
Volume 17, Issue 4 (December 2021)
Abstract
This paper studies the voltage regulation problem in DC microgrids in the presence of variable loads. DC microgrids generally include several Distributed Generation Units (DGUs), connected to electrical loads through DC power lines. The variable nature of loads at each spot, caused for example by moving electric vehicles, may cause voltage deregulation in the grid. To reduce this undesired effect, this study proposes an incentive-based load management strategy to balance the loads connected to the grid. The electricity price at each node of the grid is considered to be dependent on its voltage. This guide moving customers to connect to cheaper connection points, and ultimately results in even load distribution. Simulations show the improvement in the voltage regulation, power loss, and efficiency of the grid even when only a small portion of customers accept the proposed incentive.
Balamanikandan A, Venkataramanaiah N, Sukanya M, Sudhakar Reddy N, Gomathy G, Venkatachalam K,
Volume 22, Issue 2 (June 2026)
Abstract
Physics-informed neural networks (PINNs) offer a promising route to bridge device-level simulations and compact circuit models. In this work, we present a hybrid modeling framework that integrates TCAD datasets with a baseline compact model and applies a PINN correction to capture stress-condition effects with high fidelity. The proposed approach achieves ≤ 2% route mean square error (RMSE) across more than 2,000 bias points, maintaining stable predictions under temperature (273–373 K) and radiation (0–100 krad) variations. Extracted Berkeley Short-channel IGFET Model (BSIM) parameters enable direct SPICE simulation, ensuring compatibility with standard circuit design workflows. For deployment, the trained PINN is exported as a quantized ONNX model, achieving sub-millisecond inference and ultra-low energy consumption (0.25 pJ/op) on a Cortex-M55 platform. This dual pathway supports both high-accuracy circuit simulation and real-time edge inference, making it suitable for embedded applications under constrained conditions. Comparative analysis with recent ANN-based models confirms that our physics-informed approach offers superior interpretability, SPICE readiness, and deployment efficiency. All datasets, code, and models are released to support reproducibility, benchmarking, and further research in compact modeling and edge-AI integration.
