NDLI: A Modular Neural Block to Enhance Power System Stability

Content Provider	IEEE Xplore Digital Library
Author	Alizadeh, M. Kojori, S.S. Ganjefar, S.
Copyright Year	1969
Abstract	Conventional supplementary controllers (CSCs) can still be widely observed in power system utilities. This work aims to develop a modular neural block (MNB) to improve control performance and stability of CSCs-aided power systems. The proposed MNB is actually a one-to-one offline trained self-recurrent wavelet neural network (SRWNN) which can be modularity added to the PI/PD/PID/Lag-Lead controllers to enhance their performance by adding an adaptive property to them. Independent of the plant model, the MNB is initially trained offline using virtual training-samples. As a prefabricated one-to-one neural block, it can then be copied to required numbers and added to the lag-lead controller or any/all branches of the PI/PD/PID controller in series connection. The employed MNB(s) is then re-trained online to increase control performance by minimizing a predefined cost-function. The online training is performed by back-propagation (BP) algorithm while the closed-loop stability is guaranteed by an efficient Lyapunov-based approach. The proposed approach is thus a model-free scheme which is simple enough for implementation. Ability of the MNB to enhance the performance of the CSCs and dynamic stability of power systems is demonstrated by the simulation results of two small power plants and an IEEE 10-machine 39-bus system.
Sponsorship	IEEE Power Engineering Society
Starting Page	4849
Ending Page	4856
Page Count	8
File Size	1741002
File Format	PDF
ISSN	08858950
Volume Number	28
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system stability Training Stability analysis Convergence Lyapunov methods Oscillators Damping wavelet neural network Adaptive control conventional supplementary controllers Lyapunov stability theorem power system stability and control
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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