Abstract
A simplified faster method for contingency analysis and screening is proposed for both on-line and off-line applications in electrical power networks. The suggested method uses the transmission line power flows to develop artificial neural network models which are then used for monitoring the transmission lines in real-time and provide binary output that signifies the state of the network. The outputs of the neural net are then used to calculate an index to determine the state of the whole power network grid. An additional term for the misclassification data has also been included to compensate for the errors in the classification of states, while using the neural networks. The proposed approach was applied to a test bus system and a state-owned utility. The results testify that the proposed method will provide faster results in shorter response time. The whole process of ANN-based security assessment is completed within 8 min.
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References
Tiwary SK, Pal J, Chanda CK (2019) Application of common ANN for similar datatypes in on-line monitoring and security estimation of power system. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Advances in intelligent systems and computing, pp 3–11. https://doi.org/10.1007/978-981-13-1951-8_1
Tiwary SK, Pal J, Chanda CK (2017) Mimicking on-line monitoring and security estimation of power system using ANN on RT lab. In: 2017 IEEE Calcutta conference (CALCON). IEEE, pp 100–104 (2017). https://doi.org/10.1109/CALCON.2017.8280704
Tiwary SK, Pal J (2017) ANN application for voltage security assessment of a large test bus system: a case study on IEEE 57 bus system. In: 2017 6th international conference on computer applications. in electrical engineering-recent advances (CERA). IEEE, pp 332–334 (2017). https://doi.org/10.1109/CERA.2017.8343350
Tiwary SK, Pal J (2017) ANN application for MW security assessment of a large test bus system. In: 2017 3rd international conference on advances in computing, communication & automation (ICACCA) (Fall). IEEE, pp 1–4. https://doi.org/10.1109/ICACCAF.2017.8344661
Tiwary SK, Pal J, Chanda CK (2017) Multi-dimensional ANN application for active power flow state classification on a utility system. In: 2020 IEEE Calcutta conference (CALCON). IEEE, pp 64–68 (2020). https://doi.org/10.1109/CALCON49167.2020.9106479
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Tiwary, S.K., Pal, J., Chanda, C.K. (2020). ANN-Based Faster Indexing with Training-Error Compensation for MW Security Assessment of Power System. In: Sikander, A., Acharjee, D., Chanda, C., Mondal, P., Verma, P. (eds) Energy Systems, Drives and Automations. Lecture Notes in Electrical Engineering, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-15-5089-8_4
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DOI: https://doi.org/10.1007/978-981-15-5089-8_4
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