Synchronous machine steady-state stability analysis using an artificial neural network

Journal Article · Fri Mar 01 00:00:00 EST 1991 · IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA)

DOI:https://doi.org/10.1109/60.73784· OSTI ID:5793812

Chen, C R; Hsu, Y Y ^[1]

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1. National Taiwan Univ., Taipei (Taiwan). Dept. of Electrical Engineering

A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.

• OSTI ID: 5793812
• Journal Information: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA), Vol. 6:1; ISSN 0885-8969
• Country of Publication: United States
• Language: English

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