Novel Current Unbalance Estimation and Diagnosis Algorithms for Condition Monitoring With Wireless Sensor Network and Internet of Things Gateway

Hamici, Zoubir; Elhaija, Wejdan Abu

doi:10.1109/tii.2019.2935743

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Diagnosis algorithms for condition monitoring and solving voltage imbalance using an IoT gateway and wireless sensors have been developed in [73]. Other applications of IoT in electrical machine diagnostics include fault detection with current signature analysis and the propagation of fault signals from motors to IoT [74].…”

Section: Analysis Of Distributed Observations For Diagnosismentioning

confidence: 99%

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

Ioannides,

Stamelos,

Papazis

et al. 2024

Energies

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The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. This article analyses selected aspects of research and applications of IoT-based control in electric drive systems and of wind energy conversion systems with induction machines. Various applications and study cases of control systems of electrical machines with IoT technology are described. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. The paper offers a comprehensive analysis of IoT-based control applications in the field of induction machines, with technical details of design, construction, experimental testing, and prototyping, that are useful to energy engineering specialists in the sector of electric drives and wind energy conversion systems.

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Section: Analysis Of Distributed Observations For Diagnosismentioning

confidence: 99%

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

Ioannides,

Stamelos,

Papazis

et al. 2024

Energies

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“…Digital electrical measuring devices utilizing the principle of inductance are able to determine the phase sequence of electricity and power [9,10], but this tool is expensive and uneconomical and can only detect phase sequence in certain areas so that when in a large and complex electrical installation system it cannot work optimally.…”

Section: Introductionmentioning

confidence: 99%

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

Nursuwars,

Shofa,

Andang

et al. 2024

E3S Web Conf.

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The IoT-based phase detection system is one of the important innovations in monitoring and managing modern electrical systems. However, challenges arise in presenting real-time data communication in the context of edge computing through the use of APIs. The problem that arises is the length of response time required in the data communication process, which can hamper the efficiency and accuracy of the system. The main objective of this research is to design and implement an effective strategy to reduce response time in API-based IoT data communication in phase detection systems. The method adopted includes a thorough analysis of existing communication processes and the development of optimized algorithms to speed up response times. This research approach involves measuring the response time before and after implementing an optimized algorithm on the client side, which in this case is represented by an Arduino device. Experiments were conducted using realistic data communication scenarios to validate the effectiveness of the proposed approach. The experimental results show that by optimizing the communication algorithm on the client side, the response time in IoT data communications can be significantly reduced. The response time which originally reached 4 seconds, was successfully reduced to only 0.8 seconds after the implementation of an optimized algorithm. This result has the potential to increase the operational efficiency of the system and expand the application of this technology in a variety of applications that require a fast response time.

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“…Environment monitoring is the task of recording atmospheric parameters over a period of time and at a specified location [1][2][3][4][5]. Results of such monitoring will help government and non-government organizations to take necessary actions and decisions about how the environment will affect the human life and in what ways can humans safeguard the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

IoT-EMS: An Internet of Things Based Environment Monitoring System in Volunteer Computing Environment

Bhoi¹,

Panda²,

Jena³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Environment monitoring is an important area apart from environmental safety and pollution control. Such monitoring performed by the physical models of the atmosphere is unstable and inaccurate. Machine Learning (ML) techniques on the other hand are more robust in capturing the dynamics in the environment. In this paper, a novel approach is proposed to build a cost-effective standardized environment monitoring system (IoT-EMS) in volunteer computing environment. In volunteer computing, the volunteers (people) share their resources for distributed computing to perform a task (environment monitoring). The system is based on the Internet of Things and is controlled and accessed remotely through the Arduino platform (volunteer resource). In this system, the volunteers record the environment information from the surrounding through different sensors. Then the sensor readings are uploaded directly to a web server database, from where they can be viewed anytime and anywhere through a website. Analytics on the gathered time-series data is achieved through ML data modeling using R Language and RStudio IDE. Experimental results show that the system is able to accurately predict the trends in temperature, humidity, carbon monoxide level, and carbon dioxide. The prediction accuracy of different ML techniques such as MLP, k-NN, multiple regression, and SVM are also compared in different scenarios.

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Novel Current Unbalance Estimation and Diagnosis Algorithms for Condition Monitoring With Wireless Sensor Network and Internet of Things Gateway

Cited by 8 publications

References 20 publications

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

IoT APIs: Time Response Optimization in Edge Computing Data Communication for Power Phase Detection System

IoT-EMS: An Internet of Things Based Environment Monitoring System in Volunteer Computing Environment

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