A neural network-based power system stabilizer using power flow characteristics
- Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering
- Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering
A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 276831
- Report Number(s):
- CONF-960111-; ISSN 0885-8969; TRN: IM9636%%506
- Journal Information:
- IEEE Transactions on Energy Conversion, Vol. 11, Issue 2; Conference: IEEE Power Engineering Society (PES) Winter meeting, Baltimore, MD (United States), 21-25 Jan 1996; Other Information: PBD: Jun 1996
- Country of Publication:
- United States
- Language:
- English
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