| Title | Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ramirez-Gonzalez, M., Segundo Sevilla, F. R., Korba, P. |
| Conference Name | 2021 World Automation Congress (WAC) |
| Keywords | convolutional neural network, data-driven model, Deep Learning, Metrics, power system stability, Predictive models, predictive security metrics, Procurement, pubcrawl, Reactive power, static security assessment, Support vector machines, Tools, Voltage |
| Abstract | Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm. |
| DOI | 10.23919/WAC50355.2021.9559458 |
| Citation Key | ramirez-gonzalez_convolutional_2021 |
Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems