Self-tuning of Kalman filters for harmonic computation

By J. Macías, Antonio Gómez Expósito. IEEE Transactions on Power Delivery, 2006

137 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3bc6c36034c126e2da940b4848a90e88e092fb7f

How researchers see this paper

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

In Artificial Intelligence

1. Hsiung-Cheng Lin
2. Mihai Ciobotaru
3. Vassilios G. Agelidis
4. Chao-Hung Chen
5. Shamim Reza

In Computational Mechanics

1. Chao-Hung Chen
2. Hsiung-Cheng Lin
3. Mihai Ciobotaru
4. Vassilios G. Agelidis
5. Shamim Reza

In Electrical and Electronic Engineering

1. Chao-Hung Chen
2. Hsiung-Cheng Lin
3. Mihai Ciobotaru
4. Vassilios G. Agelidis
5. Shamim Reza
6. Bin Wu

Citations in the corpus

• Accurate Estimation of Single-Phase Grid Voltage Fundamental Amplitude and Frequency by Using a Frequency Adaptive Linear Kalman Filter (2016, methodology): “The steps of the LKF are as follows [41]–[43]: Time Update Stage:”
• Inter-Harmonic Identification using Group-Harmonic Weighting Approach Based on the FFT (2008, methodology): “A number of algorithms, e.g., short time Fourier transform [5], least-square approach [6]‐[8], Kalman filtering [9], [ 10 ], artificial neural networks [4], [11], have been proposed to extract harmonics.”