Solar photovoltaic energy system analysis using linear quadratic regulator and model reference adaptive control techniques
| dc.contributor.author | Abraham, Idaresit Isaac | |
| dc.date.accessioned | 2026-04-15T16:35:45Z | |
| dc.date.available | 2026-04-15T16:35:45Z | |
| dc.date.issued | 2023-12 | |
| dc.description | This thesis is for the award of Master of Engineering (M.ENG.) Degree in Electrical/Electronic Engineering (Control Engineering option) | |
| dc.description.abstract | This work presents solar photovoltaic energy system analysis using incorporated linear quadratic regulator (LQR) and model reference adaptive control (MRAC). Solar photovoltaic (PV) energy system is one of the renewable energy applications, which operates by tracking energy from the sun and converting it into useful electrical energy. However, harnessing the generated energy has been the major concern of the engineers hence the adoption of various control strategies for efficient control of the generated power. The idea is to transfer the optimal generated power to the output considering the fact that PV energy produced may have been affected by irradiation and temperature conditions. To avoid the unstable output power, there is need to deploy an efficient control scheme. Most of the conventional control techniques are not optimal to resolve power inaccuracies in the system and they have shortcomings. The control methods employed in this work involved the combination of LQR and MRAC to investigate the control performance. The results obtained by simulation on MATLAB/Simulink software, show that the average tracking efficiency for LQR control scheme was 95.92% compared to MRAC Type with an average tracking efficiency of 73.41% while the integrated MRAC-LQR control has a tracking efficiency of 94.45%. Rise time was used to measure the speed of convergence for the control schemes. LQR has a rise time of 1.95ms while MRAC and MRAC-LQR have 1.323s and 0.156ms respectively. LQR control does not adapt to the changing conditions of the environment but MRAC and MRAC-LQR have adaptive features, which enables the system adjust itself at varying environmental conditions. Moreover, LQR-MRAC accepts a higher adaptation gains ) compared to the MRAC-type with. The duty cycles for LQR, MRAC and LQR- MRAC were also determined to be approximately 0.6, 0.7 and 0.6 respectively. Considering its unique features such as appreciable tracking efficiency, rise time, duty cycle and the ability to adapt even at higher adaptation gain values, LQR- MRAC control scheme is recommended over the other two (LQR and MRAC). | |
| dc.identifier.citation | Abraham, I. I. (2023). Solar photovoltaic energy system analysis using linear quadratic regulator and model reference adaptive control techniques [Unpublished Master's Thesis]. Federal University of Technology, Owerri, Nigeria | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/2663 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technology, Owerri | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Adaptive control scheme | |
| dc.subject | improvement | |
| dc.subject | linear quadratic regulator | |
| dc.subject | maximum power point | |
| dc.subject | model reference | |
| dc.subject | solar photovoltaic energy | |
| dc.subject | tracking | |
| dc.subject | Department of Electrical/Electronic Engineering | |
| dc.title | Solar photovoltaic energy system analysis using linear quadratic regulator and model reference adaptive control techniques | |
| dc.title.alternative | ||
| dc.type | Master’s Thesis |