Failure and fault classification for smart grids

Krivohlava, Zuzana; Chren, Stanislav; Rossi, Bruno

doi:10.1186/s42162-022-00218-3

Cited by 9 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, ensuring accurate data measurement throughout the operation with direct and indirect monitoring is challenging. The entire monitoring system must be considered, so transmission engineers should design a robust system that addresses all sorts of concerns [2,4]. Several studies have been conducted to prevent unauthorized access and avoid time delays in data processing, collection, and transmission.…”

Section: Introductionmentioning

confidence: 99%

Centralized Smart Energy Monitoring System for Legacy Home Appliances

Ahmad,

Almasalha,

Qutqut

et al. 2024

Preprint

View full text Add to dashboard Cite

As people increasingly rely on electrical devices for their daily tasks. As a result, energy consumption rates have sharply risen, leading to higher household electricity bills. This has produced a growing demand for energy monitoring systems that can accurately estimate energy usage, especially for older residential appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity accurately. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we propose a centralized smart energy monitoring system that utilizes prediction algorithms to calculate the power consumption of legacy home appliances. The primary goal of our proposed system is to overcome the limitations of legacy home appliances that require infrastructure upgrades. The system consists of two layers: a hardware layer that includes an Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors, and a software layer that includes Artificial Intelligence (AI) proposed predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted experiments on real home appliances to evaluate the proposed model. The accuracy of the proposed models showed positive results after several modifications and hard tuning of several parameters in the hardware devices, specifically for Jordanian power plants.

show abstract

Section: Introductionmentioning

confidence: 99%

Centralized Smart Energy Monitoring System for Legacy Home Appliances

Ahmad,

Almasalha,

Qutqut

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The monitoring system must be considered, so transmission engineers should design a robust system that addresses all sorts of concerns (Zainuddin et al 2020;Krivohlava et al 2022). Several studies have been conducted to prevent unauthorized access and avoid time delays in data processing, collection, and transmission.…”

Section: Introductionmentioning

confidence: 99%

Centralized smart energy monitoring system for legacy home appliances

Ahmad,

Almasalha,

Qutqut

et al. 2024

Energy Inform

View full text Add to dashboard Cite

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

show abstract