NDLI: Real time implementation of artificial neural networks-based controller for battery storage supported wind electric generation

Content Provider	IET Digital Library
Author	Kurian, Sony Krishnan, Sindhu T. Cheriyan, Elizabeth P.
Abstract	Energy storage systems have established their capability to overcome the problems caused by intermittent nature of renewable sources when integrated to existing grid. Voltage and frequency control, as well as load shifting can be done using grid level storage systems incorporated with renewable sources. They can effectively serve the system as an energy sink and source. Operational use of energy storage for grid level support of a wind electric generator (WEG) is demonstrated in this study. The battery storage supported WEG along with controllers is modelled. Artificial neural network controller, which is having inherent learning capability, is developed to regulate the power flow between wind generator and utility grid. The proposed algorithm and the corresponding controller are simulated in MATLAB/Simulink and implemented in the DSP processor. The real time data exchange between Simulink and the floating point DSP processor TMSF32028335 is realised using on-board JTAG emulator. The hardware implementation using DSP processor presented in this work establishes the efficacy of the proposed control strategy for real time applications.
Starting Page	937
Ending Page	946
Page Count	10
ISSN	17518687
Volume Number	9
e-ISSN	17518695
Issue Number	Issue 10, Jul (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/9/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2014.0544
Journal	IET Generation, Transmission & Distribution
Publisher Date	2015-03-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A.C. Machine Battery Storage Plants Battery Storage Supported Wind Electric Generation Control Engineering Computing Control of Electric Power System Digital Signal Processing Chips Energy ReSource Energy Sink Energy Storage System Frequency Control Grid Level Storage System Learning Capability Load Flow Control Load Shifting MATLAB Neurocontrol Neurocontrollers On-board JTAG Emulator Other Power Stations And Plant Power Flow Regulation Power Generation Control Power Grid Power System Analysis Computing Real Time Artificial Neural Network-based Controller Implementation Renewable Source Simulink TMSF32028335 Floating Point DSP Processor TurboGenerator Voltage Control WEG Wind Power Wind Power Plant
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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