http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111860969-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53b4d53983adbf56dd0e7d0bf96f8ce5 http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_126e3f407dc4815570ece7e153058846 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y04S10-50 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-06 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-067 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-06 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-06 |
| filingDate | 2020-06-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_dd89026ebd88fa2c3a7295e016269e18 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3b6f56bbafea0271529254d56ad50871 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6fb8e4caa453076f8df2dffadd46a80a http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3b84c8aaa686df02c17c4ab8ea917ff3 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_237e2c4491f35f146feeb25de18a9132 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ca45bfda9d045dc561c9c6a6e03824f2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f1daeffcfd98072039c65662477e1149 |
| publicationDate | 2020-10-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111860969-A |
| titleOfInvention | Power transmission network extension planning method based on reinforcement learning algorithm |
| abstract | The invention discloses a power transmission network extension planning method based on a reinforcement learning algorithm, which belongs to the technical field of power grid planning, and comprises the steps of firstly, constructing a QTEP algorithm environment, a state, an action and an intelligent agent suitable for power grid planning based on the acquisition of a power grid planning candidate line set, and designing a self-adaptive factor; then, comprehensively considering investment cost and reliability cost, taking comprehensive economic optimization in a planning period as a target, and establishing an optimization model by taking power grid operation constraint, N-1 power grid safety constraint and Monte Carlo error constraint as constraint conditions; and finally, according to the optimization model, combining the comprehensive characteristic feedback reward function and the algorithm convergence condition to solve the optimization model by a reinforced learning thought to obtain the optimal comprehensive economical power transmission network expansion planning scheme. According to the method, the reinforcement learning idea is applied to the power transmission network extension planning for the first time, the solution idea is fitted with planning personnel, the obtained planning scheme is reasonable and accurate, and the efficiency and the accuracy of the power transmission network extension planning can be effectively improved. |
| priorityDate | 2020-06-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID415777152 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID83436 |
Total number of triples: 25.