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Conceptual Design and Turbine Selection for a Micro Hydropower System Using Multi-Criteria Analysis
(Nigerian Journal of Engineering, 2023) Jimada-Ojuolape, Bilkisu; Balogun, Monsurat; Adesina, Lambe
Nigeria has grappled with a persistent electricity supply challenge characterized by surging demand, inadequate maintenance of power generation infrastructure, and a host of other issues. This ongoing predicament has given rise to frequent power outages, compelling citizens to resort to expensive alternatives like petrol and diesel generators. Moreover, a substantial number of Nigerian households rely on motorised boreholes for their water supply. Considering these circumstances, this study proposes an innovative solution in the form of a micro hydropower system that harnesses the existing tank-borehole setups within homes to generate electricity. The primary objective of this research is to define the specifications of the central subsystem, namely the turbine, by employing the multi-criteria analysis method, thereby facilitating the practical implementation of the proposed energy generation scheme. In particular, this study delves into an evaluation of two turbine options: Pelton and crossflow turbines. The results of the analysis revealed that the crossflow turbine emerges as the most suitable choice for this application, leading to the exclusion of other turbine alternatives such as the Francis and Turgo turbines.
Detection and Confirmation of Electricity Thefts in Advanced Metering Infrastructure by Long Short- Term Memory and Fuzzy Inference System Models
(NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, 2024) Otuoze, Abdulrahman; Mustafa, M; Sultana, U; Abiodun, E; Jimada-Ojuolape, Bilkisu; Ibrahim, O; Avazi-Omeiza, I; Abdullateef, A
The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.
Development of a GSM based Vehicle Demobilizer and Tracking System
(ABUAD Journal of Engineering Research and Development (AJERD), 2023) Jimada-Ojuolape, Bilkisu; Spencer, Oluwafunto; Balogun, Monsurat
Vehicle hijacking remains a pervasive global issue, posing heightened risks, especially when owners face armed hijackers. Concurrently, the disposal of old mobile phones contributes significantly to the escalating electronic waste (e-waste) challenge, emphasizing the imperative of responsible recycling practices. Addressing these concerns, this paper introduces a GSM-based vehicle demobilizer and tracking system, utilizing a cost-effective mobile phone connected to the device through a SIM card and operating with an NE 555 timer IC in bistable mode. In the event of a hijacking, a call to the mobile phone triggers the device, halting the vehicle. In the prototype design presented in this paper, the result of the output is represented by an LED to show that the circuitry actually works. When the device was tested, the LED illuminated when a call was placed to the mobile phone connected to the device which signifies that the circuit works and can achieve its purpose. The resulting design also shows that the vehicle owner can prevent his car from being stolen away even after it has been hijacked without self-endangerment and within a short time frame.
Enhancing Human Identification Systems Through Bi-modal Fusion Using Negative Selection Algorithm
(COVENANT JOURNAL OF ENGINEERING TECHNOLOGY, 2024-02-27) Balogun, Monsurat O; Jimada-Ojuolape, Bilkisu; Odeniyi, Latifat A
The Negative Selection Algorithm (NSA) is a computational technique inspired by the human immune system and widely used in various fields like intrusion detection, network security, data mining, and pattern recognition. However, its effectiveness in human identification has not been thoroughly explored. This study focuses on utilizing NSA for human image classification, specifically in a bi-modal system combining physiological traits (faces and fingerprints) and behavioral traits (signatures and voices), as well as a uni-modal system using all features. The research collected 2400 images from 200 individuals, pre-processed images, and salient features selected for easy classification. NSA was used for image classification in both bi-modal and uni-modal systems. The results demonstrated NSA's effectiveness, particularly in the bi-modal system. The biometric system that fused behavioral traits exhibited high accuracy, with true positive and true negative rates of 141% and 144%, respectively, and an overall accuracy of 95%. The system is based solely on physiological traits and achieved slightly lower accuracy rates at 89%. Furthermore, among the uni-modal systems, the voice-based system stood out with a true positive rate of 131% and an accuracy of 88.33%. These findings emphasize the advantages of combining different biometric traits, showcasing the potential for increased accuracy in identification systems. The study highlights NSA's role in enhancing classification accuracy, suggesting the developed biometric systems could significantly improve the performance and reliability of various integrated identification systems.
Reliability Enhancement of Synchrophasor-Based DTR system considering N-1 contingency for PMU placement
(IEEE, 2022-01-03) Jimada-Ojuolape, Bilkisu
The cyber power network has become more intelligent and efficient because of the integration of information and communication technology (ICT) infrastructure. By combining intelligent infrastructure such as phasor measurement units (PMUs), smart sensors, and other two-way communication and monitoring capabilities, this enhances reliability and sustainability. They do, however, have some disadvantages, such as component and communication network failures, as well as cyber breaches, which might compromise the existing network's reliability. Dynamic Thermal Rating (DTR) systems which is a smart grid technology, improves existing line ratings by allowing them to be increased based on several parameters without violating line safety requirements. Thus, this paper presents a DTR system that uses PMUs to implement Wide Area Monitoring (WAM) functions and investigates the impact of PMU failures and line outages on cyber-physical network reliability. The study uses a Monte Carlo simulation- based method to carry out the investigation on the IEEE RTS while considering the impacts of seven and thirteen PMUs. The test network is modified to emphasize the benefits of ICTs and the findings show that using PMUs enhances reliability greatly. While the use of seven PMUs offers reliability improvement by 53.15%, the use of 13 PMUs offers more significant improvement (by 95.79%) because a single line outage will not affect network observability.
Synchrophasor-Based Dynamic Thermal Rating System for Sustainable Cyber- Physical Power Systems
(CRC press, Tailor and Francis group, 2023) Jimada-Ojuolape, Bilkisu; Teh, Jiashen; Arowolo, Olaolu Micheal
Using Artificial Intelligence to Predict Animal Behaviour in Food Webs
(Transactions on Machine Learning and Artificial Intelligence, 2017) Aduragba, Tahir; Ahmed, Abdulkadir; Jimada-Ojuolape, Bilkisu; Ajani, Ayodeji; Adedoyin, Yinka
Overfishing of species in the marine life has caused oceans to become deserts at a fast pace. The population of specific species such as Cod and Haddock has reduced over the years. This has affected countries that hugely depend on them as a source of food. This study used Dynamic Bayesian Network (DBN) to predict animal behaviour in a food web. Two independent biomass surveys from the North Sea were used to learn predictive models and test them on the Northern Gulf Ocean. The resulting predictive model is expected to unveil useful information about what affects the population of fishes in the Northern Gulf Ocean. In addition, the predictive model was used to make predictions into the future about the effects of tampering with the population of specific species of fish in the same region. The focus was on the Cod species in the George’s Bank in relationship to species network in their food web. Looking at their biomass states and the effects it has on the hidden dependence when there is a change in their biomass states. Also, the different predictive models were used to evaluate species in the George’s Bank based on their performance. The result from the experiment shows that there is a hidden dependence, which is responsible for the collapse of species (Cod); due to the temperature or salinity of the ocean.

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