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Inertia from rotating masses of generators in power systems influence the instantaneous frequency change when an imbalance between electrical and mechanical power occurs. Renewable energy sources (RES), such as solar and wind power, are connected to the grid via electronic converters. RES connected through converters affect the system's inertia by decreasing it and making it time-varying. This new setting challenges the ability of current control schemes to maintain frequency stability. Proposing adequate controllers for this new paradigm is key for the performance and stability of future power grids. The contribution of this paper is a framework to learn sparse time-invariant frequency controllers in a power system network with a time-varying evolution of rotational inertia. We model power dynamics using a Switched-Affine hybrid system to consider different modes corresponding to different inertia coefficients. We design a controller that uses as features, i.e. input, the systems states. In other words, we design a control proportional to the angles and frequencies. We include virtual inertia in the controllers to ensure stability. One of our findings is that it is possible to restrict communication between the nodes by reducing the number of features in the controller (from 22 to 10 in our case study) without disrupting performance and stability. Furthermore, once communication between nodes has reached a threshold, increasing it beyond this threshold does not improve performance or stability. We find a correlation between optimal feature selection in sparse controllers and the topology of the network.
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Robust Neural Network-Based Spectrum Occupancy Mapping
We present a neural network decision system for determining if spectrum is occupied in a region. Given a threshold, we wish to determine if power at a given frequency exceeds the threshold, thus determining if that frequency is “occupied”. The emitting sources are unknown in number, locations, and powers. The sensors, which measure the signal power, are random in number and location. The measurements are aggregated as log-likelihood ratios into a fixed-resolution image suitable as input to a neural network. The network is trained to produce an occupancy map over a wide area, even where there are no sensors, and achieves excellent accuracy at determining occupancy. The system is robust to the number of sensors, and occupancy threshold in a variety of environments.
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- Award ID(s):
- 2002921
- PAR ID:
- 10338904
- Date Published:
- Journal Name:
- 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
- Page Range / eLocation ID:
- 1-6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DySPAN53946.2021.9677439
