Developing a Monitoring System for Tripping Fault Detection via IoT

Hafizi, M.; Leh, Nor Adni Mat; Kamarzaman, Nur Atharah; Ishak, Nurul Huda

doi:10.1109/icsgrc.2018.8657555

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…ACS712 current sensor is based on Hall Effect as shown in Fig. 8 that can sense up to 20 A of current [36]. The pin diagram and its description are shown in Fig.…”

Section: Sensors In Iot Nodesmentioning

confidence: 99%

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

Uppal¹,

Gupta²,

Anand³

et al. 2022

Computers, Materials &Amp; Continua

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The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

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“…ACS712 current sensor is based on Hall Effect as shown in Fig. 8 that can sense up to 20 A of current [36]. The pin diagram and its description are shown in Fig.…”

Section: Sensors In Iot Nodesmentioning

confidence: 99%

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

Uppal¹,

Gupta²,

Anand³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…IoT devices have already become ubiquitous at the home level, with the proliferation of smart home devices such as Nest thermostats, and platforms such as Google Home and Alexa [30]. IoT devices have been proposed in the management of extended outage conditions, such as automated fault location, isolating and service restoration (FLISR) in [31]. IoT devices also provide a way to determine the position of the defective parts, separates them, and applies switching task to recover the largest number of healthy part of the affected energy feeder by having increased sensors deployed in control devices [32].…”

Section: Iot Device Application In Smart Gridsmentioning

confidence: 99%

DER Forecast using Privacy Preserving Federated Learning

Venkataramanan¹,

Sridevi²,

Annaswamy³

2021

Preprint

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With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics such as load swings and load curtailment and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street dataset to demonstrate the validity of the proposed approach on real data.

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