An Adaptive Self-Tuned Wavelet Controller for IPM Motor Drives

Abdesh, M.; Khan, Sikandar; Rahman, M.A.

doi:10.1109/pes.2008.4596066

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…Cluster #12 combines the publications related to the accurate modeling of state-ofcharge and battery storage using ANNs and RNNs [97][98][99]. Cluster #13 includes the publications related to maximum point tracking in wind energy conversion systems using neural network controllers [100][101][102] and energy maximization using neural networks, such as learning vector quantitative neural networks, on a sliding-buoy wave energy converter [54].…”

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confidence: 99%

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Mira,

Bugiotti,

Morosuk

2023

Energies

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In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

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