Partial discharge pattern classification using multilayer neural networks
نویسندگان
چکیده
منابع مشابه
Partial discharge pattern classification using multilayer neural networks - Science, Measurement and Technology, IEE Proceedings A
Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to auto...
متن کاملPartial Discharge Classification Using Neural Networks and Statistical Parameters
Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted f...
متن کاملPattern Classification using Simplified Neural Networks
In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. This paper presents an approach for classifying patterns from simplified NNs. Although the predictive accuracy of ANNs is often higher than that of other methods or human experts, it is often said that ANNs are practically “black boxes”, due to the complexit...
متن کاملrodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Temporal Pattern Classification using Spiking Neural Networks
A novel supervised learning-rule is derived for Spiking Neural Networks (SNNs) using the gradient descent method, which can be applied on networks with a multi-layered architecture. All existing learning-rules for SNNs limit the spiking neurons to fire only once. Our algorithm however is specially designed to cope with neurons that fire multiple spikes, taking full advantage of the capabilities...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEE Proceedings A Science, Measurement and Technology
سال: 1993
ISSN: 0960-7641
DOI: 10.1049/ip-a-3.1993.0049