Use of Kalman Filter and Its Variants in State Estimation: A Review

Kumari, Neha; Kulkarni, Rohan; Ahmed, Mohammed Riyaz; Kumar, Naresh

doi:10.1007/978-3-030-77070-9_13

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Kalman Filters aim to improve speed and estimation performance under low observability. They are linked to the Forecast-Aided SE concept, where model-based approaches use the previous states as extra information to enhance accuracy and speed [4], [10]- [13]. However, Kalman Filter SE is limited by the assumptions of system linearization and the Gaussian distribution of the measurements, which reduce its accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Deep Statistical Solver for Distribution System State Estimation

Habib

Isufi

Breda³

et al. 2024

IEEE Trans. Power Syst.

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Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS 2 ), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and the governing power flow equations of the problem. DSS 2 is based on GNN and leverages hypergraphs to model the network as a graph into the deep-learning algorithm and to represent the heterogeneous components of the distribution systems. A weakly supervised learning approach is put forth to train the DSS 2 : by enforcing the GNN output into the power flow equations, we force the DSS 2 to respect the physics of the distribution system. This strategy enables learning from noisy measurements and alleviates the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS 2 outperforms the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time while being more robust to noisy, erroneous, and missing measurements. The DSS 2 achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.

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Section: Introductionmentioning

confidence: 99%

Deep Statistical Solver for Distribution System State Estimation

Habib

Isufi

Breda³

et al. 2024

IEEE Trans. Power Syst.

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“…Kalman filter was proposed by R. E. Kalman in 1960 [8] is popular for having easy computation, memory requirements and good capability on overcoming noises. There are various types of Kalman Filter, such as standard Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter etc [9]. The paper used standard Kalman filter since it contains enough part of equation for noise reducing.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Noise Cancellation Scheme Combining Kalman Filter With Adaptive FxNLMS

Rahman

Sarker

2023

International Journal of Computational and Applied Mathematics &Amp; Computer Science

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Underwater environments are more challenging than that of terrestrial. The performance of a controller or the augmented system as a whole depends on the real measured data, so noise on data readings can be fatal. To effectively and adaptively control lower and higher frequency noise, the Active Noise Cancellation (ANC) was developed. Designing a system and the parameter of modified FxLMS for reducing noise, and disturbance of sensor data is the primary focus of this paper. The required equation will be analyzed and discussed briefly. Moreover, the system will be simulated in MATLAB and then the filtered result will be analyzed. Based on the simulation results, the proposed model can filter out signals with noise, particularly when there is a significant variation in the data and no knowledge of the noise frequency that might affect sensor readings.

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