This study was conducted to proposea hierarchical temporal memory (HTM) approach for fault detection in the Onitsha-Alaoji transmission line in Nigeria. Using a mixed research method, the study employed the Hawkins HTM model with two objectives and their corresponding research questions. The study gathered primary and secondary data to detect and evaluate faults in the Onitsha-Alaoji transmission line in Nigeria using HTM and compares its efficacy to current fault detection methods. With the use of simulation and descriptive methods of data analysis, results showed that partial discharge (PD) is the fault type that is being detected and it is commonly found as a fault leading to transmission line errors. More so, fault detection simulations were conducted at 40 km using typical power spectral density analysis. The first fundamental shifted from about 10 kHz to roughly 13 kHz during a fault. The HTM model outperformed sequence learning methods, resulting in a 90% mean test classification accuracy (CA) over extreme learning machine(ELM) and online sequential learning–extreme learning machine (OS-ELM), with OS-ELM performing poorly.The study concluded and recommended that the proposed HTM model be used to identify various PD fault types that plague the Onitsha-Alaoji transmission line in Nigeria. With the increased efficacy and reliability of the proposed model compared to existing methods, it is recommended for future implementation in this transmission line and potentially other fault-prone power transmission lines in Nigeria.
This study was conducted to optimize the integration of solar-photovoltaic-distributed energy resources (SPVDERs) within the Nigerian power system networks using an AI-based Particle Swarm Optimization (PSO) Algorithm. By employing a mixed research method, primary and secondary data were gathered to calculate flow analysis, NR method's equations, PSO's position update model, particle swarm optimizer algorithm, and application modeling including Solar-PV DER modeling. The AI-based PSO algorithm design was developed for optimizing SPV-DER integration in Nigerian power system networks, and key parameters and variables that needed consideration were identified. The study also established how the performance of the AI-based PSO algorithm could be evaluated and compared with other optimization techniques for SPV-DER integration within Nigerian power system networks. The study's results showed that voltage limits were within acceptable ranges, and solar power contributions were estimated at 880.10MW with 46,718 panels needed. The study concluded and recommended that investing in AI-powered tools for efficient power distribution; monitoring and resource optimization for sustainable energy sources would optimize performance and unleash Nigeria's sustainable energy potential.
This study was conducted to develop and evaluate the Optimal Poly-1-Order (OP-1) model for approximating solar photovoltaic (PV) power generation. Using a mixed research method, the study employed Ibrahim’s simulation and prediction of grid-connected PV system theory with two objectives and their corresponding research questions. The study gathered primary and secondary data to approximate the implementation of a solar-PV system with an OP-1 model for generating electricity: optimizing energy production, load demands, and financial viability in the medical hostel facility of the University of Port Harcourt, Rivers State, Nigeria. With the use of simulation and descriptive methods of data analysis, results showed that the lighting system had 400 lights, each with 12W power. It operated for a total of 18 hours. Daily power consumption was 36,400 Wh. More so, it showed that 60 fans with 100W power were used during the same hours, resulting in a daily power usage of 108,000 Wh. Based on a comprehensive economic evaluation, the OP-1 solar-PV system was found to be economically viable for powering the medical hostel. The system met electricity demand, resulting in a remarkable 407% ROI and substantial savings for the grid, despite a lower optimized size of 193kW compared to the base peak generation of 383.90k. The study concluded and recommended that the proposed OP-1 Solar-PV power plant can meet the facility's electricity needs with a peak generation of 383.90kW and detailed energy analysis. Deploying this efficient solar-PV setup guarantees reliable and green electricity for the Medical Hostel, slashing the campus's carbon footprint and grid reliance.
As the demand for renewable energy continues to rise, it becomes crucial to discover effective ways to enhance grid-connected photovoltaic (PV) battery energy storage systems. The Institute of Petroleum Studies (IPS) complex at the University of Port Harcourt in Rivers State, Nigeria, embarked on a quest to determine the optimal approach for optimizing their PV battery energy storage system. This research aimed to fulfill this need by employing a diverse research methodology, incorporating the innovative MOALO theory. To begin with, the research gathered primary and secondary data to construct models for the power grid, solarPV, and battery. Furthermore, it meticulously analyzed the load profile of the IPS complex, at the University of Port Harcourt. Leveraging the power of the MOALO theory.The researchers accurately sized the system and evaluated the potential outcomes of simultaneously interconnecting all loads. To gauge the system's performance, there was a calculation of various parameters such as economics, random walk, boundary conditioning, entrapping ants, and ant trap development. Remarkably, the outcome showed that the fitness responses between the two trial runs, facilitated by the integration of MOALO, were strikingly similar, revealing a typical concaveconnected shape, which is characteristic of a multi-objective solver. The optimal multi-objective cost implication of the system was estimated to be around 4,300 USD, with a power mismatch performance of approximately -1.7819e+09. Based on these compelling findings, the study concluded that MOALO serves as an impressive optimization tool capable of minimizing power mismatches and optimizing costs. Moreover, it recommended the generation of excess power as a means to achieve sustainability.
The inclusion of hydroelectric power is crucial to Nigeria's overall energy mix, playing a significant role in electricity generation. However, the Shiroro hydro plant, one of the main facilities located on the Kaduna River, is currently facing operational obstacles due to deteriorating infrastructure and inadequate maintenance practices. To overcome these challenges and improve efficiency within Nigeria's hydroelectric power sector, a hybrid-optimization approach has been proposed. This study sought to enhance the efficiency of the Shiroro hydro plant by implementing this innovative method. To achieve our objectives and address pertinent research questions, a mixed research method combining primary and secondary data was employed. The analysis included hydropower modeling and hydro-turbine input-output modeling. Three optimizer models, namely the particle swarm optimizer (PSO), Ant colony optimizer (ACO), and Artificial bee colony optimizer (ABCO), were utilized to formulate objective functions and task representations. The study involved comparing the daily output and fitness response of the Shiroro hydro plant through swarm optimizer iterations. The findings revealed a clear correlation between the turbine's power output and the water flow rate and water column height, suggesting that altering these factors could significantly improve the plant's performance. The comparison of the PSO, ACO, and ABCO models demonstrated that PSO and ABCO generated optimal or near-optimal solutions, while ACO produced suboptimal results. Consequently, the study concluded that enhancing the Shiroro hydro plant's output was feasible by increasing the water flow rate and column height. Additionally, the utilization of PSO and ABCO models proved to be an effective means of accurately predicting the turbine's output. As a result, the study recommended the integration of hybrid optimization techniques to monitor and identify any deviations in the Shiroro hydro plant's daily power output. This approach would enable prompt maintenance to be carried out, preventing significant damage to the plant. Ultimately, this research contributes valuable insights into improving the efficiency and performance of Nigeria's Shiroro hydro plant.