Adaptive differential evolution algorithm for efficient reactive power management

[Display omitted] •An adaptive differential evolution procedure is presented to solve optimal reactive power dispatch problem.•A multi-objective function aims at minimizing power losses and enhancing voltage profile.•An investigated strategy for adaptive penalty factor to alleviate the effects of de...

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Veröffentlicht in:Applied soft computing Jg. 53; S. 336 - 351
Hauptverfasser: Sakr, Walaa S., EL-Sehiemy, Ragab A., Azmy, Ahmed M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2017
Schlagworte:
Adaptive penalty factor
Modified differential evolution algorithm
Multi-objective function
Optimal reactive power management
Multi-objective function
Optimal reactive power management
Adaptive penalty factor
Modified differential evolution algorithm
ISSN:1568-4946, 1872-9681
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Zusammenfassung:[Display omitted] •An adaptive differential evolution procedure is presented to solve optimal reactive power dispatch problem.•A multi-objective function aims at minimizing power losses and enhancing voltage profile.•An investigated strategy for adaptive penalty factor to alleviate the effects of dependent variable violation.•Numerical applications are carried out on three standard IEEE test systems and on a Western Delta real system.•The flexibility of synchronous machines as reactive power sources is proven compared to switchable devices. This paper introduces a proposed procedure to solve the optimal reactive power management (ORPM) problem based on a multi-objective function using a modified differential evolution algorithm (MDEA). The proposed MDEA is investigated in order to enhance the voltage profile as well as to reduce the active power losses by solving the ORPM problem. The ORPM objective function aims to minimize transmission power losses and voltage deviation considering the system constraints. The MDEA aims to enhance the convergence characteristic of the differential evolution algorithm through updating the self-adaptive scaling factor, which can exchange information dynamically every generation. The scaling factor dynamically adopts the global and local searches to efficiently eliminate trapping in local optima. In addition, a strategy is developed to update the penalty factor for alleviating the effects of various system constraints. Numerical applications of different case studies are carried out on three standard IEEE systems, i.e., 14-bus, 30-bus and 57-bus test systems. Also, the proposed procedure is applied on Western Delta Network, which is a real part of the Egyptian main grid system. The flexibility of synchronous machines to provide controllable reactive power is proven with less dependency on the discrete reactive power controllers, such as installing the switchable devices and variations of tap changers. The obtained results show the effectiveness of the proposed enhanced optimization algorithm as an advanced optimization technique that was successively implemented with good performance characteristics.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.01.004