Fault Classification in Mixed Signal Circuits using Artificial Neural Networks

fault location in power distribution networks using matching algorithm

چکیده رساله/پایان نامه : تاکنون روش‏های متعددی در ارتباط با مکان یابی خطا در شبکه انتقال ارائه شده است. استفاده مستقیم از این روش‏ها در شبکه توزیع به دلایلی همچون وجود انشعاب‏های متعدد، غیر یکنواختی فیدرها (خطوط کابلی، خطوط هوایی، سطح مقطع متفاوت انشعاب ها و تنه اصلی فیدر)، نامتعادلی (عدم جابجا شدگی خطوط، بارهای تک‏فاز و سه فاز)، ثابت نبودن بار و اندازه گیری مقادیر ولتاژ و جریان فقط در ابتدای...

Fault Classification in EHV Transmission Lines Using Artificial Neural Networks

This paper investigates a new approach based on Artificial Neural Networks (ANNs) for real-time fault classification in power transmission lines which can be used in digital power system protection. The technique uses sampled current and voltage data of each phase at one terminal as inputs to the corresponding ANN. The ANN outputs indicate the type of the fault within a time less than 5 ms. The...

Electronic Circuits Diagnosis Using Artificial Neural Networks

When we expect about something that does not treat as it should be, we are initiating the process of diagnosis. Diagnosis is a commonly used activity in our everyday lives (Benjamins & Jansweijer, 1990). Complicated systems are always prone to faults or failures. In the simplest term, a fault is every change in a system that prevents it from operating in the proper manner. We define diagnosis a...

Artificial neural networks as building blocks of mixed signal FPGA

Protein Loop Classification Using Artificial Neural Networks

We used Artificial Neural Network for protein loop classification based on amino acid sequence alone. A new algorithm recently proposed, the Hidden Layer Learning Vector Quantization (HLVQ) was used and its accuracy compared with traditional Multilayer Preceptrons (MLP). The HLVQ algorithm achieved superior accuracy correctly classifying most loops.