An energy and leakage current monitoring system for abnormality detection in electrical appliances

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.

Development of Arduino-based high heat detector temperature control prototype for household appliances

“…It offers valuable information on the prototype's sensitivity in identifying positive cases, contributing to a comprehensive evaluation of its performance in correctly recognizing positive instances and minimizing false negatives. This can be computed using (3).…”

“…Focusing on urban fires in [2], the country loses a significant amount each year due to fire catastrophes, as the overall number of fire casualties reported in 2018 was 326, with 14,364 documented cases of fire incidents, which grew to 16,408 from January to October 2019. Numerous people have died in electrical fire incidents brought on by the malfunction, failure, or deterioration of electrical equipment, and significant property damage has also been incurred [3]. More residual current flows through outdated or damaged appliances as their insulation deteriorates, eventually resulting in insulation burning due to excessive heat produced.…”

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

“…This approach can be used for classifications in another aspect of machine learning applications because of its low computational cost in removing irrelevant power consumption patterns example includes principal component analysis (PCA) in [24], linear discriminant analysis (LDA) in [36] and quadratic discriminant analysis (QDA) in [1,8].…”

Leakage Current Detector and Warning System Integrated with Electric Meter

“…The advancement of sensor technology and machine learning technology allows the prediction of any incident caused by leakage current. Recently, Morshed et al used a mixed model of multi-class support vector machines (MSVM) to monitor the historical data of normal and leakage currents in household electrical equipment to determine the leakage current [20]. The data were stored in a structured database that could be accessed remotely via the Internet.…”