کنفرانس بین المللی ماشین و محرکه های الکتریکی

صفحه اصلی / The 4th International Conference on Electrical Machines and Drives

A data-driven load shedding method based on generative adversarial networks considering missing data and unlearned fault

نویسندگان :

Nazanin Pourmoradi¹ Sasan Azad² Mohammadtaghi Ameli³

1- دانشگاه شهید بهشتی 2- دانشگاه شهید بهشتی 3- دانشگاه شهید بهشتی

کلمات کلیدی :

Load shedding،generative adversarial،convolutional neural network،Rotor angle stability،missing data،unlearned fault

چکیده :

Event-based load shedding (ELS) is a critical emergency countermeasure against transient rotor angle instability in power systems. ELS based on deep learning has recently achieved promising results. However, in real-world power systems, model input data may be incomplete, and faults may occur that are not in the training database. In this situation, the accuracy of the model decreases. To address these problems, this paper presents a data-driven LS model based on transfer learning. First, the missing data challenge is solved by using a generative adversarial network (GAN), an algorithm for unsupervised deep learning based on two competing neural networks. Then, a convolutional neural network (CNN) model is trained for optimal LS. Finally, when the model encounters unlearned faults in the online application, transfer learning is used to update the model with a small database. The proposed model's test on the IEEE 39 bus system shows its effective performance.

لیست مقالات

لیست مقالات بایگانی شده

Analysis of the Impact of Initial Rotor Position Estimation Error on the Speed, Current, and Startup Torque of Synchronous Reluctance Motors Considering Magnetic Saturation

Farzad Bodaghi - Hamidreza Pairo

Analysis of Short-Circuit Fault Types in Switched Reluctance Generators for Low-Power Wind Turbine Applications

Emad Mirzaei - Hossein Torkaman - Milad Babalou

Few-Shot Learning for Precise Excitation-Winding Fault Localization in Resolvers

Ali Pour Ghoraba - Zahra Nasiri-Gheidari

Thermo‑Fluid Feasibility Analysis of Alternator Rotor Winding Replacement via Computational Fluid Dynamics

Omid Mahdavi keshavar - Ehsan Mohammadian - Hadi Zarafshani

Optimal Design for Enhanced Torque and Efficiency of a Dual-Stator Dual-Rotor Axial-Flux Switching PM Motor for Lightweight EVs

Mohammad Farahzadi - Mohammadreza Naeimi - Amir Ebrahimi Shohani - Fariba Farrokh - Mohammad Sedigh Toulabi - Karim Abbaszadeh

Effect of Global Vacuum Pressure Impregnation Parameters on Quality of Impregnated Stators

Fatemeh Ramezani - Ali Alaeddini - Mahdi Khademnahvi - Mansour Torabi

کنترل برداری با استفاده از کنترل کننده مدلغزشی برای موتور القایی تک فاز بدون حسگر سرعت با وجود نامعینی های پارامتری موتور

علی جعفری - مهرداد غلامی

Performance Comparison of Different Methods of Torque Ripple Reduction for Permanent Magnet-assisted Synchronous Reluctance Motor

Amir Hosein Amiri - Seyed Ehsan Abdollahi - Saeed Hasanzadeh

Improved Bearing Faults Detection in Induction Motor Through 2-D Convolutional Neural Network

Seyed Javad Mirmohammad Meiguni - Abolfazl Vahedi - Vahid Rahimi Bafrani - Akbar Rahideh

A Brief Review on the Application of Machine Learning for Transient Stability Prediction of Synchronous Generators to Proposing a Comparative Framework

Ali Abdalredha - Alireza Sobbouhi - ابوالفضل واحدی

بیشتر

ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.0.1