Abstract
Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The EN 50530 standard test is used to calculate the efficiency of the proposed method under varying weather conditions. The simulation results demonstrate that the proposed technique accurately tracks the maximum power point and avoids the drift problem, whilst achieving efficiencies of greater than 99.6%.
Highlights
In recent years, the global demand for energy has increased dramatically due to population growth
Taking an account of these, many types of Maximum power point tracking (MPPT) methods have been developed for PV systems, which can be divided into two types: classical methods, such as Perturbation and Observation (P&O) [5], Incremental Conductance (IC) [6], and Fractional Open Circuit Voltage [7]; and artificial intelligent techniques, for instance, Neural-fuzzy ANFIS [8], Fuzzy Logic (FL) [9], genetic algorithms (GAs) [10], particle swam optimism (PSO) [11], sliding mode [12] and Neural Networks (NNs) [13]
A novel maximum power point tracking technique based on fuzzy logic for a grid-connected PV system has been presented, which has the ability to track the MPP when there are big fluctuations of irradiation
Summary
The global demand for energy has increased dramatically due to population growth. The P&OMPPT is a popular method for PV-MPPT owing to its low cost and simple implementation [14] It poses many challenges, such as lower converging speed, high oscillation around a maximum power point MPP, and a drift problem associated with rapidly changing irradiance [5,15]. [31,32], the researchers presented an improved maximum power point tracking technique using the Fuzzy-IC algorithm for a PV array and fuel cells. [33] improved the conventional FL-MPPT method by adding fuzzy cognitive networks Whilst these proposals reduce the oscillations around the MPP and avoid the drift problem during changing irradiance, their implementation becomes more complex due to an additional step control unit. The EN 50530 standard test results for comparative analyses are provided in Section The EN 50530 standard test of MPPT efficiency, with Section Conclusion containing the conclusion
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