Predictive Maintenance of Lead-Acid Batteries Using Machine Learning Algorithms

Sridevi, H. R.; Bothra, Shrey

doi:10.1007/978-981-19-5482-5_63

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the domain of battery management, which might be related to PV energy storage systems, the integration of machine learning algorithms has emerged as a powerful tool for predictive maintenance [103]. While the focus lies primarily on lead-acid batteries, the principles extrapolate to PV systems, where battery storage plays a crucial role.…”

Section: Tools and Devices For Predictive Maintenancementioning

confidence: 99%

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

Orosz,

Rassõlkin,

Arsénio

et al. 2024

Energies

View full text Add to dashboard Cite

The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems.

show abstract

Section: Tools and Devices For Predictive Maintenancementioning

confidence: 99%