Comparison of dynamic load modeling using neural network and traditional method

2002 ◽  
Author(s):  
He Ren-Mu ◽  
A.J. Germond
Keyword(s):  
Neural Network ◽  
Dynamic Load ◽  
Traditional Method ◽  
Load Modeling

Artificial neural network based dynamic load modeling

1997 ◽  
Vol 12 (4) ◽  
pp. 1576-1583 ◽  
Author(s):  
T. Hiyama ◽  
M. Tokieda ◽  
W. Hubbi ◽  
H. Andou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Load ◽  
Load Modeling ◽  
Artificial Neural

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

Quantitative Analysis for SF6 and its Compositions in GIS

2012 ◽  
Vol 562-564 ◽  
pp. 1336-1339
Author(s):  
Hai Lun Wang ◽  
Jian Wei Shen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Traditional Method ◽  
Particle Swarm ◽  
Prediction Errors ◽  
Swarm Optimization ◽  
Training Analysis ◽  
Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

A hybrid fuzzy, neural network bus load modeling and predication

1999 ◽  
Vol 14 (2) ◽  
pp. 718-724 ◽  
Author(s):  
H.R. Kassaei ◽  
A. Keyhani ◽  
T. Woung ◽  
M. Rahman
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Load Modeling ◽  
Fuzzy Neural

scholarly journals Dynamic load modeling of some low voltage devices

2009 ◽  
Vol 22 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Lidija Korunovic ◽  
Dobrivoje Stojanovic
Keyword(s):  
Induction Motor ◽  
Dynamic Load ◽  
Low Voltage ◽  
Supply Voltage ◽  
Incandescent Lamp ◽  
Mercury Lamp ◽  
Load Modeling ◽  
Load Model ◽  
Fluorescent Lamps

This paper presents the results of dynamic load modeling for some frequently used low voltage devices. The modeling of long-term dynamics is performed on the basis of step changes of supply voltage of the heater, incandescent lamp, mercury lamp, fluorescent lamps, refrigerator, TV set and induction motor. Parameters of dynamic exponential load model of these load devices are identified, analyzed and mutually compared.

Sign in / Sign up

Export Citation Format

Share Document