Internet Of Things Based Monitoring System Research Articles

IoT based monitoring system for DFIG based wind turbines under voltage dips

Due to their partial-rating power converters and dynamic performance, doubly-fed induction generators (DFIGs) are commonly used in wind turbines (WT). However, grid-connected DFIGs are vulnerable to voltage dips, which can cause power quality concerns and possible turbine damage. An Internet of Things (IoT)-based monitoring technique for field-oriented control (FOC)-based DFIG with grid-side converter (GSC) control is proposed in this paper. The proposed approach uses real-time WT voltage and current data to dynamically alter the GSC control settings, allowing for steady operation and minimizing power quality problems during voltage dips. A 2 MW variable-speed pitch-controlled DFIG with a speed limit of 900–2000 rpm is used for performance investigation under voltage dips with crowbar protection. An IoT based monitoring system sends real-time information to a cloud server for additional analysis after detecting anomalies. The ThingSpeak cloud platform sends email notifications of defects, and an MIT App Inventor application is constructed for data visualization. The proposed strategy's effectiveness is confirmed through the simulation results.

Low-cost real-time internet of things-based monitoring system for power grid transformers

<p><span lang="EN-US">One of the most common causes of blackouts is unexpected failures at power system transformer levels. The purpose of this project is to create a low-cost Internet of things (IoT)-based monitoring system for power grid transformers in order to investigate their working status in real-time. Our monitoring system’s key functions are the gathering and display of many metrics measured at the transformer level (temperature, humidity, oil level, voltage, vibration, and pressure). The data will be collected using various sensors connected to a microcontroller with an embedded Wi-Fi module (DOIT Esp32 DevKit v1), and then supplied to a cloud environment interface with a full display of all the ongoing changes. This technology will provide the power grid maintenance center with a clear image of the transformers’ health, allowing them to intervene at the right time to prevent system breakdown. The method described above would considerably improve the efficiency of a power transformer in a smart grid system by detecting abnormalities before they become critical.</span></p>

Transformer Health Monitoring using IoT Based

Abstract: Transformers are critical components of electric powersystems, yet precise fault identification remains difficult. The study presents a novel transformer defect diagnostic approach based on an Internet of Things (IoT) monitoring system and ensemble machine learning (EML). The IoT based monitoring system is divided into two parts: a data measuring subsystemand a data reception subsystem.To begin, the data measuring subsystem measures transformer vibration signals, which are then relayed to the remote server via the data receipt subsystem.

Development of Monitoring System Based on Internet of Things (IoT) for Freshwater Prawn Farming

Aqua-farming or water farming is an aquatic counterpart to agriculture or ground farming. It is one of methods to make out foods or commercial product. With the current incompetent aqua-farming activities such as labour energy consumption to monitor the water quality and to change the water in the tank in schedule so that this project is important to tackle down the problems stated. This project aimed to reduce the incompetency of water quality monitoring process and the water changing process by developing a vertical freshwater prawn tank to reduce space with an automated water changing and IoT based monitoring system. The autonomous system implemented with solenoid valve and water pump will recycle the water from the prawn tanks with the clean water from the reservoir tank. The monitoring system employs ultrasonic sensor and waterproof temperature sensor that will monitor the water level in the prawn tanks and the temperature of the water respectively that will be visualized in the Thing speak Channel regularly. This project will be implemented as a prototype with an IoT technology for the freshwater prawn farming activity using low-cost embedded devices like Arduino Mega2560, ESP8266 Wi-Fi module and the RTC module. The value from the sensors’ reading will be visualized in the Thing speak Channel display.

Information model of low-voltage distribution IoT monitoring terminal based on IEC 61850

Abstract Low-voltage distribution lines lack efficient monitoring. With the massive proliferation of distributed energy resource supply, it is necessary to establish an effective monitoring system for low-voltage distribution lines to ensure the electrical safety and continuity of power supply services. This paper proposes monitoring system for low-voltage distribution lines based on the Internet of things (IoT) technology. An IoT based monitoring system contains a large number of low-voltage monitoring terminals, different manufacturers, and different types of terminal equipment to solve the interconnection problem of information interaction. This paper first analyzes the terminal functional communication requirements based on IEC 61850 standards. Further, the information models required by the electrical volume collection and non-electric quantity collection functions are studied. The information modeling for different types of monitoring terminal equipment is presented and thus provide the configuration suggestions. The monitoring terminal based on the information model developed in this paper is applied in a university campus to realize the monitoring of low-voltage lines and distributed power supply.

Development of internet of things-based monitoring system for application solar energy technology in Bunaken island

There are some barriers to monitoring and evaluating power plant systems in a remote location; thus, Internet of Things (IoT) enabled systems can be leveraged remotely. This solution is a part of implementing renewable energy-based technology for coastal communities on the small remote island, such as Bunaken Island. Furthermore, this study aims to develop an IoT-based monitoring system of solar energy utilization as a renewable energy source for off-grid solar home systems in remote areas. The methods of this study refer to the stages of the method research and development (R&D) that consist of designing and testing the product of IoT-based solar PV monitoring system. Moreover, this study indicates that the IoT-reported system can report the performance of the remote solar power systems installed in Bunaken Island.

RETRACTED ARTICLE: Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation.

The pandemic situation has pretentious the habitual life of the human, it also has surpassed the regional, social, business activities and forced human society to live in a limited boundary. In this paper, the application of the internet of things (IoT) and machine learning (ML) based system to combat pandemic situation in health care application has been discussed. The developed ML and IoT based monitoring system help in tracking the infected persons from the previous data and makes them get isolate from the non-infected person. The developed ML combined IoT system uses parallel computing in tracking the pandemic disease and also in the prevention of pandemic disease by predicting and analysing the data using artificial intelligence. The implementation of ML-based IoT in the pandemic situation in healthcare application has proved its performance in tracking and prevents the spreading of pandemic disease. It also further has a positive impact on reducing medical costs and has recorded improved treatment for infected patients. The proposed methodology has an accuracy of 93 % in monitoring and tracking. The result obtained help in preventing the spread of the pandemic and provide support to the healthcare system.

Development of an Industrial IoT Based Monitoring System for Voltage Regulators

The increase in electronic loads connected to electrical network became relevant requirements of the quality of supply. Among the equipment used in the power distribution system stands out regulators medium voltage installed over the grid to maintain voltage levels within indifferent operating range of the oscillations. Therefore, the quality of supply has become a priority and the implementation of technologies that allow transforming conventional electrical distribution networks into intelligent ones is strategic for Distribution Operation Centers (COD) to perform remote interaction with the equipment, in this case, with a power regulator. medium voltage. In this context, this article demonstrates an IoT (Internet of Things) solution for monitoring and controlling medium voltage regulators, through the use of LPWA (Low Power Wide Area) networks, integrating this equipment with the concepts of Smart Grid (SG). The complete development of the project is presented, including the study of the communication protocol DNP3 (Distributed Network Protocol 3), responsible for the exchange of information, between the hardware system and the voltage regulator, the communication system being through the Sigfox and GPRS (General Packet Radio Service), transmitting the regulator data to supervision software. Validation of communication hardware and software with the medium voltage regulator, occurred with bench tests in expressive periods and, after installation in the field, the experiment was validated by reading and recording parameters, such as number of maneuvers, voltage and mains current, obtained directly from the voltage regulator.

Implementation of Fuzzy Logic on Internet of Things-Based Greenhouse

This research is an internet of things-based monitoring system using LoRaWAN and the IoT Thingspeak application server. This research is focused on helping farmers, or the agriculture sector focused on Pakcoy. Pakcoy is a plant that can live at an altitude of 500-1200 meters above sea level using the Greenhouse concept; Pakcoy plants can be protected from pests. Nutrients for Pakcoy are sprayed through the leaves, accelerating the provision of nutrients for plants. The Fuzzy Sugeno method is used to control the Greenhouse temperature and Soil Moisture for effective plant development, as well as monitoring the Greenhouse temperature, soil moisture, and pH of the growing media based on the Internet of Things that uses the Long Range 915 Mhz, therefore, the users can find out in real-time the data can be accessed by using Thingspeak and can be used in areas where internet access is difficult.

The contribution of the Internet of Things and smart systems to agricultural practices: A survey

The growing demand for agriculture products contradicts the challenges associated with the decline in farming land size and labour workforce. Recent literature acknowledges that the Internet of Things (IoT) plays a crucial role in enhancing the smart aspects already deployed in agriculture. Smart farming is an emerging concept because of IoT sensors capable of aggregating information about their agriculture fields under real-life circumstances. The paper aims to survey the recent implementation of IoT based monitoring systems applied in agriculture worldwide. A systematic literature review carried out to investigate the extent of IoT adoption in agriculture industries. The understanding of Reason to adopt IoT, the Scale of adoption and the cost and benefit of IoT adoption were drawn from papers published in SCOPUS, Web of Science, and DOAJ between 2010 up to early 2020. Twenty-eight most relevant articles extracted from the search. This paper recommends strategies to adopt an IoT-based monitoring tool pertinent to irrigation systems implementation in Indonesia.

An adaptive packets hopping mechanism for transmission line monitoring systems with a long chain topology

To achieve the goals of efficient and safe operation of smart grids, a remote monitoring system for extra-high voltage (EHV) transmission line is required. In Taiwan, most of the towers of EHV transmission systems built by Taiwan Power Company (TPC) are located in remote areas and their line topology resembles a long chain. Therefore, the monitoring network designed for the EHV transmission lines cannot easily gain access to energy sources to power its communication nodes and may become fragmented due to any node failure. In view of the challenge, this study proposes a customized routing scheme for EHV transmission line monitoring, named transmission coverage adjustable multi-hop algorithm (TCAMA). TCAMA not only controls transmission power but also performs route selection to minimize power consumption. This algorithm also creates multiple redundant paths to meet the communication performance requirements. To verify the effectiveness of TCAMA, an Internet of Things-based monitoring system has adopted the proposed TCAMA to monitor an actual TPC EHV transmission system to examine its communication network performance. First, a gateway was developed, and a point-to-point transmission experiment was conducted to evaluate the transmission performance of the gateway with different transmission power and distances. Second, the results of the point-to-point experiment served as the transmission parameters in the network simulation, and 150 simulations were conducted. Then, the simulation results were analyzed to determine whether the data packet transmission performance met the communication requirements of a smart grid. Third, the performance of TCAMA was further examined using the distribution and topology of critical EHV transmission lines in Taiwan. Based on the field test results, TCAMA can prevent packet storms and can perform customized tasks using empirical measurements. In addition, TCAMA is capable of achieving 100% packet delivery while maintaining end-to-end communication delay much less than 2000 ms. The delay and energy consumption during the network transmission can also be reduced when TCAMA is employed.

FCM clustering and FLS based CH selection to enhance sustainability of wireless sensor networks for environmental monitoring applications

Wireless Sensor Networks (WSNs) are the physical monitoring infrastructure of the Internet of Things (IoT) technology. For IoT based monitoring systems, the WSNs need to be sustainable with a maximum number of alive nodes so that the monitoring is effective. The sensor nodes are battery-driven and hence energy efficiency is one of the major challenges. Based on the clustering methods and the selection of Cluster Head (CH), the energy consumption of the sensor nodes can be minimized. In this research work, a clustering protocol based on fuzzy techniques is proposed to improve the stability and sustainability of WSN. Fuzzy techniques are used to tackle uncertainties occurring in wireless sensor networks. Clusters are formed based on Fuzzy-c-means (FCM) algorithm. The aim is to group the nodes properly so as to reduce intra-cluster communication distances. The CHs are then selected based on the Fuzzy Logic System (FLS). The performance of the proposed protocol is observed for an increase in the coverage area and node density. The proposed protocol is also analyzed for different sink locations. Due to better network stability and sustainability, the proposed protocol can be used for large scale IoT based monitoring systems.

Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning

Transformer is important to the electric power systems, and its accurate fault diagnosis is still hard. In the paper, a novel transformer fault diagnosis method using an Internet of Things (IoT) based monitoring system and an ensemble machine learning (EML) is presented. The monitoring system based on IoT technology consists of two parts: a data measurement subsystem and a data reception subsystem. Firstly, transformer vibration signals are measured by using the data measurement subsystem, and they are sent to the remote server by using the data reception subsystem. Then, an EML composed of deep belief networks (DBNs) and stacked denoising autoencoders (SDAs) with different activation functions, and relevance vector machines (RVMs) is proposed. DBNs and SDAs are respectively used to extract features from the signals, and RVMs are respectively employed as classifier. In order to ensure efficient of the EML, a novel combination strategy is proposed. A transformer fault diagnosis experiment is performed, and the diagnosis results confirm that the designed monitoring system can collect vibration signals effectively and long-distance, and the proposed EML can usefully remedy the inadequate information of features extracted by individual deep learning method, and its RVM classifier is obviously better than other commonly used classifiers.

Atrial fibrillation classification using deep learning algorithm in Internet of Things-based smart healthcare system.

Detecting the electrocardiogram pattern in Internet of Things-based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things-based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things-based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things-based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things-based smart healthcare system.

An Internet of Things-Based Monitoring System for Shop-Floor Control

With the advent of the fourth industrial revolution (Industry 4.0), manufacturing systems are transformed into digital ecosystems. In this transformation, the internet of things (IoT) and other emerging technologies pose a major role. To shift manufacturing companies toward IoT, smart sensor systems are required to connect their resources into the digital world. To address this issue, the proposed work presents a monitoring system for shop-floor control following the IoT paradigm. The proposed monitoring system consists of a data acquisition device (DAQ) capable of capturing quickly and efficiently the data from the machine tools, and transmits these data to a cloud gateway via a wireless sensor topology. The monitored data are transferred to a cloud server for further processing and visualization. The data transmission is performed in two levels, i.e., locally in the shop-floor using a star wireless sensor network (WSN) topology with a microcomputer gateway and from the microcomputer to Cloud using Internet protocols. The developed system follows the loT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. In addition, the open platform communication—unified architecture (OPC-UA) standard is employed to support the connectivity of the proposed monitoring system with other IT tools in an enterprise. The proposed monitoring system is validated in a laboratory as well as in machining and mold-making small and medium-sized enterprises (SMEs).

Smart Safety & Health Care in Cities

The emerging technology of Internet of Things (IoT) has become essential in several applications, more specifically health and safety products, and used by many growing organizations. The IoT has already enabled these bodies to develop a new network of intercommunicating objects of consumers’ everyday life. Companies staffing many (potentially thousands) of employees may require hundreds of health and safety systems in continuous use. Building Service Managers, however, do not know which devices needs maintenance or replenishment and require significant amount of time to continuously check that their health and safety systems are maintained according to legislation requirements. The consequences of companies not meeting their health and safety obligations are serious and expensive if any of their employees are seriously injured as a consequence of the health and safety products not being in their original, immaculate order for use. Furthermore, insurance companies will refuse to pay compensation for injury to employees if it has been shown that the health and safety products are not in their original, immaculate order for use. Therefore, design and use an IoT based monitoring system to record the state of health and safety products within companies is an urgent response to market. This paper presents consequences and associated procedures for efficient application of the IoT.