No abstract

Distribution system is very essential to load centre or service mains. This is because it is the final section of electric power system (EPS) to supply the consumers. Once this section is compromised, low voltage consumers will be denied of a reliable supply of electricity. One way to make supply to low voltage consumers reliable is by bringing generation close to them through distributed generators. However, location of distributed generator is very important with respect to the entire EPS security. In this study, power network structural topology (PNST) is proposed to optimally locate distributed generator within distribution system which results in minimal loss as well as maintaining voltage profile within constraint limits. 5bus IEEE test system was used as case study to show the feasibility of the proposed method. Results obtained for both test systems were validated through the results from power world simulation tool.In this section, power network structural topology (PNST) and power world simulation software are considered. The PNST is the proposed approach in this study while power world simulation software is used to validate the result obtained from PNST. Power network structural TopologyPower network structural topology (PNST) is an approach that considers the inherent characteristic of electric power circuitry in order solve power system problems. PNST approach proposed in this study, is based on the two-port network technique which has been reported in previous works [15][16][17].

Banking sectors have perpetual queues owing to the significance of the services they render to humanity and this poses a merging challenge of queue management in the execution their jobs and in the effective delivery of services to their customers. The study focuses on the role of queuing theory in the banking sectors thus far from pedigree of queuing theory till date. This study used the historical approach to juxtapose the likelihood of the impact of queuing in the various activities in the banking as seen in the regular applications to established areas of telecommunications systems and engineering. The outcome of this review espouses that a few approaches have been applied to sort the problems encountered in the banking sectors for needful improvement. Some of these approaches are ANN (Artificial Neural Network), BPR (Business Process Reengineering) M/M/1, M/G/1 and the Erlang B&C formulas for the management of excessive bank reserves and customer queues in terms of waiting times and economic cost. These approaches have been able to improve the performance of the banking sector to an extent, however there is still a couple of limitation regarded as external factors that varies from one banking system to another and lots of works are needed to further combat the problems faced by the banking sectors.

Energy is a key component in the overall growth of every nation. Insufficient energy delivery hinders political growth, restricts social growth, limits economic growth, and negatively affects the standard of living of citizens, both in urban and rural areas. Sufficient energy delivery increases food production, improves the standards of living of citizens, improves healthcare and enhancements in other human services, enhances industrial output, provides effective and efficient transportation not forgetting adequate shelter to the citizens of the nation. Currently, there is a significant level of deficiency in Nigeria's energy sector. This study seeks to address this issue by analysing cost implications of conventional energy source and solar energy source. This study brings to focus the payback period of a solar powered home and the return on investment that might accrue during this time to the residential homeowners. Furthermore, the best cost-effective load sharing option for residential owners considering two energy sources is also obtained.

The process of speech recognition is such that a speech signal from a client or user is received by the system through a microphone, then the system analyses this signal and extracts useful information from the signal which is converted to text. This study focuses on the design and implementation of a speech recognition system integrated with internet of thing (IoT) to control electrical appliances and door with raspberry pi as a core element. To design the speech recognition system, digital signal processing (DSP) technique and hidden Markov model were fully considered for processing, extraction and high predictive accuracy of the system. The Google application programming interface (API) was used as a cloud server to store command and give the system to assess to the internet. With 150 speech samples on the system, a high level of accuracy of over 80% was obtained.