Electricity Characteristic Recognition Study Based on Fuzzy Clustering-Quantum Particle Swarm Algorithm

doi:10.3969/j.issn.1000-7229.2015.08.014

Abstract

Abstract:

In allusion to such defects as sensitive to initial clustering center and not convenient to determine clustering number during utilizing traditional fuzzy C-Means （FCM） algorithm to extract power load patterns, this paper constructed objective function to reflect clustering effect, and used a quantum particle swarm algorithm for global optimization to determine the optimal clustering center and classification aiming at the defects of traditional intelligent optimization algorithm, such as easy convergence, falling into local optimum, etc. After determining the optimal clustering center and clustering number, the characteristics vector was constructed to fully reflect each kind of load. At last, by compared with the calculated results of traditional FCM algorithm, the effectiveness and correctness of the proposed algorithm in electricity recognition were verified.

Key words: smart city, load characteristic, classification and synthesis, quantum particle swarm algorithm, fuzzy clustering

CLC Number:

TM 714

GUO Kunya，XIONG Xiong，JIN Peng，SUN Qian，JING Tianjun. Electricity Characteristic Recognition Study Based on Fuzzy Clustering-Quantum Particle Swarm Algorithm[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(8): 84-88.

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