Showing papers by "Abid Yahya published in 2020"

Enhancement of dielectric properties of Baobab Oil and Mongongo Oil using cost-effective additive for power transformer insulating fluids

Abstract: This work aims to determine the applicability of Natural Esters (NE) like Baobab Oil (BAO) and Mongongo Oil (MGO) in power transformers. The dielectric property of Kerosene (KER) is utilized to decrease the viscosity of natural esters to promote better oil circulation in transformer tanks. Kerosene, being non-volatile and a good lubricant, protects alkali metals from oxidation/corrosion and its miscible property is useful as an oil thinner. The blending compositions used here are 1% KER:99% NE, 5% KER:95% NE and 10% KER:90% NE and the blends are subjected to dielectric testing using IEC (International Electrotechnical Commission) and ASTM (American Society for Testing and Materials) standards. Favourable temperature effects are observed with an improvement of 16% in breakdown strength and 61.9% reduction in kinematic viscosity for BAO while an improvement of 24% in breakdown strength and 56.4% reduction in kinematic viscosity are observed for MGO at 10% KER addition without affecting the dielectric performance of NE. In fact, temperature has a strong positive linear correlation with breakdown voltage and an equally strong negative linear correlation with viscosity for natural esters. Based on the results, kerosene is a promising vapour drying additive, and it is therefore recommended as a cost-effective additive to reduce oil viscosity and to minimize the amount of water saturation.

Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network (ANN)

Abstract: The performance of correlation between the dielectric parameters of Baobab Oil (BAO) and Mongongo Oil (MGO) is evaluated using Artificial Neural Network (ANN). The BAO and MGO naturally own high Un...

An Overview of Potential Liquid Insulation in Power Transformer

Abstract: The dielectric performance of mineral oil and natural esters is still a challenge that has been studied for many years to attain the best liquid insulation for power transformer. With the continued need for power transformers, the use and reuse of mineral oil in transformers has reached several thousand tonnes across the world. Therefore, industries are compelled to resolve the problem of pollution and non-biodegradability of petroleum-based mineral oil. Typically, there is no right solution; to that end researchers have recently shifted their focus towards edible oil markets to find the alternative insulating fluid. Although edible oils are a better choice over mineral oil, they still exhibit several detrimental properties as with mineral oil. The purpose of this survey is to contrast mineral oil-based transformer oils in the power industry and its alternatives. The review makes use of several research articles over a period of 32 years, covering and uncovering the facts on mineral oil, silicone oil and natural esters. In this view, the technology utilisation, efficiency improvement and inferences are accessible for current and future authors to select the right technology for the development of new insulating fluid.

Artificial intelligence-based load optimization in cognitive Internet of Things

Abstract: The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.

Improving energy detection in cognitive radio systems using machine learning

Abstract: This is an open-access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

Climate: Environmental Monitoring Using Wireless Sensor Network System

Abstract: This chapter gives an overview of environmental monitoring systems using wireless sensor network, big data, and Internet of Things (IoTs). This chapter outlines the effect of climate change on wild animals and also discusses innovations in response to climate change. Electric fences are commonly used to control and manage the movement of animals in game reserves, private game and farms to restrict intruders such as unwanted predators and humans from the bound area. Wireless sensor network-based system for intruder detection and monitoring is presented in this system to minimize the human-animal disputes in Africa. It is challenging to observe elephants monitoring since these huge animals travel for very long distances. The biggest challenge in the existing wireless-based anti-poaching system is the limited network or no coverage. As a result, the non-monitored animals are simply subjected to poaching. Taking advantage of WSN, an anti-poaching system is proposed in this chapter.

Optimizing Energy in Cooperative Sensing Cognitive Radios

Abstract: One of the viable solutions for effective spectrum management is cognitive radio. Single sensing systems are prone to interference; thus, the use of cooperative spectrum sensing. This paper aims to determine the required number of cognitive radios that would optimize the performance of a communication network in terms of energy utilization and bandwidth requirement. The cognitive sensing technique used was energy detection due to its reduced energy, computational, and communication resources requirement. The channel noise variance was set to −25 dB. Spectrum sensing was carried out at a frequency of 936 MHz and bandwidth of 200 kHz. Machine learning was first used to enhance the specificity of detection to minimize interference. Genetic Algorithm (GA) and Simulated Annealing (SA) were used to optimize the number of cognitive radios putting into consideration all constraints in the network. Genetic Algorithm gave a better result of two optimization techniques used. It gave an overall reduction of 40.74% in energy conserved without affecting the detection accuracy.

Agriculture: Wireless Sensor Network Theory

Abstract: This chapter provides the Internet of Things (IoTs)-based Smart Agriculture System. The system aims at improving agricultural production in Botswana by remotely monitoring farms of all types using the Internet of Things (IoTs). The system uses sensors to monitor different parameters for these living things to ensure that a proper environment is maintained at all times. This chapter gives a detailed explanation of the data loggers which transmit data wirelessly to a central point located on the farm. The central location is referred to as a gateway, and it is where the farm employees can visualize and analyze data. The on-farm network of sensors and gateway is linked to an online server through General Packet Radio Service (GPRS) and Satellite to allow for remote online data acquisition (DAQ). In this chapter, low cost and reliable wireless data acquisition system are implemented in real time at the banana field. The moisture stress, reducing the usage of excessive water, rapid growth of the weeds is achieved with the implementation of sensor-based site-specific irrigation. Internet of Things-based remote control of irrigation can also be achieved in the system. The implemented system can be used to transfer the fertilizer and other chemicals to the field with the help of adding new sensors and valves.

Livestock: Monitoring Foot and Mouth Disease Using Wireless Sensor Networks

Abstract: This chapter presents a system to detect foot and mouth disease as early as possible in a herd of cattle using wireless sensor networks. The system combines animal behavior and sensor values to determine the status of the cattle in terms of foot and mouth disease. The system combines animal behavior and sensor values to determine the status of the cattle in terms of foot and mouth disease. The system first measures an average response of a cow under normal circumstances with the focus being on the measurements of body temperature; distance covered; and feeding rate every 2 h. Data regarding the state of the cow is sent at certain time intervals to the farmers using ZigBee via a gateway. This chapter provides results of the trials performed on the project. A signal is acquired from a cow sensor node and transmitted to the Gateway which is interfaced to a computer running LABVIEW software as the graphical user interface of the system. The trials were done both for negatively and positively diagnosed cows.