As the scale of power system increases

traditional numerical methods in eigenvalue computation for small-signal stability assessment are unable to meet the requirement of real-time analysis. Therefore

this paper proposes a small-signal stability assessment method based on deep-learning(convolutional neural network). This method takes the signals of wide-area measurement system as inputs of the model and generates critical eigenvalues as outputs. After the necessary preprocessing of the inputs and outputs

the mapping relationship between inputs and outputs can be established by deep neural network. Discrete cosine transformation and graphics processing unit parallelization techniques are employed to overcome the challenges from high dimension and slow training rate of large-scale system. Case study results indicate that the proposed method is able to accurately obtain the critical eigenvalues of the studied system after offline training using historic data

given no significant change in control parameters.