MUMUNI ADEMOLA QUADRI

Electronics and Computer Engineering

At the Electronics And Computer Engineering department office

Appointment on Visitation important

Topic: DEVELOPMENT OF A DECOUPLED MIMO-BASED PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER USING MULTI-VARIANT MACHINE LEARNING CLASSIFICATION APPROACHES

Description:

This work entails modelling a decoupled MIMO PID and optimizing it's gains using ML to intelligently select the optimal gain

#	Certificate	School	Year
1.	Ph.D (COMPUTER ENGINEERING)	FEDERAL UNIVERSITY, OYE-EKITI	2027

Intelligent Detection and Classification System for Different Windows of Voltage Dips

Voltage disturbance monitoring can usually be a complex task. The daily arrival of power quality sensitive equipments has made the provision of good power quality a real challenge across the globe. As a result, all power quality disturbances, especially voltage dip must be detected, classified and diagnosed accurately so that proper mitigation measures can be implemented. This paper presents an intelligent system for voltage dip detection and classification, which is based on the voltage dip windows defined by the South African utility ESKOM.  ESKOM groups voltage dips into five classes Y, X, S, T, and Z. The five dip classes were generated through simulation in DIgSILENT Power Factory 14.0 software, and the tests are carried out on a typical South African electricity network. The aim is to show the distinctive characteristics of each class and give the guidelines for the automatic processing of the dip types. It also shows the effects of renewable distributed generation (RDG) on voltage dip mitigation in electricity networks. The model is trained, tested and validated in Matlab environment using the neural network Toolbox.

MUMUNI QUADRI is a Assistant Lecturer at the Department of Electronics and Computer Engineering

MUMUNI has a Ph.D in COMPUTER ENGINEERING from FEDERAL UNIVERSITY, OYE-EKITI