Loss of Excitation Faults Detection in Hydro- Generators Using an Adaptive Neuro Fuzzy Inference System

نویسندگان

  • M. S. Abdel Aziz
  • M. Elsamahy
  • M. A. Moustafa Hassan
  • F. Bendary
چکیده

This paper presents a new approach for Loss of Excitation (LOE) faults detection in Hydrogenerators using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a 345kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R and X) and the generator RMS Line to Line voltage and Phase current (Vtrms and Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other techniques. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process and the obtained results are very promising.

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

Public Transportation and Private Car: A System Dynamics Approach in Understanding the Mode Choice

63 Applications of ANFIS in Loss of Excitation Faults Detection in Hydro-Generators Mohamed Salah El-Din Ahmed Abdel Aziz, Dar Al-Handasah (Shair and Partners), Giza, Egypt Mohamed El Samahy, Elec. Power Dept., The higher Institute of Engineering, El-Shorouk Academy, Egypt Mohamed A. Moustafa Hassan, Electrical Power Department, Faculty of Engineering, Cairo University, Giza, Egypt Fahmy El Ben...

متن کامل

Nusselt Number Estimation along a Wavy Wall in an Inclined Lid-driven Cavity using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to determine the Nusselt number (Nu) along a wavy wall in a lid-driven cavity under mixed convection regime. Firstly, the main data set of input/output vectors for training, checking and testing of the ANFIS was prepared based on the numerical results of the lattice Boltzmann method (LBM). Then, the ANFIS was develope...

متن کامل

Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)

Streamflow forecasting has an important role in water resource management (e.g. flood control, drought management, reservoir design, etc.). In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) is used for long-term streamflow forecasting (monthly, seasonal) and moreover, cross-validation method (K-fold) is investigated to evaluate test-training data in the model.Then,...

متن کامل

Loss of Excitation Detection in Doubly Fed Induction Generator by Voltage and Reactive Power Rate

The doubly fed induction generator (DFIG) is one of the most popular technologies used in wind power systems. With the growing use of DFIGs and increasing power system dependence on them in recent years, protecting of these generators against internal faults is more considered. Loss of excitation (LOE) event is among the most frequent failures in electric generators. However, LOE detection stud...

متن کامل

Voting Algorithm Based on Adaptive Neuro Fuzzy Inference System for Fault Tolerant Systems

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

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 2016