Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3471
Title: Application of AI for frequency normalization of solar PV-thermal electrical power system
Authors: Estrice, Milton 
Sharma, Gulshan 
Akindeji, Kayode Timothy 
Davidson, Innocent Ewaen
Keywords: Solar PV-Thermal;Electrical power system;Frequency dynamics;Proportional;Integral;FLPI control.
Issue Date: Aug-2020
Publisher: IEEE
Source: Estrice, M., Sharma, G., Akindeji, K. and Davidson, I. E. 2020. Application of AI for frequency normalization of solar PV-thermal electrical power system. Presented at: 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). Available: doi:10.1109/icabcd49160.2020.9183885
Conference: 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) 
Abstract: 
Grid-connected solar-PV schemes have become
a significant part of the energy balance in the power system to
satisfy the growing request for clean, affordable energy. This
study attempts to link solar-PV generation with conventional
thermal power plants and to integrate the control zone
resulting in a hybrid solar PV-thermal electric power system
using an AC tie line. An analysis of the frequency dynamics for
varying load conditions of the interconnected system is studied.
Diverse approaches of proportional, integral, and
proportional-integral fuzzy logic built controllers are design
and tested in order to match the electric power with variable
loads of the system and hence to normalize the frequency ofthe
system in shortest possible time. A comparative analysis of the
design topologies is conducted out for the PV-Thermal scheme.
Results obtain from the implementation are shown to justify
the performance of proposed control efforts, using MATLAB
software tool
URI: http://hdl.handle.net/10321/3471
DOI: 10.1109/icabcd49160.2020.9183885
Appears in Collections:Research Publications (Engineering and Built Environment)

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