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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ayodeji Olalekan Salau
Afe Babalola University
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Analysis of a new voltage stability pointer for line contingency ranking in a power network Tayo Uthman Badrudeen; Funso Kehinde Ariyo; Ayodeji Olalekan Salau; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4266

Abstract

Improper management of reactive power in a power network could lead to voltage instability. This paper presents a well-detailed study on voltage instability due to violation of power equilibrium in a power network and introduces a new voltage stability pointer (NVSP). The proposed NVSP is developed from a reduced 2-bus interconnected network to predict the sensistivity of voltage stability to reactive power variation. The simulation results from MATLAB were evaluated on IEEE 14-bus test system. The contingency ranking was achieved by varying the reactive power on the load buses to its maximum loading limit. The maximum reactive power point was taken at each load bus and the critical lines were ranked according to their vulnerability to voltage collapse. The results were compared with other notable voltage stability indices. The results prove that the NVSP is an essential tool in predicting voltage collapse.
Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks Ayodeji Olalekan Salau; Tsehay Admassu Assegie; Adedeji Tomide Akindadelo; Joy Nnenna Eneh
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4020

Abstract

Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
Estimation of concrete compression using regression models Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Tayo Uthman Badrudeen
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4210

Abstract

The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).
Co-Authors Adedeji Tomide Akindadelo Crescent Onyebuchi Omeje Funso Kehinde Ariyo Joy Nnenna Eneh Radha Mothukuri Ravulapalli Lakshmi Tulasi Sangeetha Ganesan Sepiribo Lucky Braide Sepiribo Lucky Braide Shruti Jain Tamilarasi Suresh Tayo Uthman Badrudeen Tayo Uthman Badrudeen Tsehay Admassu Assegie