Modeling and Simulation of A Low Cost Perturb& Observe and Incremental Conductance MPPT Techniques In Proteus Software Based on Flyback Converter

• Published in: IOP conference series. Materials Science and Engineering Vol. 881; no. 1; pp. 12152 - 12165
• Main Authors: Saleh, Ameer L., Obed, Adel A., Hassoun, Zainab Abdullah, Yaqoob, Salam J.
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.07.2020
• Subjects: Flyback converter; incremental conductance (INC) algorithm; MPPT controller; Perturb and observe (P&O) algorithm; Proteus software
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/881/1/012152

A photovoltaic (PV) panel that extracted the electrical energy from the sunlight energy is as nonlinear in nature with respect to the weather conditions such as solar irradiance and ambient temperature. Hence, the PV panel characteristics are exhibited a nonlinear P-V and I-V characteristics. Therefore, a one-point in the nonlinear PV characteristics at which the power is maximum. For this reason, the maximum power of the PV panel can be extracted using maximum power point tracking (MPPT) technique, the efficiency of the PV panel is increased. This MPPT technique is implemented using a suitable DC/DC converter linked with a corrected load resistance. In this paper, isolated flyback DC/DC converter is used to implement the MPPT techniques of low cost perturb& observe (P&O) and incremental conductance (INC) algorithms that represent the main controller in the PV system. The Proteus software is used to modeling and simulate the PV panel model that integrated with MPPT under variable cell temperature and solar irradiance and its control. Each technique is simulated with the PV model, flyback converter and a resistance load in order to the comparison between their performances. The results show that the P&O technique is tracked faster than the INC technique but the fluctuation is higher. On other hand, more power form the PV panel is obtained in INC technique which is lead to that the INC is more efficient than the P&O algorithm.