An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking

By K. Yu, Neville R. Watson, J. Arrillaga. IEEE Transactions on Power Delivery, 2005

214 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d896ca8e6a196130587e2e776c5db309e26ef8a7

How researchers see this paper

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

In Control and Systems Engineering

1. Liye Xiao
2. Mihai Ciobotaru
3. Shaotao Dai
4. Wenyong Guo
5. Vassilios G. Agelidis
6. Huaiwei Liao
7. Shamim Reza

In Electrical and Electronic Engineering

1. Liye Xiao
2. Mihai Ciobotaru
3. Shaotao Dai
4. Wenyong Guo
5. Vassilios G. Agelidis
6. Huaiwei Liao
7. Shamim Reza
8. 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): “A frequency-independent KF can also be used to track the harmonic states without providing the frequency estimation [59], [60].”
• Power System Harmonic State Estimation and Observability Analysis via Sparsity Maximization (2007, methodology): “Other approaches, such as artificial neural networks [12], cascade correlation network [25], Kalman filters [13], [14], [ 15 ], are proposed for Harmonic State Estimation in transmission networks.”
• Enhancing Low-Voltage Ride-Through Capability and Smoothing Output Power of DFIG With a Superconducting Fault-Current Limiter–Magnetic Energy Storage System (2012, methodology): “The Kalman filter [47] is used in the grid fault detection.”