Zobrazeno 1 - 10
of 348
pro vyhledávání: '"A. Harrag"'
Publikováno v:
Frontiers in Energy Research, Vol 10 (2022)
The measurement of solar radiation and its forecasting at any particular location is a difficult task as it depends on various input parameters. So, intelligent modeling approaches with advanced techniques are always necessary for this challenging ac
Externí odkaz:
https://doaj.org/article/dfa9f82d968147b8948824efcbb4d823
Publikováno v:
Revue des Énergies Renouvelables, Vol 22, Iss 1, Pp 85-91 (2019)
In this paper, we propose a particle swarm optimization technique for the characterization of the equivalent electrical model of photovoltaic cell. The three diodes model with nine parameters is considered. The particle swarm optimization algorithm i
Externí odkaz:
https://doaj.org/article/7c509dd4f08144e68c9882ec9856e395
This paper presents a brief comparison for voltage and current controllers implementation in both stationary and dynamic reference frame for a microgrid (MG) application. Diagrams of implementations are reviewed and the simulation results are present
Externí odkaz:
http://arxiv.org/abs/2404.00040
Publikováno v:
Revue des Énergies Renouvelables, Vol 21, Iss 3, Pp 487-493 (2018)
This paper proposes a novel single sensor maximum power point tracking method for PV system composed of Solarex MSX-60W PV panel operating at variable atmospheric conditions and DC-DC boost converter controlled using the proposed MPPT that uses only
Externí odkaz:
https://doaj.org/article/c84ee62f27224b09aa9a9b10d961b078
Autor:
A. Harrag, S. Messalti
Publikováno v:
Revue des Énergies Renouvelables, Vol 21, Iss 2, Pp 303-314 (2018)
In this paper, a genetic algorithm is used for the optimization and tuning of PI controller parameters in order to improve the performance of SVC compensator in both dynamic and static response. The efficiency of the proposed method has been studied
Externí odkaz:
https://doaj.org/article/3f67b05f0d5d42dbb38cfcea3effc88f
Autor:
A. Harrag, S. Messalti
Publikováno v:
Revue des Énergies Renouvelables, Vol 21, Iss 1, Pp 129-139 (2018)
In this paper, an indirect hybrid fuzzy-P&O variable step size MPPT controller has been proposed. The classical fixed step P&O MPPT algorithm is the most widely used due to its simplicity and easy implementation. However, this algorithm presents seve
Externí odkaz:
https://doaj.org/article/6b06f79960dd480f9dc1f0538e891aee
Publikováno v:
Revue des Énergies Renouvelables, Vol 20, Iss 4, Pp 555-571 (2017)
In this paper, a new modified variable step P&O maximum power point tracking algorithm using Proportional-Integral-Derivative controller tuned by particle swarm optimization is proposed. The classical fixed step P&O algorithm has good performances du
Externí odkaz:
https://doaj.org/article/d81f3787484e49f18742e2c6b944536d
Autor:
A. Harrag, S. Messalti
Publikováno v:
Revue des Énergies Renouvelables, Vol 20, Iss 2, Pp 295-308 (2017)
With the world’s fastest growing rate and the prominent part of installed renewable energy sources, it is clear that wind is now a mainstream source of energy supply that will play a leading role in the proclaimed fight against climate change. But
Externí odkaz:
https://doaj.org/article/bafc72a9be9441a18d2ff1d057fdc65e
Publikováno v:
Revue des Énergies Renouvelables, Vol 19, Iss 3, Pp 487-495 (2016)
This paper deals with the development of neural network IC-based variable step size MPPT controller. The proposed neural network MPPT controller is firstly, developed in offline mode required for testing different set of neural network parameters and
Externí odkaz:
https://doaj.org/article/0d86d6be188e479d9a2ac6a6a06ec25e
Autor:
A. Harrag, S. Messalti
Publikováno v:
Revue des Énergies Renouvelables, Vol 18, Iss 4, Pp 701 – 711-701 – 711 (2015)
In this paper, we propose a new technique based on genetic algorithm for the extraction of electrical parameters (the saturation current, the serial resistance, the parallel resistance and the ideality factor). The models with five, seven and nine pa
Externí odkaz:
https://doaj.org/article/f8e9d002dfad4a57b7df91f50ae12c8f