• Nenhum resultado encontrado

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

N/A
N/A
Protected

Academic year: 2017

Share "Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques."

Copied!
16
0
0

Texto

Loading

Imagem

Fig 1. Flowchart of ANN algorithm.
Table 1. Some actual data of incipient transformer fault from an electrical utility.
Table 1. (Continued) H 2 CH 4 C 2 H 2 C 2 H 4 C 2 H 6 CO Fault Type 953 737 464 39 465 657 No fault 523 438 217 697 769 55 No fault 230 367 664 777 375 632 No fault 513 677 693 980 176 18 No fault 98 38 1 3 0 7 No fault 11 12 22 78 31 32 No fault 140 1 76
Fig 2. Flowchart of PSO technique.
+6

Referências

Documentos relacionados

Once the weights and biases are found using Particle swarm optimization (PSO) with neural network used as training algorithm for specified epoch, the same are used to train the

In this paper, a scooter fault diagnosis system based on continuous wavelet transform technique and faults classification using artificial neural network for the purpose of the

Abstract — This paper presents Artificial Neural Network (ANN) implementation for the Radio Frequency (RF) and Mechanical modeling of lateral RF Micro Electro

Optimal Design of Double Folded Stub Microstrip Filter by Neural Network Modelling and Particle..

Popular optimization techniques, as genetic algorithm (GA) and particle swarm optimization (PSO), were used to design the shape of the ground plane in order to improve

In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression

Neste trabalho, apresentam-se pesquisas realizadas por integrantes do (TRANS)FORMAÇÃO, organizando-as em três categorias, a partir de um recorte que tornasse

Response surface methodology (RSM) and artificial neural network (ANN) were used to model the bending strength of gypsum-bonded fiberboard.. According to the hydration