<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with Artificial Neural Network Controller (ANNC) and Adaptive Neuro Fuzzy Inference System(ANFIS) controllers. The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network and ANFIS controllers requires less computation time. They have the ability to generalize and can interpolate in between the training data. The ANNC and ANFIS controllers designed were tested on a 75 V, 100 VA STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that ANFIS controller was producing better results than the ANNC.</p></abstract>

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with Artificial Neural Network Controller (ANNC) and Adaptive Neuro Fuzzy Inference System(ANFIS) controllers. The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network and ANFIS controllers requires less computation time. They have the ability to generalize and can interpolate in between the training data. The ANNC and ANFIS controllers designed were tested on a 75 V, 100 VA STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that ANFIS controller was producing better results than the ANNC.

A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment

Department, Thiagarajar College of Engineering, Madurai 625015, Tamilnadu, India

Journal of Electrical Engineering

{"article-title":"A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment"}

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and...

A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment

Pubblicato online: 08 giu 2013

Pagine: 159 - 165

DOI: https://doi.org/10.2478/jee-2013-0023

Parole chiave
ANFIS, artifcial neural network, dSPACE DS1104, STATCOM, pulse width modulation

This content is open access.

A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment

Pubblicato online: 08 giu 2013

Pagine: 159 - 165

DOI: https://doi.org/10.2478/jee-2013-0023

Parole chiaveANFIS, artifcial neural network, dSPACE DS1104, STATCOM, pulse width modulation

This content is open access.

Parole chiave
ANFIS, artifcial neural network, dSPACE DS1104, STATCOM, pulse width modulation