Evaluation of neural network based real time maximum power tracking controller for PV system
- Kumamoto Univ. (Japan). Dept. of Electrical Engineering and Computer Science
- Clarkson Univ., Potsdam, NY (United States). Dept. of Electrical and Computer Engineering
This paper presents a neural network based maximum power tracking controller for interconnected PV systems to commercial power sources. The neural network is utilized to identify the optimal operating voltage of the PV system. The controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV system to the identified optimal one, which yields the maximum power generation. The controller is a PI type one. The proportion an the integral gains are set to their optimal values to achieve the fast response and also to prevent the overshoot and also the undershoot. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV system. Because of the accurate identification of the optimal operating voltage of the PV system, more than 99% power is drawn for the actual maximum power.
- OSTI ID:
- 147931
- Report Number(s):
- CONF-950103-; ISSN 0885-8969; TRN: IM9601%%37
- Journal Information:
- IEEE Transactions on Energy Conversion, Vol. 10, Issue 3; Conference: Winter meeting of the IEEE Power Engineering Society, New York, NY (United States), 29 Jan - 2 Feb 1995; Other Information: PBD: Sep 1995
- Country of Publication:
- United States
- Language:
- English
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