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Data-Driven Security Assessment of the Electric Power System

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Document pages: 6 pages

Abstract: The transition to a new low emission energy future results in a changing mixof generation and load types due to significant growth in renewable energypenetration and reduction in system inertia due to the exit of ageing fossilfuel power plants. This increases technical challenges for electrical gridplanning and operation. This study introduces a new decomposition approach toaccount for the system security for short term planning using conventionalmachine learning tools. The immediate value of this work is that it providesextendable and computationally efficient guidelines for using supervisedlearning tools to assess first swing transient stability status. To provide anunbiased evaluation of the final model fit on the training dataset, theproposed approach was examined on a previously unseen test set. Itdistinguished stable and unstable cases in the test set accurately, with only0.57 error, and showed a high precision in predicting the time of instability,with 6.8 error and mean absolute error as small as 0.0145.

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