Neural Network-based Algorithm for Harmonic Analysis of Industrial Systems Using Asynchronous Simulations in Time and Frequency Domains

2010 ◽  
Vol 5 (6) ◽  
pp. 688-694 ◽  
Author(s):  
Rahmat A. Hooshmand ◽  
Saeid Noohi
Keyword(s):  
Neural Network ◽  
Harmonic Analysis ◽  
Industrial Systems ◽  
Frequency Domains ◽  
Time And Frequency Domains

A Design of Comfortable Dental Treatment Sound Based on Chord-forming in Time and Frequency Domains

2015 ◽  
Vol 135 (12) ◽  
pp. 1565-1573
Author(s):  
Yoshitaka Ohshio ◽  
Daisuke Ikefuji ◽  
Yuko Suhara ◽  
Masato Nakayama ◽  
Takanobu Nishiura
Keyword(s):  
Dental Treatment ◽  
Frequency Domains ◽  
Time And Frequency Domains

Fuzzy Extreme Analysis in Time- and Frequency Domains

2011 ◽  
Vol 57 (4) ◽  
Author(s):  
Włodzimierz Pogribny ◽  
Marcin Drzycimski ◽  
Zdzisław Drzycimski
Keyword(s):  
Frequency Domains ◽  
Time And Frequency Domains

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.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

scholarly journals Characterizing patterns and variability of building electric load profiles in time and frequency domains

2021 ◽  
Vol 291 ◽  
pp. 116721
Author(s):  
Han Li ◽  
Zhe Wang ◽  
Tianzhen Hong ◽  
Andrew Parker ◽  
Monica Neukomm
Keyword(s):  
Electric Load ◽  
Frequency Domains ◽  
Time And Frequency Domains

Differences between apparent polarization resistance values obtained in the time and frequency domains

1995 ◽  
Vol 381 (1-2) ◽  
pp. 1-4 ◽  
Author(s):  
S. Feliu ◽  
J.C. Galvan ◽  
S. Feliu ◽  
J. Simancas ◽  
J.M. Bastidas ◽  
...  
Keyword(s):  
Polarization Resistance ◽  
Frequency Domains ◽  
Time And Frequency Domains

Human pain induces specific cortical responses in time and frequency domains: An EEG study

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. S714
Author(s):  
Claudio Babiloni ◽  
Filippo Carducci ◽  
Fabio Babiloni ◽  
Febo Cincotti ◽  
Fabrizio Rosciarelli ◽  
...  
Keyword(s):  
Cortical Responses ◽  
Frequency Domains ◽  
Time And Frequency Domains
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