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What is Fuzzy System

Chapter 1
Handbook of Research on Power and Energy System Optimization
It is a component of machine learning techniques that takes membership values within 0 to 1 unlike crisp sets.
Published in Chapter:
Fuzzy-Logic-Based Reactive Power and Voltage Control in Grid-Connected Wind Farms to Improve Steady State Voltage Stability
Tukaram Moger (National Institute of Technology Karnataka, India) and Thukaram Dhadbanjan (Indian Institute of Science Bangalore, India)
Copyright: © 2018 |Pages: 54
DOI: 10.4018/978-1-5225-3935-3.ch001
Abstract
This chapter presents a fuzzy logic approach for reactive power and voltage control in grid-connected wind farms with different types of wind generator units to improve steady state voltage stability of power systems. The load buses' voltage deviation is minimized by changing the reactive power controllers according to their sensitivity using fuzzy set theory. The proposed approach uses only a few high sensitivity controllers to achieve the desired objectives. A 297-bus-equivalent grid-connected wind system and a 417-bus-equivalent grid-connected wind system are considered to present the simulation results. To prove the effectiveness of the proposed approach, a comparative analysis is also carried out with the conventional linear-programming-based reactive power optimization technique. Results demonstrated that the proposed approach is more effective in improving the system performance as compared with the conventional existing techniques.
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Chapter 164
Multilayer Optimization Approach for Fuzzy Systems
Approach of the computational intelligence that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data.
Published in Chapter: Multilayer Optimization Approach for Fuzzy Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 2
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
A fuzzy system (FS) is any Fuzzy Logic based system, which either uses Fuzzy Logic as the basis for knowledge representation using different forms of knowledge. Systems variables interactions and intermodal relationship can be modeled using different ways such as membership functions and shape analysis of membership function. Membership functions are mathematical way of representing values for inference mechanism.
Published in Chapter: An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 109
Fuzzy Systems Modeling: An Introduction
A system formulated and described by fuzzy set-based real-valued functions.
Published in Chapter: Fuzzy Systems Modeling: An Introduction; From: Encyclopedia of Artificial Intelligence
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Chapter 7
Reactive Power Loss Index for Identification of Weak Nodes and Reactive Compensation Analysis to Improve Steady State Voltage Stability
It is a component of machine learning techniques which takes membership values within 0 to 1 unlike crisp sets.
Published in Chapter: Reactive Power Loss Index for Identification of Weak Nodes and Reactive Compensation Analysis to Improve Steady State Voltage Stability; From: Novel Advancements in Electrical Power Planning and Performance
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Chapter 7
Applying CI in Biology through PSO
It is a component machine learning techniques which takes membership values within 0 to 1 unlike crisp sets.
Published in Chapter: Applying CI in Biology through PSO; From: Handbook of Research on Computational Intelligence Applications in Bioinformatics
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Chapter 205
Rough Set-Based Neuro-Fuzzy System
A system whose variables range over states that are fuzzy sets. A fuzzy system is capable of modelling the linguistic terms that are expressions of human language.
Published in Chapter: Rough Set-Based Neuro-Fuzzy System; From: Encyclopedia of Artificial Intelligence
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Chapter 103
Fuzzy Control Systems: An Introduction
A system formulated and described by fuzzy set-based real-valued functions.
Published in Chapter: Fuzzy Control Systems: An Introduction; From: Encyclopedia of Artificial Intelligence
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Chapter 19
Fuzzy Based Modeling, Control, and Fault Diagnosis of Permanent Magnet Synchronous Generator
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