Online Static Security Assessment Module Using Radial Basis Neural Network Trained with Particle Swarm Optimization

被引：5
作者：
Lekshmi, M. [1 ]
Nagaraj, M. S. [2 ]
机构：
[1] Jain Univ, Bangalore, Karnataka, India
[2] Acharya Inst Technol, Dept EEE, Bangalore, Karnataka, India
来源：
INTELLIGENT AND EFFICIENT ELECTRICAL SYSTEMS | 2018年 / 446卷
关键词：
Security systems; Contingency analysis; Neural network; Particle swarm optimization; Power systems; POWER-SYSTEM; ALGORITHMS;
D O I：
10.1007/978-981-10-4852-4_20
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Secure operation of power system is a big concern in recent power industries. Contingency selection and ranking method used is very important for the security assessment. The multilayer feed-forward artificial neural network and radial basis neural network for implementation of online static assessment take less time to assess the contingency. Composite security index is used for contingency selection and ranking. Generation power, load power, and voltages are used as input to the artificial neural network (ANN) and composite security index is decided by the trained data of ANN. In this paper, radial basis neural network is trained with particle swarm optimization (PSO) to reduce the training time and improve the accuracy. MATAB 2013a is used for building algorithms and testing. IEEE 30 bus system is used as the test system. Single line removal is taken as contingency conditions.
引用
收藏
页码：215 / 224
页数：10
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