A contemporary review of the applications of nature-inspired algorithms for optimal design of automatic generation control for multi-area power systems View Full Text


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Article Info

DATE

2017-05-27

AUTHORS

Farshad Kalavani, Milad Zamani-Gargari, Behnam Mohammadi-Ivatloo, Mohammad Rasouli

ABSTRACT

The modern electric grid is one the most complex man-made control systems. Proportional–integral–derivative (PID) controllers are widely used in a variety of applications including automatic generation control (AGC), automatic voltage regulators, power system stabilizers and flexible AC transmission system devices. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Using the PIDs, AGC keeps the balance between generation and load demand in order to minimize frequency deviations. Furthermore, the AGC regulates the tie-line power exchange and facilitates bilateral contracts spanning over several control areas, thus ensuring reliable operation of the interconnected transmission system. Since the power system load variations occur continually, generation control is set to automatic to restore the frequency after disturbances. The PID controllers have the advantage of simple structure, good stability, and high reliability. However, a robust and efficient tuning of PID parameters are still being investigated using different techniques. One of the recent areas of such studies is nature-inspired algorithms. The main objective of utilizing nature-inspired algorithms is to optimize parameters of several controllers simultaneously. This paper reviews the latest applications of various nature-inspired algorithms for optimal design of AGC control in power systems. Different algorithms, proposed in the recent literature, are classified based on the type of controller, objective function and test systems. More... »

PAGES

1-32

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10462-017-9561-7

DOI

http://dx.doi.org/10.1007/s10462-017-9561-7

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https://app.dimensions.ai/details/publication/pub.1085614468


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