Modified Invasive Weed Optimization based on Fuzzy PSS in Multi-machine Power System

نویسندگان

  • O. Abedinia
  • N. Amjady
  • A. Akbari Foroud
  • H. A. Shayanfar
چکیده

In this paper a new Modified Invasive Weed Optimization is proposed to find a parameters of fuzzy PID controller as a stabilizer in multi-machine power system. Actually, IWO is a bio-inspired numerical algorithm which is inspired from weed colonization and motivated by a common phenomenon in agriculture that is colonization of invasive weeds. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Thus, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by MIWO that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to Single machine connected to Infinite Bus (SMIB) and IEEE 3-9 bus power system. The proposed technique is compared with some intelligent algorithms through ITAE and FD.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Design of Multi-Stage Fuzzy PID Bundled Artificial Bee Colony for Multi-machine PSS

This paper presents a new strategy based on Multi-stage Fuzzy (MSF) PID controller based on Artificial Bee Colony (ABC) for damping Power System Stabilizer (PSS) in multi-machine environment. The recent studies in artificial intelligence demonstrated that the ABC optimization is strong intelligent method in complicated stability problems. Also, finding the parameters of PID controller in power ...

متن کامل

Design of Multi-Stage Fuzzy PID Bundled Artificial Bee Colony for Multi-machine PSS

This paper presents a new strategy based on Multi-stage Fuzzy (MSF) PID controller based on Artificial Bee Colony (ABC) for damping Power System Stabilizer (PSS) in multi-machine environment. The recent studies in artificial intelligence demonstrated that the ABC optimization is strong intelligent method in complicated stability problems. Also, finding the parameters of PID controller in power ...

متن کامل

Optimization of Power System Stabilizer for Multi-Machine Power System using Invasive Weed Optimization Algorithm

In this paper, an evolutionary algorithm-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) is proposed for multi-machine power system. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. Owing to its superior performance in comparison with many other existing meta-heuristics, it has used to search for optimal...

متن کامل

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

In this paper, two evolutionary algorithmsInvasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a der...

متن کامل

Studies with a Generalized Neuron Based PSS on a Multi-Machine Power System

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012