Design of fault-tolerant control system for distributed energy resources based power network using Phasor Measurement Units

Author:

Butt Aiman Shahid1,Huda Noor ul1ORCID,Amin Arslan Ahmed1ORCID

Affiliation:

1. Department of Electrical Engineering, FAST National University of Computer and Emerging Sciences, Chiniot, Punjab, Pakistan

Abstract

Distributed Energy Resources (DERs) play a significant role in reducing carbon emissions and improving the power system, nonetheless, maintaining the stability of the system parameters under faulty conditions is a major challenge. To minimize fault duration and maximize the system’s fault tolerance capability while maintaining affordability and being environment friendly, DERs supported by storage units are used in parallel with the main grid which offers promising results. This paper proposes the utilization of DERs as the primary source while the main grid shares the peak load. The paper also discusses the application of Phasor Measurement Units (PMUs) to record current and voltage values. PMUs are used to detect the fault in its early stage and communicate to the central controller to shift the load on storage units and isolate fault locations. The operation is controlled at two levels, that is at the load end and the junction point of the grid and DER. Any anomaly detected by PMUs is tested for the fault location, where faults are then controlled and minimized using the proposed method while keeping economic factors under consideration. The system is tested in light of results from mathematical modeling and design simulation which show very low latency time against demand response and quick isolation of fault location. A comparison with existing and previous works also shows the promising performance of the proposed fault-tolerant power system using PMUs.

Publisher

SAGE Publications

Subject

Applied Mathematics,Control and Optimization,Instrumentation

Reference34 articles.

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3. Design of Active Fault Tolerant Control System for Air Fuel Ratio Control of Internal Combustion Engines Using Artificial Neural Networks

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