{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T23:41:25Z","timestamp":1650930085785},"publisher-location":"New York, NY, USA","reference-count":9,"publisher":"ACM","funder":[{"name":"State Grid Corporation of China Project ?Research on high penetrated renewable energy oriented intelligent identification for curtailment impacts and aid decision-making for promoting consumption in regional power grids?","award":["5108-202135035A-0-0-00"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,3]]},"DOI":"10.1145\/3508297.3508318","type":"proceedings-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T22:12:15Z","timestamp":1650579135000},"source":"Crossref","is-referenced-by-count":0,"title":["A Deep Reinforcement Learning-Based Real-Time Control for Transfer Limits of Critical Inter-Corridors"],"prefix":"10.1145","author":[{"given":"Fei","family":"Xue","sequence":"first","affiliation":[{"name":"Electric power research institute, State Grid Ningxia Electric Power Company, China"}]},{"given":"Hongqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Electric power research institute, State Grid Ningxia Electric Power Company, China"}]},{"given":"Jili","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest Branch, State Grid Corporation of China, China"}]},{"given":"Gao","family":"Qiu","sequence":"additional","affiliation":[{"name":"The College of Electric Engineering, Sichuan University, China"}]},{"given":"Junyong","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Electric Engineering, Sichuan University, China"}]},{"given":"Youbo","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Electric Engineering, Sichuan University, China"}]},{"given":"Tingjian","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Electric Engineering, Sichuan University, China"}]},{"given":"Tianxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"The College of Electric Engineering, Sichuan University, China"}]}],"member":"320","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"An Automated Transient stability constrained optimal power flow based on trajectory sensitivity analysis","author":"Tang W.","unstructured":"L., Tang , W. , Sun . 2017. An Automated Transient stability constrained optimal power flow based on trajectory sensitivity analysis . IEEE transactions on power systems, vol. 32 , no. 1, 590-599. L., Tang, W., Sun. 2017. An Automated Transient stability constrained optimal power flow based on trajectory sensitivity analysis. IEEE transactions on power systems, vol. 32, no. 1, 590-599."},{"key":"e_1_3_2_1_2_1","unstructured":"J. Sun D. Fang. 2005. Total transfer capability with transient stability constraints. Automation of electric power systems no. 8 21-25 (in Chinese). J. Sun D. Fang. 2005. Total transfer capability with transient stability constraints. Automation of electric power systems no. 8 21-25 (in Chinese)."},{"key":"e_1_3_2_1_3_1","volume-title":"Online TTC estimation using nonparametric analytics considering wind power integration","author":"Liu J.","unstructured":"Y., Liu , J. , Zhao , L. , Xu , 2019. Online TTC estimation using nonparametric analytics considering wind power integration . IEEE transactions on power systems, vol. 34 , no. 1, 494-505. Y., Liu, J., Zhao, L., Xu, 2019. Online TTC estimation using nonparametric analytics considering wind power integration. IEEE transactions on power systems, vol. 34, no. 1, 494-505."},{"key":"e_1_3_2_1_4_1","volume-title":"A unified online deep learning prediction model for small signal and transient stability","author":"Azman Y. J.","unstructured":"S. K., Azman , Y. J. , Isbeih , M. S. E. , Moursi and K. , Elbassioni . 202 0. A unified online deep learning prediction model for small signal and transient stability . IEEE transactions on power systems, vol. 35 , no. 6, 4585-4598. S. K., Azman, Y. J., Isbeih, M. S. E., Moursi and K., Elbassioni. 2020. A unified online deep learning prediction model for small signal and transient stability. IEEE transactions on power systems, vol. 35, no. 6, 4585-4598."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2021.3081159"},{"key":"e_1_3_2_1_6_1","volume-title":"Analytic deep learning-based surrogate model for operational planning with dynamic TTC constraints","author":"Qiu","unstructured":"G., Qiu 2021. Analytic deep learning-based surrogate model for operational planning with dynamic TTC constraints . IEEE transactions on power systems, vol. 36 , no. 4, 3507-3519. G., Qiu 2021. Analytic deep learning-based surrogate model for operational planning with dynamic TTC constraints. IEEE transactions on power systems, vol. 36, no. 4, 3507-3519."},{"key":"e_1_3_2_1_7_1","unstructured":"Haarnoja T. Zhou A. Hartikainen K. Tucker 2018. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905. Haarnoja T. Zhou A. Hartikainen K. Tucker 2018. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905."},{"key":"e_1_3_2_1_8_1","volume-title":"International conference on machine learning","author":"Mnih V.","year":"2016","unstructured":"Mnih , V. , Badia , A. P. , Mirza , M. , Graves , 2016 . Asynchronous methods for deep reinforcement learning . In International conference on machine learning , 1928-1937. Mnih, V., Badia, A. P., Mirza, M., Graves, 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, 1928-1937."},{"key":"e_1_3_2_1_9_1","volume-title":"et\u00a0al.\u00a02021. Surrogate-assisted optimal re-dispatch control for risk-aware regulation of dynamic total transfer capability.\u00a0in IET Gener. Transm. Distrib","author":"Qiu G","year":"1949","unstructured":"Qiu , G ,\u00a0 Liu , Y ,\u00a0 Liu , J , et\u00a0al.\u00a02021. Surrogate-assisted optimal re-dispatch control for risk-aware regulation of dynamic total transfer capability.\u00a0in IET Gener. Transm. Distrib ., 1949 \u2013\u00a01961. Qiu, G,\u00a0Liu, Y,\u00a0Liu, J, et\u00a0al.\u00a02021. Surrogate-assisted optimal re-dispatch control for risk-aware regulation of dynamic total transfer capability.\u00a0in IET Gener. Transm. Distrib., 1949\u2013\u00a01961."}],"event":{"name":"EEET 2021: 2021 4th International Conference on Electronics and Electrical Engineering Technology","location":"Nanjing China","acronym":"EEET 2021"},"container-title":["2021 4th International Conference on Electronics and Electrical Engineering Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508297.3508318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T23:25:18Z","timestamp":1650929118000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508297.3508318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,3]]},"references-count":9,"alternative-id":["10.1145\/3508297.3508318","10.1145\/3508297"],"URL":"http:\/\/dx.doi.org\/10.1145\/3508297.3508318","relation":{},"published":{"date-parts":[[2021,12,3]]}}}