A robust dynamic state estimation for droop controlled islanded microgrids

Farag, Hany E. Z.; Saxena, Shivam; Asif, Amir

doi:10.1016/j.epsr.2016.05.030

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…The Kalman filter (KF) is an optimal estimator called the linear quadratic problem. The KF is a statistical characterization of an estimation problem [35], [36]. The linear quadratic problem estimates the instantaneous ''state'' of a linear dynamic system subjected to a white noise disturbance through techniques linearly related to the state and corrupted by the noise.…”

Section: A Kalman Filtermentioning

confidence: 99%

“…In practice, the KF is much more than an optimal estimator. It propagates all the probability distribution of the variables for estimating the states, i.e., it is a complete characterization of the current state of the system, with the influence of all past measurements, which makes it the preferred method for the predictive design of sensor systems [35], [36], [37].…”

Section: A Kalman Filtermentioning

confidence: 99%

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A Methodology of Sensor Fault-Tolerant Control on a Hierarchical Control for Hybrid Microgrids

2023

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This study develops a new Sensor Fault-Tolerant methodology for two-level Centralized Hierarchical Control of isolated microgrids based on a modified Kalman filter algorithm. The main objective is to increase the reliability and safety margins of isolated smart microgrids in the presence of different sensor faults on the secondary control. Consequently, Sensor Fault-Tolerant control reduces the costs because costly redundant hardware is not required. Because of its low computing effort, speed, ease of implementation, and tuning, this method can be used in more complex control configurations, multiple sensor faults, and different hierarchical control levels. The designed Sensor Fault-Tolerant Hierarchical Control System was initially proposed for a grid-forming topology of single-phase BESSs systems connected in cascade to the microgrid. The implemented fault tolerance methodology can maintain control objectives with sensor faults. Consequently, the MG's voltage at the time of the fault does not exceed 5%, and the voltage unbalance at the common coupling point or on the critical bus is compensated to a quality reference value of less than 2%. The performance of the proposed algorithm is tested using the MATLAB/Simulink simulation platform.

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Section: A Kalman Filtermentioning

confidence: 99%

Section: A Kalman Filtermentioning

confidence: 99%

A Methodology of Sensor Fault-Tolerant Control on a Hierarchical Control for Hybrid Microgrids

2023

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“…In [22], a distributed dynamic state estimator was proposed to estimate the operation of the energy resources and the status (connected or inslanded) of the microgrid. In [23], two techniques for state estimation in droop-controlled islanded microgrids were proposed, via an unscented Kalman filter (UKF) [24] and a non-linear particle filter. Microgrids usually comprise high-speed conversion systems associated with extremely fast dynamics, on the order of nano-seconds.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

et al. 2022

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An observable microgrid may become unobservable when sensors are at fault, sensor data is missing, or data has been tampered by malicious agents. In those cases, state estimation cannot be performed using traditional approaches without pseudo-measurements. To address the lack of observability, this article presents the design and implementation of a novel three-phase state estimation method for unobservable and unbalanced AC microgrids, using machine learning techniques, without pseudo-measurements, and under heteroscedastic (i.e., non-constant variance) noise. The proposed machine learning state estimation (MLSE) makes full use of multiple candidate models trained with a small number of power flow simulations via OpenDSS, through random levels of demand and renewable generation in every simulation, enhanced through a proposed Tikhonov regularization operator. To deal with the heteroscedastic nature of measurements, a recursive average model is proposed to accurately estimate the state variables. Results are obtained using real data from a microgrid located on the main campus of the State University of Campinas (UNICAMP), in Brazil. The method can be easily adapted to microgrids with different configurations, distributed energy resources, and measurements. It is shown that the proposed MLSE outperforms the traditional weighted least square (WLS) state estimator.INDEX TERMS Machine learning, microgrids, Moore-Penrose left pseudoinverse, Tikhonov regularization operator, recursive average model.

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“…Hence, VPP necessitates the relevant information concerned itself. Control strategies suggested so far include conventional decentralized secondary voltage-frequency control strategy with regard to adaptive state estimator [34], the reactive power sharing control strategy with regard to voltage compensation [35], dynamic state estimator with respect to nonlinear particle filter [36], voltage drop compensation using adaptive virtual impedance strategy [37], and virtual negative impedance control strategy [38]. As a consequence, decentralized control strategy has effectually ameliorated the reliability, self-adaptability and upgradeability of microgrid.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Khanjanzadeh,

Soleymani,

Mozafari

2021

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In this paper, a novel Power-Frequency Droop Control (PFDC) is introduced to perfectly bring back the system frequency and share the reactive power in isolated microgrid with virtual power plant (VPP). The frequency-based power delivery must be essentially implemented in VPP which can operate as a conventional synchronous generator. It has been attained by enhancing the power processing unit of each VPP to operate as an active generator. The inverter coupling impedance which has been assigned by the virtual impedance technique has reduced the affected power coupling resulting from line resistance. The reference has been subsequently adjusted to compensate the frequency deviation caused by load variation and retrieve the VPP frequency to its nominal value. In addition, the line voltage drop has compensated the voltage drop and load sharing error to obliterate the reactive power sharing imprecision resulting from the voltage deviation. The voltage feedback confirms the correct voltage after compensating the voltage drop. As an illustration, conventional PFDC after a load change cannot restore the system frequency which is deviated from 50 Hz and rested in 49.9 Hz while, proposed PFDC strategy fades away the frequency deviation via compensating the variation of the frequency reference. Likewise, the frequency restoration factor (γ ) has an effective role in retrieving the system frequency, i.e., the restoration rate of the system frequency is in proportion with γ . As a whole, the simulation results have pointed to the high performance of proposed strategy in an isolated microgrid.

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A robust dynamic state estimation for droop controlled islanded microgrids

Cited by 10 publications

References 40 publications

A Methodology of Sensor Fault-Tolerant Control on a Hierarchical Control for Hybrid Microgrids

A Methodology of Sensor Fault-Tolerant Control on a Hierarchical Control for Hybrid Microgrids

Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

Bulletin of the Polish Academy of Sciences: Technical Sciences

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