A predictive algorithm for maximum power point tracking in solar photovoltaic systems through load management

In solar photovoltaic (PV) systems, maximum power point tracking (MPPT) is achieved when the impedance of the system load matches that of the PV array. The conventional approach to MPPT is to manage the output power of the PV array to adjust its impedance. This requires power electronics such as an inverter or charge controller, which induces cost and power loss. A PV system can also vary the system load by switching on and off parts of the load to match the impedances and find the maximum power point. It offers an inexpensive and efficient solution to MPPT as it does not require any conventional MPPT device. However, the demonstrated load-matching PV system is subject to power loss due to many unsuccessful switches. The algorithm in that system deployed a trial-and-error approach since the optimum switch points are typically unknown. This paper presents a predictive algorithm that can dynamically estimate the optimum switch points throughout the day using the powers measured from unsuccessful switches. It allows the system to track the maximum power point with minimum switches. The improved load-matching PV system has applications in PV-powered electrolytic hydrogen production.