Ann-Trained Using Bat Algorithm for Modeling University-Based Energy Consumption on Short Term Basis

Adequate planning and right decision making in the energy sector lies on accurate forecasts of the load demand. In this paper, Artificial Neural Network (ANN) trained via Bat algorithm was employed for short term load projection of University of Ibadan, Nigeria. Daily load demand of the study area was obtained from the log records. The neural network was built, trained with historical data gotten from the premier University in Nigeria and then used to predict 24 hour’s load demand from Dec., 1st to Dec., 7th, 2016. The experimental results indicated that the proposed method achieved a Mean Absolute Percentage Error (MAPE) of 6.60% and a Mean Percentage Error (MPE) of 4.17%. This research finds application in scheduling of power demand in power system. Keywords: Artificial Neural Network, Bat algorithm, Mean Absolute Percentage Error, Mean Percentage Error, Short-term forecasting, DOI: 10.7176/JIEA/10-2-04 Publication date:March 31st 202

Voltage Rise Problem in Distribution Networks with Distributed Generation: A Review of Technologies, Impact and Mitigation Approaches

Energy demand has constantly been on the rise due to aggressive industrialization and civilization. This rise in energy demand results in the massive penetration of distributed generation (DG) in the distribution network (DN) which has been a holistic approach to enhance the capacity of distribution networks. However, this has led to a number of issues in the low voltage network, one of which is the voltage rise problem. This happens when generation exceeds demand thereby causing reverse power flow and consequently leading to overvoltage. A number of methods have been discussed in the literature to overcome this challenge ranging from network augmentation to active management of the distribution networks. This paper discusses the issue of voltage rise problem and its impact on distribution networks with high amounts of distributed energy resources (DERs). It presents different DG technologies such as those based on conventional and unconventional resources and other DERs such as battery storage systems and fuel cells. The study provides a comprehensive overview of approaches employed to curtail the issue of voltage increase at the point of common coupling (PCC), which includes strategies based on the network reinforcement methodology and the active distribution network management. A techno-economic comparison is then introduced in the paper to ascertain the similarities and dissimilarities of different mitigation approaches based on the technology involved, ease of deployment, cost implication, and their pros and cons. The paper provides insights into directions for future research in mitigating the impact of voltage rise presented by grid-connected DGs without limiting their increased penetration in the existing power grid

Internal Model Control Tuned Proportional Integral Derivative for Quadrotor Unmanned Aerial Vehicle Dynamic Model

In recent times, there are has been growing substantive attention to the quadrotor Unmanned Aerial Vehicle (UAV) stability control. However, inherent nonlinearity is a major challenge with this control technique, this paper, therefore, developed a PID based Internal Model Control (IMC) method for the dynamic model of quadrotor UAV. The versatility and simplicity of the Proportional-Integral-Derivative (PID) controller enable it to enjoy wide usage and acceptability as stability control methods for the unmanned aerial vehicles. The aim of this paper is to use the PID controller with IMC to control a UAV. The proposed approach - IMC-PID control method -was simulated using MATLAB software and X-plane flight simulator. Thereafter, a comparative analysis of the IMC-PID control method with Chien-Hrones-Reswick, Cohen-coon, and Ziegler Nichols based PID Controllers was done using pitch and altitude as performance metrics. Keywords: Internal Model Control,MATLAB/Simulink, Proportional Integral Derivative, Quadrotor, Unmanned Aerial Vehicle (UAV),X-Plane, DOI: 10.7176/CTI/9-01 Publication date: April 30th 202

REAL TIME ASSESSMENT OF 500-KVA, 11/0.415 KV DISTRIBUTION TRANSFORMER SITUATED AT BELLS UNIVERSITY OF TECHNOLOGY, OTA, OGUN STATE USING FLUKE 435 SERIES II

Distribution transformer being major equipment in utility companies deserves to be routinely scrutinized for its performance with a view to ensure continuous supply of energy to end-users as well as to sustain improved revenue collection by the utility company. Presented in this paper is the real time assessment of 500-kVA, 11/0.415 kV distribution transformer situated at Bells University of Technology, Ota, Ogun State using power quality and energy analyzer equipment. The parameters measured on real time include phase to neutral voltage, phase to phase voltage, root mean square and peak current, frequency, active power, reactive and apparent power and line power factor. The assessment revealed wide variation of system voltages far from the standard, however, the line power factor and as well as frequency of operation was observed to be within the standard. Based on this analysis, several findings and appropriate recommendations were suggested to improve the supply of energy in the study area. It is hope that the findings of this research will be of immense benefit to distribution engineers at the Department of works in Bells University of Technology, Ota for field compliance

REAL TIME ASSESSMENT OF 500-KVA, 11/0.415 KV DISTRIBUTION TRANSFORMER SITUATED AT BELLS UNIVERSITY OF TECHNOLOGY, OTA, OGUN STATE USING FLUKE 435 SERIES II

Distribution transformer being major equipment in utility companies deserves to be routinely scrutinized for its performance with a view to ensure continuous supply of energy to end-users as well as to sustain improved revenue collection by the utility company. Presented in this paper is the real time assessment of 500-kVA, 11/0.415 kV distribution transformer situated at Bells University of Technology, Ota, Ogun State using power quality and energy analyzer equipment. The parameters measured on real time include phase to neutral voltage, phase to phase voltage, root mean square and peak current, frequency, active power, reactive and apparent power and line power factor. The assessment revealed wide variation of system voltages far from the standard, however, the line power factor and as well as frequency of operation was observed to be within the standard. Based on this analysis, several findings and appropriate recommendations were suggested to improve the supply of energy in the study area. It is hope that the findings of this research will be of immense benefit to distribution engineers at the Department of works in Bells University of Technology, Ota for field compliance

Analysis of the Impact of Clustering Techniques and Parameters on Evolutionary-Based Hybrid Models for Forecasting Electricity Consumption

Electricity is undeniably one of the most crucial building blocks of high-quality life all over the world. Like many other African countries, Nigeria is still grappling with the challenge of the energy crisis. However, accurate prediction of electricity consumption is vital for the operation of electric utility companies and policymakers. In response, this study underlines the application of hybrid modelling techniques for the accurate prediction of electricity consumption, using Lagos districts, Nigeria, as a case study. To begin with, this research investigates the performance of three evolutionary algorithms — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) — to optimize the parameters of adaptive network-based fuzzy inference systems (ANFIS). In addition, the impact of renowned clustering techniques such as grid partitioning (GP), fuzzy c-means (FCM), and subtractive clustering (SC) on other pivotal key hyperparameters of the ANFIS was examined and analyzed. Furthermore, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models that are based on six different variants of PSO. The efficacy of the proposed model was evaluated using four standard statistical measures. Finally, the results showed that the combination of the ANFIS approach and PSO under an SC approach and clustering radius of 0.6 delivered the best forecast scheme with the highest accuracy of the MAPE (8.8418%), the MAE (872.1784), the CVRMSE (10.7895), and the RMSE (1.0945E+03). The simulation results were analyzed and compared to other approaches, revealing that the suggested model is better