http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112116058-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y04S10-50 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-28 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2148 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24323 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-006 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-28 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate | 2020-09-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-05-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-05-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-112116058-B |
| titleOfInvention | A Transformer Fault Diagnosis Method Based on Particle Swarm Optimization for Multi-granularity Cascade Forest Model |
| abstract | The invention discloses a transformer fault diagnosis method based on particle swarm algorithm optimization of multi-granularity cascade forest model. First, the uncoded ratio of characteristic gas dissolved in transformer oil is used as the characteristic parameter of the model, and then the characteristic parameter is normalized. Divide the training set and the test set; then build a multi-granularity cascade forest model, and optimize the two key parameters of the multi-granularity cascade forest through the particle swarm algorithm, and obtain two optimal parameters; The granular cascade forest model is used to identify transformer fault categories. This method effectively improves the fault diagnosis accuracy of transformers and provides a reliable basis for operation and maintenance personnel to correctly judge the operating status of transformers. |
| priorityDate | 2020-09-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 33.