Comparative study of harmonics estimation in micro grid using adaptive extended Kalman filter

Nayak, Pravati; Pattnaik, Jyoti Manjary; Samantaray, Sushmita; Singh, Soumya

doi:10.1109/iceeot.2016.7755527

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Hence, the weight is given to the measurement and 1-Hk is given to the estimate, as the number of iterations increases the significance of the measurements decreases and the new value comes closer to the previous estimate value [15]. The uncertainty in the estimate needs to be updated with the updating of the state of the dynamic system, for finding the present uncertainty in the estimate using the previous uncertainty, estimate uncertainty update equation or covariance update (7): So, ( 4) and ( 8) is categorized in priori, and represents the prediction of co-variance and state of the system [16], [17]. The equation ( 3) and ( 6) is categorized in posteriori and represents the correction of covariance and state of the system with the block diagram as depicted in Figure 2 which describes the workflow of categorized priori along with prediction of co-variance and state of system.…”

Section: Methodsmentioning

confidence: 99%

Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks

Padupanambur

Ahmed

Divakar

2022

IJECE

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The employment of microgrids in smart cities is not only changing the landscape of power generation, transmission, and distribution but it helps in green alleviation by converting passive consumers into active produces (using renewable energy sources). Real-time monitoring is a crucial factor in the successful adoption of microgrids. Real-time state estimation of a microgrid is possible through internet-of-things (IoT). State estimation can provide the necessary monitoring of grid for many system optimization applications. We will use raw and missing data before we learn from data, the processing must be done. This paper describes various Kalman variants use for preprocessing. In this paper a formulated approach along with algorithms are described for optimal state estimation and forecasting, with weights update using deep neural networks (DNN) is presented to enable accurate measurements at component and system level model analysis in an IoT enabled microgrid. The real load data experiments are carried out on the IEEE 118-bus benchmark system for the power system state estimation and forecasting. This research paves a way for developing a novel DNN based algorithms for a power system under dynamically varying conditions and corresponding time dependencies.

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Section: Methodsmentioning

confidence: 99%

Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks

Padupanambur

Ahmed

Divakar

2022

IJECE

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“…Therefore, the system noise covariance matrix should be increased appropriately. The sensitivity of the Kalman filter depends on the ratio of the system noise covariance matrix Q and the observed noise covariance matrix R [11]. If the description of Q and R in practical applications is inaccurate, the filtering estimation error will be very large or even divergent [11], [12].…”

Section: Measurements Updatementioning

confidence: 99%

“…The sensitivity of the Kalman filter depends on the ratio of the system noise covariance matrix Q and the observed noise covariance matrix R [11]. If the description of Q and R in practical applications is inaccurate, the filtering estimation error will be very large or even divergent [11], [12]. Therefore, the adaptive Kalman filter based on the innovation sequence is used to accurately estimate Q and R [13], [14] to enhance the stability of the filter.…”

Section: Measurements Updatementioning

confidence: 99%

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

WANG

Tan

2023

IEICE Trans. Inf. & Syst.

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The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the antigross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.

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“…The noise covariance matrices are defined as time-varying matrices, but it is difficult to define how these matrices evolve over time. Some stochastic filters define mathematical expressions for these two noise covariance matrices, obtaining new matrices for each discrete time instant k ∈ N. They are the Adaptive Kalman Filter (AKF) (Brown and Rutan, 1985), the Adaptive Extended Kalman Filter (AEKF) (Nayak et al, 2016) and the Optimal Adaptive Kalman Filter (OAKF) (Yang and Gao, 2006) . Some of these filters are applied to target tracking problems as in Kumar et al (2017); Tripathi et al (2016); Dang (2008).…”

Section: Introductionmentioning

confidence: 99%

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

Melo

Frencl

Oroski

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Improving the precision of an instrument is a hard task. Generally, this can be accomplished using stochastic filters, e.g., Kalman Filter (KF). Normally, these filters have to be tuned by an arbitrary definition of its noise covariance matrices. In this context, the objective of this paper is estimate dynamically the measurement noise covariance matrix, by means of the Bayes Theorem (BT), in a Kalman Filter algorithm, using the Inverted-Wishart distribution (IW-R Kalman Filter). The proposed method is compared with three other classic stochastic filters. In this comparison the IW-R Kalman Filter has achieved the best RMSE (Root Mean Square Error), outperforming the other filters. Therefore, one can conclude that dynamic estimation of noise covariances could improve the performance of the traditional stochastic filters.

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Comparative study of harmonics estimation in micro grid using adaptive extended Kalman filter

Cited by 9 publications

References 13 publications

Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks

Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

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