Comprehensive Service Restoration Scheduling in Distribution System Using Apparent Impedance Based and Fuzzy Decision Algorithms

Abstract:

The paper develops a tool to identify the fault type, the fault location, and an associated restoration procedure following a fault in a distribution system. These coupled tasks are important for electric power utilities to provide better services to their customers and to achieve a target reliability level. Fault type and fault location are identified by an apparent impedance-based method in which the linear relationship between the line impedance and distance is established. A service restoration with a number of objectives and constraints are solved by a fuzzy decision method. To demonstrate the effectiveness of the methodology, service restoration a large-scaled interconnected distribution system within the service area of Metropolitan Electricity Authority (MEA) is demonstrated. It found that, following a fault event, the fault type, the fault location and a proper restoration plan can be reached very efficiently and therefore provide valuable information decision support and flexibility to distribution system operators for restoration scheduling of distribution systems.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 3980-3986
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.3980
• Online since: January 2012
• Authors: Jutanon Kaewmanee, Somporn Sirisumrannukul
• Keywords: Fault Identification, Fuzzy Reasoning, Service Restoration Planning

References

[1] J. Mora, J. Melendez, Marc Vinyoles, J. Sanchez and Manel Castro: An Overview to Fault Location Method in Distribution System Based on Single End Measures of Voltage and Current, University of Girona.

DOI: 10.24084/repqj02.260

[2] IEEE Std C37. 114-2004: IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines.

[3] Han-Ching Kuo and Yuan-Yin Hsu: Distribution System Load Estimation and Service Restoration Using A Fuzzy Set Approach, IEEE Trans. Power Delivery, Vol. 8, No. 4, October (1993), p.1950-(1957).

DOI: 10.1109/61.248307

[4] Yuan-Yin Hsu and Han-Ching Kuo: A Heuristic Based Fuzzy Reasoning Approach For Distribution System Service Restoration, IEEE Trans. Power Delivery, Vol. 9, No. 2, April (1994), pp.948-953.

DOI: 10.1109/61.296277

[5] Chao-Ming Huang: Multiobjective Service Restorat-ion of Distribution Systems Using Fuzzy Cause-Effect Networks, IEEE Trans. Power Systems, Vol. 18, No. 2, May (2003), pp.867-874.

DOI: 10.1109/tpwrs.2003.811003

[6] M. R. Mansour, A. C. Santos, J. B. Lodon Jr., A. C. B. Delbem and N. G. Bretas: Energy Restoration in Distribution System using Multi-objective Evolutionary Algorithm and Efficient Data Structure, IEEE Bucharest Power Tech Conference, June (2009).

DOI: 10.1109/ptc.2009.5282205

[7] Adly A. Girgis and Melisa B. Johns.: A Hybrid Expert System for Faulted Section Identification, Fault Type Classification and Section of Fault Location Algorithms, IEEE Transactions on Power Delivery, Vol. 4, No. 2, April (1989), pp.978-984.

DOI: 10.1109/61.25578

[8] John J. Grainger and William D. Stevenson, JR.: Power System Analysis, McGraw-Hill, (1994).

[9] N. Rugthaicharoencheep and S. Sirisumrannukul: Feeder Reconfiguration for Loss Reduction in Distribution System with Distributed Generators by Tabu Search, GMSARN International Journal 3, Vol. 3, No. 2, June (2009), pp.47-54.

DOI: 10.1109/supergen.2009.5348230