Differential Protection of a Three-Phase Power Transformer Using Hall Effect Current Transducer

In a power system, transformers and other electrical equipment need to be protected not only from short circuit, but also from abnormal operating conditions, such as over loading, and differential fault protection. The differential protection relay works on the princ1ple that in a healthy system, the current leaving a circuit is equal to the current entering the circuit. The differential protection can also be applied to a transformer (even though the primary and secondary currents are not equal), by rating the CTs according to the transformation ratio. In a power system, the differential relay should operates only in its specified protection zone, and not for out of its protection zone, when short circuit fault occurs. Differential protection zone for a transformer is in the limited zone between transformer primary side CTs and transformer secondary side CTs. If a short circuit fault occurs in this zone, then the differential relay will operate to protect transformer not to be damaged by the high circuit current. This work has been focused on construction, normal operation of differential relay and on the problem when differential relay is functioning outside of its protection zone and a way of solving the problem, further to test its function by creating faults on nearby power system. This work has shown that if the current ratio of current transducers are not matched with the current of transformer, therefore it would cause the differential relay functions even though the faults occur outside the relay protection zone

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against state-of-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems.Comment: Accepted in Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2023

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare

This is an accepted manuscript of an article published by Elsevier in Internet of Things on 18/01/2021, available online at: https://doi.org/10.1016/j.iot.2020.100353 The accepted version of the publication may differ from the final published versionContinuous Glucose Monitoring Systems (CGMs) device is the most developed technology, which has reshaped manual diabetes management with smart features having sensor, transmitter and monitor. However, the number of users for CGMs device is still very low compared to existing manual systems although this device provides a smart landmark in blood glucose monitoring for diabetes management. Consequently, the aspire of the assessment is to explore the factors that influence users’ intention to adopt CGMs device on the Internet of Things (IoT) based healthcare. This paper provides an adoption model for CGMs device by integrating some factors from different theories in existing studies of wearable healthcare devices. The proposed adoption model also examines current factors as a guideline for the users to adopt the CGMs device. We have collected data from 97 actual CGMs device users. Partial least square and structural equation modelling were involved for measurement and structural model assessment of this study. The experiential study specifies that interpersonal influence and trustworthiness are the strong predictors of attitude toward a wearable device, which shows significant relationships to use for CGMs device’s adoption. Personal innovativeness shows no significant relationship with attitude toward a wearable device. Besides, self-efficacy has no direct influence on a person’s health interest where heath interest directly influences users’ intention to use CGMs device. Moreover, perceived value is not found to be significant for measuring intention to use CGMs devices. The results from this research provide suggestions for the developers to ensure users’ intention to adopt CGMs device

Reduction of magnetizing inrush current in power transformers

Stability and security of the transformer protection are important points to system operation. Transient currents caused by the transformer at the time of energization can produce mechanical stress to the transformer, causing damage to transformer windings, and may cause protection system malfunction. It often affects the power system quality and may disrupt the operation of sensitive electrical/electronic loads such as computers, medical equipments and adjustable speed drives (ASD) of the industry. It may also cause inadvertent operation of transformer protection relays (e.g. differential relay). Looking into above matters, reduction and the way to control of transformer inrush currents have become important concerns to the power industry

Sustainable and Robust Home Healthcare Logistics: A Response to the COVID-19 Pandemic

Today, research on healthcare logistics is an important challenge in developing and developed countries, especially when a pandemic such as COVID-19 occurs. The responses required during such a pandemic would benefit from an efficiently designed model for robust and sustainable healthcare logistics. In this study, we focus on home healthcare logistics and services for planning the routing and scheduling of caregivers to visit patients’ homes. Due to the need for social distancing during the COVID-19 pandemic, these services are highly applicable for reducing the growth of the epidemic. In addition to this challenge, home healthcare logistics and services must be redesigned to meet the standards of a triple bottom line approach based on sustainable development goals. A triple bottom line approach finds a balance between economic, environmental, and social criteria for making a sustainable decision. Although, recently, the concept of green home healthcare has been studied based on the total cost and green emissions of home healthcare logistics and services, as far as we know, no research has been conducted on the formulation of a triple bottom line approach for home healthcare logistics and services. To achieve social justice for caregivers, the goal of balancing working time is to find a balance between unemployment time and overtime. Another contribution of this research is to develop a scenario-based robust optimization approach to address the uncertainty of home healthcare logistics and services and to assist with making robust decisions for home healthcare planning. Since our multi-objective optimization model for sustainable and robust home healthcare logistics and services is more complex than other studies, the last novel contribution of this research is to establish an efficient heuristic algorithm based on the Lagrangian relaxation theory. An initial solution is found by defining three heuristic algorithms. Our heuristic algorithms use a symmetric pattern for allocating patients to pharmacies and planning the routing of caregivers. Then, a combination of the epsilon constraint method and the Lagrangian relaxation theory is proposed to generate high-quality Pareto-based solutions in a reasonable time period. Finally, an extensive analysis is done to show that our multi-objective optimization model and proposed heuristic algorithm are efficient and practical, as well as some sensitivities are studied to provide some managerial insights for achieving sustainable and robust home healthcare services in practice

Voltage Control by Microcontroller to Reduce Transformer Inrush Current

Abstract.When a transformer is energized by the supply voltage, a high current called transientinrush current which it may raise to ten times of the transformer full load current could be drawn by the primary winding.This paper discusses a microcontroller circuit with the intention of controlling and limiting the inrush current for a transformer by the method of ramping up the supply voltage feeding to the transformer primary. Simulations and the experimental results show a significant reduction of inrush current when the ramping up voltage is applied to the three-phase transformer load. Inrush current could be almost eliminated if choosing a correct switching step rate

Integrated Air Transportation and Production Scheduling Problem with Fuzzy Consideration

Nowadays, several methods in production management mainly focus on the different partners of supply chain management. In real world, the capacity of planes is limited. In addition, the recent decade has seen the rapid development of controlling the uncertainty in the production scheduling configurations along with proposing novel solution approaches. This paper proposes a new mathematical model via strong recent meta-heuristics planning. This study firstly develops and coordinates the integrated air transportation and production scheduling problem with time windows and due date time in Fuzzy environment to minimize the total cost. Since the problem is NP-hard, we use four meta-heuristics along with some new procedures and operators to solve the problem. The algorithms are divided into two groups: traditional and recent ones. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as traditional algorithms, also Keshtel Algorithm (KA) and Virus Colony Search (VCS) as the recent ones are utilized in this study. In addition, by using Taguchi experimental design, the algorithm parameters are tuned. Besides, to study the behavior of the algorithms, different problem sizes are generated and the results are compared and discussed