Battery protection-where do we stand?

Nailen, R.L.

doi:10.1109/28.85479

Cited by 31 publications

(1 citation statement)

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“…The failure developed in battery, in particular, the internal faults could bring a difficulty to eliminate them by electrical protection elements such as fuse or circuit-breaker by reason of absence an appropriate selection of fuse for this circuit [13]. This will result in undetected faults that conduct the battery to work in low performance and decrease its lifecycle, furthermore, it increases the risk of fire hazard and even an explosion, without account the contamination of equipment's beside [13]. To solve this problem and overcome the limitation of existed battery fault analysis, this paper proposes internal faults detection of solar battery using anomaly detection technique.…”

Section: Introductionmentioning

confidence: 99%

Battery Internal Fault Monitoring Based on Anomaly Detection Algorithm

Sabri¹,

Tlemçani²,

Chouder³

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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Battery internal faults are one of the major factors causing safety concern, performance degradation, and cost increases. To extend the lifetime of the battery and bring more security in the system, internal fault detection of solar battery is proposed in this paper using an unsupervised machine learning algorithm based on anomaly detection method. The advantages of adopting such a method consist of using unlabeled data that meet the battery case in the difficulty of obtaining the fault data. In contrast, healthy data can easily be obtained from the battery and therefore allows building the anomaly detection algorithm. The effectiveness of the proposed method is validated using a simulation platform of a stand-alone photovoltaic system developed in Matlab/Simulink that takes as system input a real profile of irradiance and temperature captured from the Centre de Development des Energies renewable (CDER), Algeria. The test results in real-time data show the ability of the proposed approach to detect the fault occurrence in the battery.

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Section: Introductionmentioning

confidence: 99%