This paper presents the genetic algorithm (GA) and particle swarm optimization (PSO) based frequency regulation for a wind‐based microgrid (MG) using reactive power balance loop. MG, operating from squirrel cage induction generator (SCIG), is employed for exporting the electrical power from wind turbines, and it needs reactive power which may be imported from the grid. Additional reactive power is also required from the grid for the load, directly coupled with such a distributed generator (DG) plant. However, guidelines issued by electric authorities encourage MGs to arrange their own reactive power because such reactive power procurement is defined as a local area problem for power system studies. Despite the higher cost of compensation, static synchronous compensator (STATCOM) is a fast‐acting FACTs device for attending to these reactive power mismatches. Reactive power control can be achieved by controlling reactive current through the STATCOM. This can be achieved with modification in current controller scheme of STATCOM. STATCOM current controller is designed with reactive power load balance for the proposed microgrid in this paper. Further, gain values of the PI controller, required in the STATCOM model, are selected first with classical methods. In this classical method, iterative procedures which are based on integral square error (ISE), integral absolute error (IAE), and integral square of time error (ISTE) criteria are developed using MATLAB programs. System performances are further investigated with GA and PSO based control techniques and their acceptability over classical methods is diagnosed. Results in terms of converter frequency deviation show how the frequency remains under the operating boundaries as allowed by IEEE standards 1159:1995 and 1250:2011 for integrating renewable‐based microgrid with grid. Real and reactive power management and load current total harmonic distortions verify the STATCOM performance in MG. The results are further validated with the help of recent papers in which frequency regulation is investigated for almost similar power system models. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library, (ii) designing of STATCOM current controller with PI controller, (iii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes and their interfacing with proposed simulink model, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management between STATCOM and grid, and (vii) validation of results with available literature.
Coordinated failure response and recovery in a decentralized microgrid architecture
The challenge of failure management emerges in decentralized architectures due to the distributed nature of the layered control process. Failures in microgrid system may occur at the microgrid level or any of the control layers. A failure detection and response mechanism is required to attain a reliable, fault-tolerant microgrid operation. This paper introduces a Failure Management Unit as an essential function in a microgrid Energy Management System. The proposed unit comprises failure detection mechanisms and a recovery algorithm. The proposed system is applied to a microgrid case study and shows a robust detection and recovery outcome during a system failure. The real-time experimental results were achieved using Hardware-In-the-Loop platform. Coordination between controllers during the recovery period requires low-bandwidth communications, which has no significant overhead on the communication infrastructure.
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- Award ID(s):
- 1650470
- NSF-PAR ID:
- 10082505
- Date Published:
- Journal Name:
- 2017 IEEE Energy Conversion Congress and Exposition (ECCE)
- Page Range / eLocation ID:
- 4821 to 4825
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ECCE.2017.8096819
