Kalman filter is designed to predict the correct values for noisy measurements, using a probabilistic model. However, the chosen system model has a huge impact on the outcome of this algorithm, which makes it very important for the model to be as representative of the system as possible. A new approach to handle the noisy measurements in low power Maximum Power Point Tracking (MPPT) algorithms is presented in this paper. The proposed filtering proved beneficial in terms of algorithm stability.
Noisy reading correction in low power MPPT using kalman filter
Haidar M.;Caviglia D. D.
2019-01-01
Abstract
Kalman filter is designed to predict the correct values for noisy measurements, using a probabilistic model. However, the chosen system model has a huge impact on the outcome of this algorithm, which makes it very important for the model to be as representative of the system as possible. A new approach to handle the noisy measurements in low power Maximum Power Point Tracking (MPPT) algorithms is presented in this paper. The proposed filtering proved beneficial in terms of algorithm stability.
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