Two-staged simplified fuzzy inference for dynamic contingency screening in power systems

2002 ◽  
Author(s):  
H. Mori ◽  
E. Ando
Keyword(s):  
Power Systems ◽  
Fuzzy Inference ◽  
Contingency Screening

Genetic algorithm-based method for contingency screening in power systems

1996 ◽  
Vol 116 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Hiroumi Saitoh ◽  
Yutaka Takano ◽  
Junichi Toyoda
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Contingency Screening

scholarly journals Fuzzy Inference System for Integrated Vvc In Isolated Power Systems

2014 ◽  
Vol 5 (1) ◽  
pp. 91-106 ◽  
Author(s):  
Vega-Fuentes E ◽  
Cerezo-Sánchez J M ◽  
León-del Rosario S ◽  
Vega-Martínez A
Keyword(s):  
Power Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System

scholarly journals A Neuro-fuzzy Approach for Predicting Load Peak Profile

2015 ◽  
Vol 5 (6) ◽  
pp. 1304 ◽  
Author(s):  
Abdellah Draidi ◽  
Djamel Labed
Keyword(s):  
Power Systems ◽  
Fuzzy Inference ◽  
Load Forecasting ◽  
Infrastructure Development ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables ◽  
Load Peak ◽  
Inference Systems ◽  
Mathematical Process

<p>Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development.</p> <p>Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain.</p> <p>Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously.</p> In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.

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