Improvement of Model Predictive Control in Maximum Power Tracking in a Photovoltaic System using Fuzzy Control in the Presence of Uncertainty in the Model

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Samani Loghman; Mirzaei Rahmatollah

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Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2020 | Volume:10 | Issue:4 Page(s): 53-69

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Title

Improvement of Model Predictive Control in Maximum Power Tracking in a Photovoltaic System using Fuzzy Control in the Presence of Uncertainty in the Model

Author(s)

Samani Loghman | Mirzaei Rahmatollah | Issue Writer Certificate

Keywords

Maximum Power Point TrackingQ1

Model Predictive ControlQ1

Fuzzy controllerQ2

Photovoltaic SystemsQ1

Abstract

The P-V characteristic of a photovoltaic system is a nonlinear characteristic. The location and the value of the maximum power point in a Photovoltaic (PV) system depends on environmental conditions such as the intensity of solar radiation and the ambient temperature. To extract maximum power, a system including a DC-DC converter and a control method is required. Such a system is called maximum power tracking (MPPT), which is an essential part of a photovoltaic system. Model Predictive Control (MPC) is a type of control method which is used in MPPT. The model-based predictive control method detects the maximum power point using the equations of the dc-dc converter. In MPC, the system equations are ideally formulated. However, the values of the elements in the dc-dc converter change over time. There are two solutions to this problem. The first is to write real system equations. As a result, the formulations become very complicated and take up a large amount of microprocessor time and memory. Also, most of the time the exact values of the elements in the circuit are unknown. The second method is the use of additional sensors, which increases the cost of the system. In this article, a Fuzzy Model Predictive Control (FMPC) method is proposed for a Cuk converter to implement MPPT in Photovoltaic Systems. The proposed method obtains an improved performance and eliminates the need for the actual values of the elements. The method has been simulated in the MATLAB/Simulink workspace and its performance superiority has been demonstrated by comparing it against existing MPC and FL-MPPT approaches.

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APA: Copy

Samani, Loghman, & Mirzaei, Rahmatollah. (2020). Improvement of Model Predictive Control in Maximum Power Tracking in a Photovoltaic System using Fuzzy Control in the Presence of Uncertainty in the Model. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 10(4 ), 53-69. SID. https://sid.ir/paper/203082/en

Vancouver: Copy

Samani Loghman, Mirzaei Rahmatollah. Improvement of Model Predictive Control in Maximum Power Tracking in a Photovoltaic System using Fuzzy Control in the Presence of Uncertainty in the Model. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2020;10(4 ):53-69. Available from: https://sid.ir/paper/203082/en

IEEE: Copy

Loghman Samani, and Rahmatollah Mirzaei, “Improvement of Model Predictive Control in Maximum Power Tracking in a Photovoltaic System using Fuzzy Control in the Presence of Uncertainty in the Model,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 10, no. 4 , pp. 53–69, 2020, [Online]. Available: https://sid.ir/paper/203082/en

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