Optimal Size & Location of Distributed Generation using Bird Swarm Optimization with Cuckoo Search Sorting Algorithm
K. Sri Kumar
K. Sri Kumar, Department of EEE, University College of Engineering, Kakinada (Andhra Pradesh), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 781-787 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11470986S319/19©BEIESP | DOI: 10.35940/ijeat.F1147.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper a natural habitat inspired metaheuristic Bird Swarm Optimization algorithm is implemented with improvisations made for the development of solution for the optimal allocations and optimal size prediction problem of Dispersed generation/ Distributed Generator in a radial power system distribution system in consideration of the drawbacks in the previous algorithms both in the context of convergence time and the optimal sizing with respect to the cost analysis for operation of the system with different number of DG’s installed in such a way that the optimal locations and sizes of DG’s installed is finalised with highest priority to the economical operation along with the immediate priority given to the network losses along with voltage deviations. To avoid the draw backs in previous optimisation algorithm regarding accuracy and run time. Along with the Cognitive component Weighted factor Bird Swarm optimisation (CWFBSO) algorithm a new concept is introduced called DG Size tuner Such that cost effective economical installation is possible as by the size tuner it is possible to compare the losses and voltage profile within the mean difference of the optimal sizes of final allocation determined by the main algorithm i.e., CWFBSO. Obtained results using CWFBSO in determining optimal locations and sizes of DG’s is capable showing good performance with less run time and convergence time and by using size tuner the optimal size selected economically with respect to less voltage deviation and minimal losses.
Keywords: Load Flow, Forward-Backward Sweep Method, Loss Factors Analysis, Voltage Sensitivity Factors, Cognitive Component, Weighted Factor, Bird Swarm Optimisation, Distributed Generation, Optimal Location, Optimal Size, Real Power Losses, Size Tuner.
Scope of the Article: Algorithm Engineering