An improved MPPT control strategy based on incremental conductance algorithm
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(2020) 5:14
ORIGINAL RESEARCH
Protection and Control of Modern Power Systems
Open Access
An improved MPPT control strategy based on incremental conductance algorithm Liqun Shang*, Hangchen Guo and Weiwei Zhu
Abstract PV power production is highly dependent on environmental and weather conditions, such as solar irradiance and ambient temperature. Because of the single control condition and any change in the external environment, the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate, resulting in misjudgment. To improve the efficiency and economy of PV systems, an improved incremental conductance algorithm of MPPT control strategy is proposed. From the traditional incremental conductance algorithm, this algorithm is simple in structure and can discriminate the instantaneous increment of current, voltage and power when the external environment changes, and so can improve tracking efficiency. MATLAB simulations are carried out under rapidly changing solar radiation level, and the results of the improved and conventional incremental conductance algorithm are compared. The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence. It not only optimizes the system, but also improves the efficiency, response speed and tracking efficiency of the PV system, thus ensuring the stable operation of the power grid. Keywords: Photovoltaic array, MPPT, Phenomenon of misjudgment, Incremental conductance algorithm
1 Introduction With the deterioration of the environment and the depletion of conventional energy sources, solar energy as a new type of green energy has attracted widespread attention throughout the world [1, 2]. Photovoltaic (PV) power generation is the most common form of solar energy generation. The output power of a single PV cell, which is the basic unit of PV power generation, is relatively low. In practical applications, given the requirements of voltage and power, it is necessary to combine multiple PV modules in series and parallel to form a PV array. PV array output current and voltage are affected by meteorological conditions (irradiance, temperature etc.) and thereby appear to be nonlinear. Its output power also changes continuously. Therefore, how to adjust the load characteristics so that the system can output the maximum power in
real time, namely, to achieve the maximum power point tracking (MPPT), is particularly important in PV systems [3–5]. MPPT methods mainly include traditional methods and intelligent control algorithms [6]. Traditional MPPT methods include hill climbing [7, 8], perturbation and observation [9, 10], and incremental conductance methods [11, 12], while intelligent control algorithms include fuzzy-logic [13], artificial neural networks [14], flower pollination algorithm [15], and particle swarm optimization [16, 17]. Although the effectiveness of intelligent control algorithms has been verified by experiments in many cases, the algorithms still have the disadvan
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