Three-phase four-wire wind-diesel based microgrid

Pathak, Geeta; Singh, Bhim; Panigrahi, Bijaya Ketan

doi:10.1109/icpes.2016.7584037

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…A LQR approach has been discussed in [137] and validated for its performance for unbalance mitigation using a HIL platform. An adaptive algorithm is discussed in [138], which combines a least mean square and sign algorithm, which is used to achieve VU in the network. Toman and Asumadu [139] explained a distributed finite control set MPC for the same, while the approach in [140] is to reduce NS impedance of the inverter to improve the performance.…”

Section: Fig 4 Block Diagram Of a Typical Control Scheme For Addressmentioning

confidence: 99%

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Unbalance mitigation strategies in microgrids

Vijay

Doolla

Chandorkar

2020

IET Power Electronics

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Unbalance or asymmetry in the distribution network is a well-known power quality issue. In the modern active distribution system, with the increasing penetration of renewables, this phenomenon becomes more pronounced. In the context of microgrids (MGs), several works have been proposed for the management and mitigation of the unbalance, for both the sharing of unbalanced load and maintaining the voltage quality in the islanded mode and for the control of distributed generators in the grid-connected mode during unbalanced conditions. This study comprehensively reviews, summarises, and classifies the various strategies of the unbalance mitigation techniques for the islanded and grid-connected modes of operation for threephase MGs and presents the possible challenges and avenues for future investigations on the topic.

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Section: Fig 4 Block Diagram Of a Typical Control Scheme For Addressmentioning

confidence: 99%

“…The objective of the unbalance compensation or mitigation depends upon the conditions of the MG and the operating mode (islanded or grid‐connected). Unbalance mitigation in islanded MGs [18, 21, 22, 23, 25, 27–32, …”

Section: Unbalance Mitigation In the Islanded Modementioning

confidence: 99%

Unbalance mitigation strategies in microgrids

Vijay

Doolla

Chandorkar

2020

IET Power Electronics

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“…In [25], horizon optimization is utilized for predictive control using a genetic algorithm. In [26], the prediction of the next two samples is used instead of predicting the next sample. Moreover, [27] proposes a new predictive control method, called fast predictive control.…”

Section: Study Of a Microgrid Containing A Dg Sourcementioning

confidence: 99%

“…Figure 2 shows the finite state model predictive control (FS-MPC). e algorithm for solving this method includes the following steps [26]: A : Load current measurement; B : Prediction of the load current in the next samples for all possible switching states according to the following equation:…”

Section: E Finite State Model Predictive Control (Fs-mpc)mentioning

confidence: 99%

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Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Sayedi

Fatehi

Simab

2022

International Transactions on Electrical Energy Systems

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The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

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