University of Central Florida Research Foundation Inc

Raghuram Ranganathan

Wasfy Mikhael

Issa E Batarseh

Nasser Kutkut

A method for controlling a photovoltaic (PV) panel in a PV system including a computing device that provides motor control signals and implements an iterative adaptive control (IAC) algorithm for adjusting an angle of the PV panel. The IAC algorithm relates P at a current time k (P(k)), an elevation angle of the PV panel at k (&#x3b8;(k)), P after a next step (P(k+1)) and an elevation angle of the PV panel at k+1 (&#x3b8;(k+1)). The algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates &#x3b8;(k+1) using P(k+1). The motor control signals cause the motor to position the PV panel to achieve &#x3b8;(k+1). A change in P resulting from the positioning is compared to a predetermined change limit, and only if the change in P is &#x2267; the change limit, again sensing P, and repeating the generating, calculating and positioning.

Method of adaptive solar tracking using variable step size

A system controller for position controlling a photovoltaic (PV) panel in a PV system including a power sensor sensing output power (P), and a motor for positioning the PV panel. The system controller includes a computing device having memory that provides motor control signals and implements an iterative adaptive control (IAC) algorithm stored in the memory for adjusting an angle of the PV panel. The IAC algorithm includes an iterative relation that relates P at current time k (P(k)), its elevation angle at k (&#x3b8;(k)), P after a next step (P(k+1)) and its elevation angle at k+1 (&#x3b8;(k+1)). The IAC algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates a position angle &#x3b8;(k+1) of the PV panel using the perturbed power value. The motor control signals from the computing device cause the motor to position the PV panel to achieve &#x3b8;(k+1).

Iterative adaptive solar tracking having variable step size

Complex adaptive methods for complex information processing employ optimal individual convergence factors for real and imaginary components of the weight vector. For wireless receivers operating on QPSK, a Complex IA-ICA performs better than existing Complex Fast-ICA methods in terms of accuracy and convergence speed, can process such complex signals in time-varying channels, and employs time-varying and time-invariant convergence factors, independent for the real and imaginary components of the system parameters, and provide individual or group system parameter adjustments. Such systems employ the within complex adaptive ICA with individual element adaptation (Complex IA-ICA). In adaptive beamforming, system identification and other adaptive systems based on the Least Squares method, complex least mean square methods, with optimally and automatically derived convergence factors, are employed and which perform much better in terms of convergence speed and accuracy, when compared to the traditional Complex LMS and Block Complex LMS methods.

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Cookeville, TN, United States of America

Raghuram Ranganathan