A Stochastic Approach to Asynchronous Differential Power Processing for True Maximum Power Point Tracking of Photovoltaic Sub-Modules
MS student Felix Hsaio with advisor R. Pilawa-Podgurski
Photovoltaic (PV) systems produce maximum power when their modules (panels) and sub-modules are biased at the correct voltages and currents. These operating points change dynamically with factors such as insolation, shading, temperature, and panel age, so power converters must perform maximum power point tracking (MPPT). In differential power processing (DPP), only the differences in powers of adjacent PV modules/sub-modules are processed, rather than the full system power.
As the power converters then process less power, there is less loss and thus, overall efficiency increases. An MPPT algorithm utilizing the slopes of the power-voltage curves is investigated. Because these functions have only one relative extremum (the maximum power point), it is possible to determine whether the sub-module voltage is too high or too low based on this slope.
The algorithm then controls the voltage differences between sub-modules to bring them closer to their maximum power points.
Requiring communication for large systems can cause significant delays in the MPPT process, since the information must traverse the entire system. By introducing a stochastic process of idling times, it is possible for DPP converters to execute this algorithm asynchronously, with minimal chance of interference. Simulation results for a 4-panel system, shown in Figure 18, demonstrate a steady-state tracking efficiency of over 99.8%.
This project is funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000217.