http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-106682303-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2218-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-06 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R31-42 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-231 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24323 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-50 |
| filingDate | 2016-12-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2019-09-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2019-09-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-106682303-B |
| titleOfInvention | A Three-level Inverter Fault Diagnosis Method Based on Empirical Mode Decomposition and Decision Tree RVM |
| abstract | The invention discloses a three-level inverter fault diagnosis method based on empirical mode decomposition and decision tree RVM, aiming at the problem of fault diagnosis of diode mid-point clamped three-level inverter in a photovoltaic power generation system, firstly analyzing The operating conditions of the inverter circuit and fault classification are carried out, and then the middle, upper and lower bridge arm voltages are used as measurement signals, and the empirical mode decomposition method is used to extract each signal component, and then the corresponding parameters such as energy and energy entropy are calculated, and then Using the particle swarm clustering algorithm to generate a decision tree RVM classification model, the multi-mode fault diagnosis of the photovoltaic diode mid-point clamped three-level inverter is finally realized. Its advantages are: no need to set parameters, fewer classification models, high computing efficiency, high diagnostic accuracy, and strong robustness. |
| priorityDate | 2016-12-26-04:00^^<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/compound/CID25572 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID415713197 |
Total number of triples: 20.