Abstract
This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power–voltage (P–V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.
Highlights
The output power of a photovoltaic module array is affected by daylight intensity and temperature and exhibits a nonlinear characteristic
This method is advantageous for simple interpretation but, when the atmospheric conditions change substantially, it cannot track the updated maximum power point
In a photovoltaic power generation system, photovoltaic modules are connected in parallel and series into an array to increase the output power of the system
Summary
The output power of a photovoltaic module array is affected by daylight intensity and temperature and exhibits a nonlinear characteristic. On the basis of the characteristic that the maximum power point is associated with similar voltage under various irradiations, the constant voltage method [1] tracks the maximum power point and involves easy control and simple calculation This method cannot track the updated maximum power point after the atmospheric conditions change substantially. The power feedback method [3] adopts the variation rates of the output power and voltage (dP/dV) to determine the maximum power point This method decreases energy consumption and exhibits high overall efficiency, but the accuracy of the sensory modules involved is undesirable. The work proposed in [16] suggests a simple relationship to predict the correct position of the global maximum power point This method can only be used to examine PV module array with two-peak output characteristics. The modified PSO method enabled effective identification of the global maximum power point of a photovoltaic module array with double-peak, triple-peak, and quadruple-peak P–V curves
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
