In this paper, an electromagnetic compatibility estimator is proposed using an artificial neural network with a scaled conjugate gradient algorithm. Neural networks are trained with the help of seven different optimization algorithms in MATLAB. Their performance is evaluated on the basis of number of neurons, desired output, and mean-squared error in offline mode in MATLAB. Among seven algorithms, scaled conjugate gradient algorithm is found to be the best choice. Hence, it is implemented in LabVIEW for online assessment of electromagnetic compatibility issues. Voltage dip, swell, and harmonics are generated with the help of an experimental setup. It consists of 230 V, 50 Hz input voltage supply, microcontroller, variac, and solid-state relays. It is interfaced to the LabVIEW software with the help of an NI USB 6361 data acquisition system. It enabled the continuous online monitoring of various signals. Along with voltage dip and swell, harmonics are also evaluated with the help of spectrum analyzer in LabVIEW. The detailed description of a hardware setup and mathematical modeling of trained network is given in this paper.

Chetan Bhimrao Khadse

School of Electrical Engineering

Engineering and Technology

Madhuri A Chaudhari

Vijay B Borghate

MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Electromagnetic Compatibility Estimator Using Scaled Conjugate Gradient Backpropagation Based Artificial Neural Network

IEEE Transactions on Industrial Informatics

This paper proposes conjugate gradient back-propagation based artificial neural network for real time power quality assessment. The novel real time voltage sag and swell detection, classification scheme using artificial neural network is presented. The ANN is trained in MATLAB using voltage sampled signal data and corresponding parameters of the neural network are utilized to implement in LabVIEW in real time sag swell detection and classification. The Levenberg Marquardt, the resilient back-propagation and conjugate back-propagation algorithm performance is evaluated in MATLAB to find the best neural network for real time application. Among these three back-propagation algorithms, conjugate gradient algorithm has better performance for real time power quality monitoring. The mathematical model of Conjugate gradient back-propagation neural network is implemented in LabVIEW. Real time voltage signals of sag and swell for different time duration and magnitude, intensity are acquired from hardware experimental setup. Hardware setup mainly consists of single phase 230 V voltage source, microcontroller, dimmerstat and solid state relays. Voltage signals of sag and swell are sensed using high precision voltage sensor. Data acquisition system is used to acquire the signal from voltage sensor. The output of data acquisition system is given to the personal computer with LabVIEW. The proposed monitoring system also detects odd and even harmonic components in the voltage signal acquired using FFT. Real time hardware results obtained using proposed power quality monitoring system for detection of voltage sag, swell and harmonics claims the suitability, robustness and adaptability to monitor power quality issues.

International Journal of Electrical Power & Energy Systems

Conjugate gradient back-propagation based artificial neural network for real time power quality assessment

Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

IEEE Transactions on Power Delivery

Integrated Fault Location and Power-Quality Analysis in Electric Power Distribution Systems

IEEE Transactions on Energy Conversion

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

2015 Annual IEEE India Conference (INDICON)

Comparison between three back-propagation algorithms for power quality monitoring

Journal	Data powered by TypesetIEEE Transactions on Industrial Informatics
Publisher	Data powered by TypesetIEEE
ISSN	15513203
Open Access	No