Load Profiling with Fuzzy Self-Organizing Algorithms
被引:0
|
作者:
Gavrilas, Mihai
论文数: 0引用数: 0
h-index: 0
机构:
Tech Univ Iasi, Power Syst Dept, Iasi, RomaniaTech Univ Iasi, Power Syst Dept, Iasi, Romania
Gavrilas, Mihai
[1
]
Ivanov, Ovidiu
论文数: 0引用数: 0
h-index: 0
机构:
Tech Univ Iasi, Power Syst Dept, Iasi, RomaniaTech Univ Iasi, Power Syst Dept, Iasi, Romania
Ivanov, Ovidiu
[1
]
Gavrilas, Gilda
论文数: 0引用数: 0
h-index: 0
机构:Tech Univ Iasi, Power Syst Dept, Iasi, Romania
Gavrilas, Gilda
机构:
[1] Tech Univ Iasi, Power Syst Dept, Iasi, Romania
来源:
NEUREL 2008: NINTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS
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2008年
关键词:
Distribution systems;
Fuzzy logic;
Load profiling;
Self-organization;
D O I:
暂无
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
This paper describes an enhanced fuzzy self-organizing algorithm (EF-SOM) to address the problem of consumer classification in electric distribution networks based on the shape of the load profiles (LPs). This algorithm is a modified form of the standard fuzzy Kohonen algorithm, which determines the deviations between metered-LPs and the prototypes of the typical LPs using weighted windows around It he peak and valley hours. The EF-SOM algorithm was tested on an independent load profile database, proofing its ability to filter outliers LPs and to produce realistic classification results.