Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation DOI Creative Commons
Tousif Khan Nizami, Sasank Das Gangula, Ramanjaneya Reddy Udumula

et al.

Engineering Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 27, 2024

ABSTRACT This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates polynomial‐neural network with a backstepping strategy yield robust system for tracking motor. Such unification of online network‐based estimation and control, results effective regulation across wide load torque uncertainties, besides yielding promising transient steady‐state performance. stability entire closed‐loop is ensured through Lyapunov criterion. efficacy revealed extensive experimental investigation under various operating points during start‐up, step‐reference tracking, external step‐load disturbances. real‐time experimentation conducted on laboratory prototype 200 W, using dspace DS1104 board MPC8240 processor. obtained confirm improvement response by significantly reducing settling time steady state behavior no peak over/undershoots disturbances, contrast other similar works presented literature intended same application.

Language: Английский

Power Generation Expansion Planning With High Penetration of Geothermal Energy – Potential, Prospects and Policy DOI Creative Commons
Muhammad Amir Raza, Mahmoud Ahmad Al‐Khasawneh, Yahya Z. Alharthi

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100614 - 100614

Published: Jan. 1, 2025

Language: Английский

Citations

2
Global progress towards the Coal: Tracking coal Reserves, coal Prices, electricity from Coal, carbon emissions and coal Phase-Out DOI
Muhammad Amir Raza,

Abdul Karim,

M.M. Aman

et al.

Gondwana Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

10
Maximum Power Point tracking implementation based on self-learning adaptive GA-Neural controller for standalone PV applications DOI Creative Commons

Ihssane Chtouki,

Patrice Wira,

Malika Zazi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104587 - 104587

Published: March 1, 2025

Language: Английский

Citations

0
An Intelligent Frequency Control Scheme for Inverting Station in High Voltage Direct Current Transmission System DOI Creative Commons

Saleem Saleem,

Muhammad Amir Raza,

Syed Waqar Umer

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 1, 2025

ABSTRACT Power system stability is crucial for the reliable and efficient operation of electrical grids. One key factors affecting power frequency alternating current (AC) while connected with High Voltage Direct Current (HVDC) transmission system. Changes in load demand can lead to deviations, which have detrimental effects on performance Frequency should therefore be controlled within predefined limits order prevent unexpected disturbances that may cause problems loads or even entire fail. A broad simulation model HVDC developed using MATLAB software evaluate effectiveness proposed controllers such as Adaptive Neuro‐Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), optimization Proportional‐Integral‐Derivative (PID) controller Particle Swarm Optimization (PSO) based control strategy addressing instability problems. To assess how well ANFIS, ANN, PID‐PSO controls system, several situations were simulated, including changes operational circumstances. The result reveals ANN performs more accurate results than other and, displaying its capacity successfully reduce deviations maintained a 50 Hz. Adopted method suggested easy integration AC grid enhances quality stability.

Language: Английский

Citations

0
Hybrid Fuzzy–DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels DOI Creative Commons
Diana Yaziel Ortiz-Muñoz, David Luviano‐Cruz, Luis Pérez-Domínguez

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4869 - 4869

Published: April 27, 2025

Partial shading conditions reduce the efficiency of photovoltaic (PV) systems by introducing multiple local maxima in power–voltage curve, complicating Maximum Power Point Tracking (MPPT). Traditional MPPT methods, such as Perturb and Observe (P&O) Incremental Conductance (IC), frequently converge to maxima, leading suboptimal power extraction. This study proposes a hybrid reinforcement learning-based approach that combines fuzzy techniques with Deep Deterministic Policy Gradient (DDPG) enhance tracking accuracy under partial shading. The method integrates membership functions into actor–critic structure, improving state representation convergence speed. proposed algorithm is evaluated simulated PV environment various scenarios benchmarked against conventional P&O IC methods. Experimental results demonstrate Fuzzy–DDPG outperforms these classical achieving higher 95%, compared 85% for 88% average, while also minimizing steady-state oscillations. Additionally, reduces errors up 7.9% algorithms. These findings indicate combination logic deep learning provides more adaptive efficient solution, ensuring improved energy harvesting dynamically changing conditions.

Language: Английский

Citations

0
Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation DOI Creative Commons
Tousif Khan Nizami, Sasank Das Gangula, Ramanjaneya Reddy Udumula

et al.

Engineering Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 27, 2024

ABSTRACT This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates polynomial‐neural network with a backstepping strategy yield robust system for tracking motor. Such unification of online network‐based estimation and control, results effective regulation across wide load torque uncertainties, besides yielding promising transient steady‐state performance. stability entire closed‐loop is ensured through Lyapunov criterion. efficacy revealed extensive experimental investigation under various operating points during start‐up, step‐reference tracking, external step‐load disturbances. real‐time experimentation conducted on laboratory prototype 200 W, using dspace DS1104 board MPC8240 processor. obtained confirm improvement response by significantly reducing settling time steady state behavior no peak over/undershoots disturbances, contrast other similar works presented literature intended same application.

Language: Английский

Citations

0