Neural-network-based sensorless maximum wind energy capture with compensated power coefficient

By Hui Li, K. Shi, P. McLaren. IEEE transactions on industry applications, 2004

306 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/394093964d80b078aaaa143b4b3ca05fea08f4e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Avinash Kumar Sinha
2. Navneet Kishore
3. S. K. Sahoo
4. Whei-Min Lin

Citations in the corpus

• Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System (2011, background): “[15] proposed a small wind generation system with NN for wind-speed estimation and PI control for maximum wind-power extraction.”
• Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review (2018, methodology): “In [76], the neural network is applied to estimate wind speed and control of the peak wind power extraction against the”