Development of a Novel IoT-Enabled Power- Monitoring Architecture With Real-Time Data Visualization for Use in Domestic and Industrial Scenarios
TL;DR: This article has proposed the third and final version of the IoT-enabled power monitor to fulfill the need for three-phase power monitoring, and introduces a novel split architecture with centralized voltage measurement, which removes theneed for local voltage measurements.
Abstract: With the increased interest in smart cities and smart infrastructures, the need for energy conservation is increasing. Especially with the current electrical energy production mainly relying on nonrenewable resources, conservation of electrical energy is one of the challenging aspects across the globe. However, one can only perform energy conservation optimally by identifying consumption patterns at a granular level, which requires accurate and ubiquitous monitoring infrastructure. Because the electrical energy wastage can occur at any granularity (from a small house-hold appliance to grid-level wastage), the development of a low-cost, easy-to-install, and accurate power-monitoring infrastructure is need of the hour. Hence, in this article, we propose the developed designs for IoT-enabled power monitoring. First is the noninvasive power monitor with voltage connection. The second design introduces a novel split architecture with centralized voltage measurement, which removes the need for local voltage measurements. We have proposed the third and final version of the IoT-enabled power monitor to fulfill the need for three-phase power monitoring. Unlike first and second designs, this design can be used with noninvasive and invasive current sensors. The proposed architecture also supports essential features, such as secure data transfer. Developed devices transmit real-time data to the cloud server, which makes the data ubiquitously available anywhere and anytime. For analyzing the performance of the proposed architecture, the developed devices are deployed in real industrial scenarios. As an example use case, the electrical anomaly detection framework using the data collected is also explained, and the corresponding results are discussed.
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TL;DR: This paper proposes to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle the goals reporting, optimization, fault detection, and predictive maintenance in manufacturing enterprises.
Abstract: The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. Apart from the prevalent goal of reducing overall power consumption for economical and ecological reasons, such data can, for example, be used to improve production processes.
Based on a literature review and expert interviews, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. To tackle these goals, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software.
We transfer our findings to two manufacturing enterprises and show how the presented goals reflect in these enterprises. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public demo allowing to reproduce and extend our research.
12 citations
TL;DR: From the results of this paper, it is found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models.
Abstract: In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.
12 citations
Cites background from "Development of a Novel IoT-Enabled ..."
...IoT devices communicate with each other through wireless communication systems and transfer information to a centralized system [5]....
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01 Mar 2021
TL;DR: In this article, the authors follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes, including reporting, optimization, fault detection, and predictive maintenance.
Abstract: The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.
9 citations
TL;DR: In this paper , a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) was proposed to determine the changes in leakage currents caused by installed devices at a certain moment.
Abstract: Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system's condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.
3 citations
TL;DR: In this article, a taxonomy of the state-of-the-art IIoT data-acquisition middleware systems based on infrastructure, protocol heterogeneity, interoperability, real-time, and security is presented.
Abstract: The development of industrial Internet of Things (IIoT), big data, and artificial intelligence technologies is leading to a major change in the production system. The change is being propagated into the wave of transforming the existing system with a vertical structure into the corresponding horizontal platform or middleware. Accordingly, the way of acquiring IIoT data from an individual system is being altered to the way of being increasingly centralized through an integrated middleware of a scalable server or through a large platform. That said, middleware-based IIoT data acquisition must consider multiple factors, such as infrastructure (e.g., operation environment and network), protocol heterogeneity, interoperability (e.g., links with legacy systems), real-time, and security. This manuscript explains these five aspects in detail and provides a taxonomy of eighteen state-of-the-art IIoT data-acquisition middleware systems based on these aspects. To validate one of these aspects (network), we present our evaluation results at a real production site where IIoT data-acquisition loss rates are compared between wireless (long-term evolution) and wired networks. As a result, the wired communication can be more suitable for centralized IIoT data-acquisition middleware than wireless networks. Finally, we discuss several challenges in establishing the best IIoT data-acquisition middleware in a centralized way.
3 citations
References
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TL;DR: The algorithm's potential use for phase detection by collectively leveraging smart meter and feeder meter data is explored and shows encouraging results when applied in a downstream section of a large feeder.
Abstract: Many utilities have the data quality problem with the geographical information system (GIS) records at distribution level This affects many business functions of a utility, including asset management, outage response, and workforce safety For correcting connectivity errors in the GIS representation of the distribution network topology, BC Hydro has developed an in-house algorithm The algorithm leverages smart meter interval measurements and identifies the neighboring meters by voltage profile correlation analysis It also predicts customers’ upstream and downstream location relationship by voltage magnitude comparisons The output of the algorithm is then compared with the existing GIS records to correct any errors in it This paper presents in detail the algorithm and the promising testing results within the practical BC Hydro system Challenges for underground services are demonstrated The algorithm’s potential use for phase detection by collectively leveraging smart meter and feeder meter data is explored It shows encouraging results when applied in a downstream section of a large feeder
175 citations
"Development of a Novel IoT-Enabled ..." refers background or methods in this paper
...[10] use the data acquired using existing smart meters for the detection of phase changes using a novel data analytics framework....
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...Few systems entirely targeted the monitoring of large-scale industries where current consumption is higher, while the others target low-current applications [4]–[10]....
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TL;DR: The sovereign blockchain technology, which provides transparency and provenance, is utilized in this paper to mitigate these above mentioned problems and proves very efficient as the user can monitor how the electricity is used, and it also provides a platform where there is no manipulation from either party.
Abstract: Electricity is the commonest commodity for most businesses in our world today. The use of electricity has been a breakthrough for the discovery of new technologies and has become the main driving force behind several innovations. With the introduction of smart grid systems, there have been improvements in how utility companies interact with their customers with regards to electricity use. However, since the readings are done via the Internet, there is the tendency for the data to be compromised when it gets into the hands of the wrong people. Moreover, customers mostly do not know why they pay huge amounts and which appliances use more electricity, since they are not privy to the readings. The sovereign blockchain technology, which provides transparency and provenance, is utilized in this paper to mitigate these above mentioned problems. A smart contract, which executes laid down procedures to provide a trust-based system between participants on the network is also implemented. Our system proves very efficient as the user can monitor how the electricity is used, and it also provides a platform where there is no manipulation from either party.
167 citations
"Development of a Novel IoT-Enabled ..." refers background in this paper
...[16] proposed blockchain-based storage of monitored data for improved security....
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TL;DR: In this article, the authors describe the role of advanced sensing systems in the electric grid of the future and describe the project, development, and experimental validation of a smart power meter, which uses the metrics proposed in the IEEE Standard 1459-2010 to analyze and process voltage and current signals.
Abstract: This paper aims to describe the role of advanced sensing systems in the electric grid of the future. In detail, the project, development, and experimental validation of a smart power meter are described in the following. The authors provide an outline of the potentialities of the sensing systems and IoT to monitor efficiently the energy flow among nodes of an electric network. The described power meter uses the metrics proposed in the IEEE Standard 1459–2010 to analyze and process voltage and current signals. Information concerning the power consumption and power quality could allow the power grid to route efficiently the energy by means of more suitable decision criteria. The new scenario has changed the way to exchange energy in the grid. Now, energy flow must be able to change its direction according to needs. Energy cannot be now routed by considering just only the criterion based on the simple shortening of transmission path. So, even energy coming from a far node should be preferred, if it has higher quality standards. In this view, the proposed smart power meter intends to support the smart power grid to monitor electricity among different nodes in an efficient and effective way.
167 citations
Additional excerpts
...[5] developed an IoT-enabled invasive smart...
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TL;DR: This paper proposes a method capable of detecting previously unidentified appliances in an automated way by mapping their VI trajectory to a newly learned feature space created by a siamese neural network such that samples of the same appliance form tight clusters.
77 citations
"Development of a Novel IoT-Enabled ..." refers methods in this paper
...[15] discussed a novel method for identifying load appliances from the measured voltage and current patterns using a Siamese neural network, and Gao et al....
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08 Mar 2017
TL;DR: The proposed design is to implement a very low cost wireless sensor network and protocol for smart energy and web application capable of automatically reading the unit and sending the data automatically for the power users to view their current energy meter reading.
Abstract: With the advent of Internet and computational era, not only opportunity to send and receive data between humans, but also among the devices without human control over it. This is known as Internet of Things (IoTs) concept which can be applied for solving the growing issue of power/energy management. A solution is a cheap and easy to implement and manage energy monitoring system for our daily usage of electric power. In order to overcome the human errors, manual labor and cost reducing in energy consumption with more efficiency for the power management system, in this paper, we focus mainly on IoT's energy monitoring. The proposed design is to implement a very low cost wireless sensor network and protocol for smart energy and web application capable of automatically reading the unit and sending the data automatically for the power users to view their current energy meter reading. By using this system, the users will be aware of the electricity usage in his/her home to reduce the power wastage and cost of consumption. The system consists of a digital energy meter, ESP8266 WiFi module and web applications for management system. The ESP8266 WiFi module will be embedded into the meter and implement the TCP/IP protocol for the communications between the meter and web application. The experimental results show that the proposed system works very well with efficiency, and it is feasible to implement in practical applications for very low cost-build automatic energy meter reading.
77 citations
"Development of a Novel IoT-Enabled ..." refers background in this paper
...The architecture proposed in [12] uses a current sensor that can support a maximum current measurement of 30 A....
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