A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. (1st March 2017)

• Record Type: Journal Article
• Authors: Ram, J. Prasanth; Babu, T. Sudhakar; Dragicevic, Tomislav; Rajasekar, N.
• Is Part Of: Energy conversion and management. Volume 135(2017)
• Page Start: 463
• Page End: 476
• Publication Date: 2017-03-01
• Subjects: BPFPA - Bee Pollinator Flower Pollination Algorithm -- PV - Photo Voltaic -- GA- Genetic Algorithm -- FPA - Flower Pollination Algorithm -- HS - Harmony Search; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.12.082
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A new Bee Pollinator Flower Pollination Algorithm (BPFPA) is proposed for Solar PV Parameter extraction. Standard RTC France data is used for the experimentation of BPFPA algorithm. Four different PV modules are successfully tested via double diode model. The BPFPA method is highly convincing in accuracy to convergence at faster rate. The proposed BPFPA provides the best performance among the other recent techniques. Abstract: The inaccurate I-V curve generation in solar PV modeling introduces less efficiency and on the other hand, accurate simulation of PV characteristics becomes a mandatory obligation before experimental validation. Although many optimization methods in literature have attempted to extract accurate PV parameters, all of these methods do not guarantee their convergence to the global optimum. Hence, the authors of this paper have proposed a new hybrid Bee pollinator Flower Pollination Algorithm (BPFPA) for the PV parameter extraction problem. The PV parameters for both single diode and double diode are extracted and tested under different environmental conditions. For brevity, the I 01, I 02, Ipv for double diode and I 0, Ipv for single diode models are calculated analytically where the remaining parameters 'Rs, Rp, a 1, a 2 ' are optimized using BPFPA method. It is found that, the proposed Bee Pollinator method has all the scope to create exploration and exploitation in the control variable to yield a less RMSE value even under lower irradiated conditions. Further for performance validation, the parameters arrived via BPFPA method is compared with Genetic Algorithm (GA), Pattern Search (PS), Harmony Search (HS), Flower Pollination Algorithm (FPA) and Artificial Bee Swarm Optimization (ABSO). In addition, various outcomes of PV modeling and different parameters influencing the accurate PV modeling are critically analyzed.