IJRR

International Journal of Research and Review

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Research Paper

Year: 2019 | Month: August | Volume: 6 | Issue: 8 | Pages: 468-477

Voltage Stability Assessment and Loss Minimisation by Power System Reconfiguration

Pratiksha Molekar, Dr. Vijay N. Pande

Electrical Department, College of Engineering, Pune, India

Corresponding Author: Pratiksha Molekar

ABSTRACT

This paper represents a circuit theory approach for voltage stability assessment in an interconnected power system network. The basic methodology implied in this technique is the investigation of each line of the system by calculating line stability indices. Here, an interconnected IEEE 14-bus network has been reconfigured into 12-, 10- and 8-bus networks using graph theory. The line stability index and fast voltage stability indicators have been used for voltage stability assessment under normal and faulted conditions for the original IEEE 14-bus network and the reconfigured i.e. 12- bus, 10- bus and 8-bus networks. Genetic algorithm has been used to determine the optimal operating condition i.e. optimum value of line stability index and fast voltage stability index with best voltage stability for the original and the reconfigured networks. The voltage stability assessment under normal and faulted conditions can be efficiently determined for the reconfigured networks compared with the original network, this has been shown by the results. It analyses the performance of line stability indices. These indices were tested in IEEE 14 bus bar test systems, with satisfactory results. The effect of reconfiguration of 14 bus power system network on power losses in branches and computation time required for finding stability indices has been observed. Also the effect of reactive load variation on stability indices of respective buses and maximum allowable load or maximum loadability of bus is found.

Key words: Critical line, line outage, line stability index(Lmn), fast voltage stability index (FVSI),maximum loadability, voltage instability, voltage stability analysis, genetic algorithm (GA)

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