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Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system
- Source :
- Neural Networks. 93:21-35
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results.
- Subjects :
- Neurons
Brownout
Computers
Computer science
020209 energy
Cognitive Neuroscience
Distributed computing
Intelligence
Intelligent decision support system
02 engineering and technology
Perceptron
Grid
Cascading failure
Machine Learning
Electric power system
SCADA
Computer Systems
Artificial Intelligence
Multilayer perceptron
0202 electrical engineering, electronic engineering, information engineering
Neural Networks, Computer
Algorithms
Simulation
Subjects
Details
- ISSN :
- 08936080
- Volume :
- 93
- Database :
- OpenAIRE
- Journal :
- Neural Networks
- Accession number :
- edsair.doi.dedup.....1339dace3d8e61258c71743280426a14