Fault Tree-Based Reliability Assessment of a 132-kV Transmission Line Protection Scheme

The reliability of a power system network depends greatly on the performance of the protection system. Improving the reliability of the protection scheme of the transmission lines will enhance the overall reliability of the power system network. The focus of this paper is the improvement of the reliability of a power system transmission line using fault tree analysis (FTA). The paper considers the development of fault tree diagrams for the protection scheme of the 150km-long 132-kV transmission line in Northern Nigeria. The existing protection scheme is analyzed and compared with a proposed scheme equipped with a redundancy arrangement. And the result shows that the new scheme offers significant improvement (about 51%) in the availability of the line

A Comparative Study of Regression Analysis and Artificial Neural Network Methods for Medium-Term Load Forecasting

Load forecasting is an operation of predicting the future of load demands in electrical systems using previous or historical data. This paper reports the study on a medium-term load forecast carried out with load demand data set obtained from Covenant University campus in Nigeria and carry out comparative study of the two methods used in this paper. Methods/Statistical analysis: The regression analysis and Artificial Neural Network (ANN) models were used to show the feasibility of generating an accurate medium-term load forecast for the case study despite the peculiarity of its load data. The statistical evaluation methods used are Mean Absolute Percentage Error (MAPE) and root mean square error. Findings: The results from the comparative study show that the ANN model is a superior method for load forecast due to its ability to handle the load data and it has lower MAPE and RMSE of 0.0285 and 1.124 respectively which is far better result than the regression model. Application/Improvements: This result provides a benchmark for power system planning and future studies in this research domain

Development of a Prototype Robot Manipulator for Industrial Pick-and-Place Operations

In the industry today, continuous attempts to realize optimal efficiency and increased productivity have spawned much progress in the use of intelligent automated devices and machines to perform various operations and tasks. The thrust of this work is to present the development of a three-degree-of-freedom revolute robot manipulator amenable to pick-and-place operations in the industry. Appropriate kinematic equations of the manipulator are obtained, and then used to develop algorithms for locating predetermined positions of a small object in a customized workspace. An Arduino-based controller circuit is built to implement the algorithms, and servomotors are used to carry out independent joint control of the manipulator. The positions of the object are identified with the aid of light-dependent resistors (LDR). Besides, in order to aid easy fabrication of links and overall system assembly, a 3D model of the manipulator is designed. The results of the work, showing effective and satisfactory operation of the manipulator, are presented