{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LB3MA3V4D57H7RMXQYM3NRRCMR","short_pith_number":"pith:LB3MA3V4","schema_version":"1.0","canonical_sha256":"5876c06ebc1f7e7fc5978619b6c622644aa63f52c25ade8309017f1fae27753c","source":{"kind":"arxiv","id":"1805.06657","version":1},"attestation_state":"computed","paper":{"title":"Deep-learning Based Modeling of Fault Detachment Stability for Power Grid","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haotian Cui, Xianggen Liu, Yanhao Huang","submitted_at":"2018-05-17T08:54:00Z","abstract_excerpt":"The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called \"fail-delay cut-off\" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault. In practice, through the analysis and calculation of a large number of online data, we have found that the N-1 failure system of the main protection action will not be unstable, which is also a guarantee of the operation mode arrangement. In the case of the N-1 back"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1805.06657","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-17T08:54:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fe3f2f3e03929e49bee4dbff303b762e66e8953b18c0768c46ece5bb08149f85","abstract_canon_sha256":"70ca8d37b112c15a2a83560783a58afc62276004d303215d2945846493d51fea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:43.751924Z","signature_b64":"71NytXPqzv3lF14FnHIQBTCFv+7aC0DpdQmSQ9tnhO6g8LHC6ofC1mx+P/SLS5Nj3QTxvSuOongaD9sW5iwKCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5876c06ebc1f7e7fc5978619b6c622644aa63f52c25ade8309017f1fae27753c","last_reissued_at":"2026-05-18T00:15:43.751444Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:43.751444Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-learning Based Modeling of Fault Detachment Stability for Power Grid","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haotian Cui, Xianggen Liu, Yanhao Huang","submitted_at":"2018-05-17T08:54:00Z","abstract_excerpt":"The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called \"fail-delay cut-off\" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault. In practice, through the analysis and calculation of a large number of online data, we have found that the N-1 failure system of the main protection action will not be unstable, which is also a guarantee of the operation mode arrangement. In the case of the N-1 back"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06657","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.06657","created_at":"2026-05-18T00:15:43.751526+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.06657v1","created_at":"2026-05-18T00:15:43.751526+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06657","created_at":"2026-05-18T00:15:43.751526+00:00"},{"alias_kind":"pith_short_12","alias_value":"LB3MA3V4D57H","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"LB3MA3V4D57H7RMX","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"LB3MA3V4","created_at":"2026-05-18T12:32:33.847187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR","json":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR.json","graph_json":"https://pith.science/api/pith-number/LB3MA3V4D57H7RMXQYM3NRRCMR/graph.json","events_json":"https://pith.science/api/pith-number/LB3MA3V4D57H7RMXQYM3NRRCMR/events.json","paper":"https://pith.science/paper/LB3MA3V4"},"agent_actions":{"view_html":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR","download_json":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR.json","view_paper":"https://pith.science/paper/LB3MA3V4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.06657&json=true","fetch_graph":"https://pith.science/api/pith-number/LB3MA3V4D57H7RMXQYM3NRRCMR/graph.json","fetch_events":"https://pith.science/api/pith-number/LB3MA3V4D57H7RMXQYM3NRRCMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR/action/storage_attestation","attest_author":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR/action/author_attestation","sign_citation":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR/action/citation_signature","submit_replication":"https://pith.science/pith/LB3MA3V4D57H7RMXQYM3NRRCMR/action/replication_record"}},"created_at":"2026-05-18T00:15:43.751526+00:00","updated_at":"2026-05-18T00:15:43.751526+00:00"}