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Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

The objective of the proposed work is to develop the maximum power point tracking controller and inverter controller by applying the adaptive Least mean square algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is simple and required less computational time. The adaptive LMS algorithm is applied to modify the perturbation and observation, maximum power point tracking controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The development of the inverter control law is done using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and later compared them to the actual grid currents. The comparison result is used to generate the switching gate pulses for inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in current and voltage is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is done by selecting step incremental values and sampling time.

Solar PV System, MPPT Controller, Inverter Controller, Adaptive Control Algorithm, Power Quality Issues

Nalini Karchi, Deepak Kulkarni. (2023). Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System. American Journal of Electrical Power and Energy Systems, 12(2), 32-39.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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