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Showing 2 results for ahmadnia
Vahidreza Jafarinia, Mohsen Ahmadnia, Ahmad Hajipoor,
Volume 8, Issue 2 (3-2022)
Abstract
In this paper, a new adaptive model predictive control based on Laguerre functions is proposed for the load-frequency control problem of a multi-area power system, in which the estimation of the internal model of the power system is updated online using the recursive least squares method. The use of the adaptive reduced-order internal model in the structure of model predictive control is the innovation of this research. In the studied system, the controller of each area is designed independently so that the stability of the overall closed-loop system is guaranteed. Numerical simulations for a three-area power system are carried out to validate the effectiveness of the proposed scheme and the results were compared with those of conventional model predictive control (MPC) and proportional-integral-derivative control (PID). The simulation results show that the proposed scheme performs better than PID and MPC in rejecting step load disturbance (with respect to nominal and uncertain parameters) and nevertheless, thanks to the use of the reduced-order model and Laguerre functions, reduces the computational burden significantly compared to conventional MPC.
Esmaeil Bahmani, Dr Mohsen Ahmadnia, Dr Hossein Sharifzadeh,
Volume 9, Issue 2 (3-2023)
Abstract
Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.
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| نشریه سامانههای غیرخطی در مهندسی برق در خصوص اصول اخلاقی انتشار مقاله، از توصیههای «کمیته بینالمللی اخلاق نشر» موسوم به COPE و «منشور و موازین اخلاق پژوهش» مصوب معاونت پژوهش و فناوری وزارت علوم، تحقیقات و فناوری تبعیت میکند. |
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