Title

A Novel Blended State Estimated Adaptive Controller for Voltage and Current Control of Microgrid against Unknown Noise

RIS ID

141064

Publication Details

M. Munsi, A. Siddique, S. Das, S. Paul, M. Islam & M. Moni, "A Novel Blended State Estimated Adaptive Controller for Voltage and Current Control of Microgrid against Unknown Noise," IEEE Access, vol. 7, pp. 161975-161995, 2019.

Abstract

2013 IEEE. In this study, a novel blended state estimated adaptive controller is designed for voltage and current control of microgrid against unknown noise. The core feature of the microgrid (MG) is its ability to integrate more than one distributed energy resource into the main grid. The state of a microgrid may deteriorate due to many reasons, for example malicious cyber-attacks, disturbances, packet losses, etc. Therefore, it is necessary to achieve the true state of the system to enhance the control requirement and automation of the microgrid. To achieve the true state of a microgrid, this study proposes the use of an algorithm based on the unscented kalman filter (UKF). The proposed state estimator technique is developed using an unscented-transformation and sigma-points measurement technique capable of minimizing the mean and covariance of a nonlinear cost function to estimate the true state of a single-phase, three-phase single-source and three-phase multi-source microgrid system. The advantage of the proposed estimator over using extended kalman filter (EKF) is investigated in simulations. The results demonstrate that the use of the UKF estimator produces a superior estimation of the system compared with the use of the EKF. An adaptive PID controller is also developed and used in system conjunction with the estimator to regulate its voltage and current against the number of loads. Deviation in load parameters hamper the function of the MG system. The performance of the developed controller is also evaluated against number of loads. Results indicate the controller provides a more stable and high-tracking performance with the inclusion of the UKF in the system.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/ACCESS.2019.2951429