SE1951080A1 - Active Power Filter - Google Patents
Active Power FilterInfo
- Publication number
- SE1951080A1 SE1951080A1 SE1951080A SE1951080A SE1951080A1 SE 1951080 A1 SE1951080 A1 SE 1951080A1 SE 1951080 A SE1951080 A SE 1951080A SE 1951080 A SE1951080 A SE 1951080A SE 1951080 A1 SE1951080 A1 SE 1951080A1
- Authority
- SE
- Sweden
- Prior art keywords
- current
- processing
- active power
- filter device
- power filter
- Prior art date
Links
- 238000000034 method Methods 0.000 claims abstract description 62
- 238000010801 machine learning Methods 0.000 claims abstract description 47
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 230000015654 memory Effects 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims abstract description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 238000010891 electric arc Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000003066 decision tree Methods 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 238000012706 support-vector machine Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 2
- 239000000306 component Substances 0.000 description 56
- 230000004044 response Effects 0.000 description 8
- 230000008901 benefit Effects 0.000 description 7
- 238000011217 control strategy Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000013459 approach Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000035484 reaction time Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
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- 238000013527 convolutional neural network Methods 0.000 description 2
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- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/002—Flicker reduction, e.g. compensation of flicker introduced by non-linear load
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/01—Arrangements for reducing harmonics or ripples
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
- H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
- H02J3/1835—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control
- H02J3/1842—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control wherein at least one reactive element is actively controlled by a bridge converter, e.g. active filters
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0025—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control implementing a off line learning phase to determine and store useful data for on-line control
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/50—Vector control arrangements or methods not otherwise provided for in H02P21/00- H02P21/36
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B7/00—Heating by electric discharge
- H05B7/18—Heating by arc discharge
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/20—Active power filtering [APF]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Nonlinear Science (AREA)
- Plasma & Fusion (AREA)
- Databases & Information Systems (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Power Conversion In General (AREA)
- Control Of Electric Motors In General (AREA)
Claims (10)
1. 1. Förfarande (300) för styrning och övervakning av en filteranordning med aktiv effekt (100), innefattande följande steg : - mätning av nuvarande och tidigare tillstånd hos ström- och spänningssignaler över åtminstone en del av ett strömförsörjningssystem och lagring av ström- och spänningssignalerna i ett minne (202); - förbehandling (302) av nämnda ström- och spänningssignaler, vilket möjliggör utvinning och detektering av karakteristikor; - varvid en maskininlärningskomponent (260) förutsäger (303) vidare kommande ström- och spänningsnivåer; - varvid de förutsagda kommande ström- och spänningsnivåerna avges till nämnda filteranordning med aktiv effekt (100), vilket möjliggör erhållande av en önskad uteffekt (107) varvid nämnda maskininlärningskomponent (260) innefattar en beräkningsstruktur som kan innehålla en inlärningsalgoritm, varvid nämnda inlärningsalgoritm är artificiella neuronnät, stödvektormaskiner, beslutsscheman, Bayes klassificerare eller varje kombination därav.
2. Förfarande (300) enligt krav 1, varvid nämnda förbehandling (302) innefattar följande steg : - utvinning och detektering av karakteristikor genom användning av en kärnbaserad modifierad synkron referensrammatrismetod - bearbetning av nämnda karakteristikor med användning av lågpassfitrering och exponentiell utjämning.
3. Förfarande (300) enligt krav 1 eller 2, varvid nämnda förbehandling (302) innefattar vidare utförande av minst någotdera av en diskret Fourier-transform, en snabb Fourier-transform, en rekursiv diskret Fourier-transform eller varje kombination därav.
4. Förfarande (300) enligt något av kraven 1 - 3, varvid nämnda utvunna och detekterade karakteristikor är minst något av individuella övertoner, mellantoner, grundfrekvens, flimmer, och/eller varje andra störningar i signalerna.
5. Förfarande (300) enligt något av kraven 1 - 4, varvid nämnda maskininlärningskomponent (260) förutsäger kommande spänning och ström genom att förutsäga amplituden och/eller faserna hos minst något av övertoner, mellantoner eller grundfrekvens vid olika frekvenser under minst en kommande cykel.
6. Förfarande (300) enligt något av kraven 1 - 5, varvid minst en del av nämnda strömförsörjningssystem matar elektriska ljusbågsugnar.
7. Förfarande (300) enligt något av kraven 1 - 6, vidare innefattande ett steg med lagring och behandling av data relaterat till styrning och övervakning av filteranordningen med aktiv effekt (100) i ett molnbaserat arkiv (102), varvid maskininlärningskomponenten (260) kan uppdateras genom nedladdning av data som lagras i nämnda molnbaserade arkiv.
8. Förfarande (300) enligt något av kraven 1 - 7, vilket vidare innefattar ett steg med utjämning (305) av eventuella effektkvalitetsvariationer på basis av de förutsagda sändnings- och strömnivåerna.
9. Filterenhet (100) med aktiv effekt, innefattande minst en behandlings- och styrenhet (201), minst ett minne (202), minst ett matningsgränssnitt (106) och minst ett utmatningsgränssnitt (107), varvid behandlings- och styrenheten (201) är anordnad för att exekvera instruktionsuppsättningar för användning av förfarandet enligt något av föregående krav.
10. Datorläsbart medium för lagring av instruktionsuppsättningar för styrning och övervakning av utmatningen (107) från en filteranordning med aktiv effekt (100), varvid instruktionsuppsättningarna är anordnade för att exekveras i en behandlingsanordning och är anordnade för att utföra förfarandet enligt något av kraven 1 - 8.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| SE1951080A SE544845C2 (en) | 2019-09-25 | 2019-09-25 | Machine learning active power filter control method and device |
| PCT/SE2020/050891 WO2021061040A1 (en) | 2019-09-25 | 2020-09-23 | Active power filter controlled by machine learning and method thereof |
| EP20866990.3A EP4035246A4 (en) | 2019-09-25 | 2020-09-23 | ACTIVE POWER FILTER CONTROLLED BY MACHINE LEARNING AND METHOD THEREOF |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| SE1951080A SE544845C2 (en) | 2019-09-25 | 2019-09-25 | Machine learning active power filter control method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| SE1951080A1 true SE1951080A1 (sv) | 2021-03-26 |
| SE544845C2 SE544845C2 (en) | 2022-12-13 |
Family
ID=75167057
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| SE1951080A SE544845C2 (en) | 2019-09-25 | 2019-09-25 | Machine learning active power filter control method and device |
Country Status (3)
| Country | Link |
|---|---|
| EP (1) | EP4035246A4 (sv) |
| SE (1) | SE544845C2 (sv) |
| WO (1) | WO2021061040A1 (sv) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113690888B (zh) * | 2021-07-23 | 2023-08-01 | 辽宁荣信电力电子技术有限公司 | 一种fpga优化处理apf控制带宽和高次谐波的方法 |
| US11923899B2 (en) * | 2021-12-01 | 2024-03-05 | Hewlett Packard Enterprise Development Lp | Proactive wavelength synchronization |
| DE102022211107A1 (de) * | 2022-10-20 | 2024-04-25 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein | Überwachung dreiphasiger periodischer elektrischer Signale unter Verwendung eines künstlichen neuronalen Netzes |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009020684A1 (en) * | 2007-05-16 | 2009-02-12 | Edsa Micro Corporation | Real-time predictive systems for intelligent energy monitoring and management of electrical power networks |
| WO2009042581A1 (en) * | 2007-09-24 | 2009-04-02 | Edsa Micro Corporation | Real-time stability indexing for intelligent energy monitoring and management of electrical power network system |
| US8131401B2 (en) * | 2006-07-19 | 2012-03-06 | Power Analytics Corporation | Real-time stability indexing for intelligent energy monitoring and management of electrical power network system |
| US20130253718A1 (en) * | 2012-03-23 | 2013-09-26 | Power Analytics Corporation | Systems and methods for integrated, model, and role-based management of a microgrid based on real-time power management |
| CN104201680A (zh) * | 2014-09-17 | 2014-12-10 | 国家电网公司 | 一种综合电能质量调节器及控制方法 |
| CN106532702A (zh) * | 2016-11-25 | 2017-03-22 | 电子科技大学 | 一种有源电力滤波器改进宽频自适应重复控制方法 |
| WO2019216975A1 (en) * | 2018-05-07 | 2019-11-14 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100461580C (zh) | 2007-06-29 | 2009-02-11 | 清华大学 | 一种用于补偿控制延时的谐波电流预测方法 |
| CN106374490B (zh) * | 2016-09-21 | 2018-11-23 | 河海大学常州校区 | 基于动态面模糊滑模控制的有源电力滤波器控制方法 |
-
2019
- 2019-09-25 SE SE1951080A patent/SE544845C2/en unknown
-
2020
- 2020-09-23 EP EP20866990.3A patent/EP4035246A4/en active Pending
- 2020-09-23 WO PCT/SE2020/050891 patent/WO2021061040A1/en unknown
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8131401B2 (en) * | 2006-07-19 | 2012-03-06 | Power Analytics Corporation | Real-time stability indexing for intelligent energy monitoring and management of electrical power network system |
| WO2009020684A1 (en) * | 2007-05-16 | 2009-02-12 | Edsa Micro Corporation | Real-time predictive systems for intelligent energy monitoring and management of electrical power networks |
| WO2009042581A1 (en) * | 2007-09-24 | 2009-04-02 | Edsa Micro Corporation | Real-time stability indexing for intelligent energy monitoring and management of electrical power network system |
| US20130253718A1 (en) * | 2012-03-23 | 2013-09-26 | Power Analytics Corporation | Systems and methods for integrated, model, and role-based management of a microgrid based on real-time power management |
| CN104201680A (zh) * | 2014-09-17 | 2014-12-10 | 国家电网公司 | 一种综合电能质量调节器及控制方法 |
| CN106532702A (zh) * | 2016-11-25 | 2017-03-22 | 电子科技大学 | 一种有源电力滤波器改进宽频自适应重复控制方法 |
| WO2019216975A1 (en) * | 2018-05-07 | 2019-11-14 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things |
Non-Patent Citations (1)
| Title |
|---|
| RAKHONDE, B et al, "Harmonic Mitigation using Modified Synchronous Reference Frame Theory", In: International Research Journal of Engineering and Technology (IRJET), Aug 2017, Vol. 4, Issue 08, e-ISSN 2395-0056, pages 2340-2345 * |
Also Published As
| Publication number | Publication date |
|---|---|
| EP4035246A4 (en) | 2023-10-25 |
| WO2021061040A1 (en) | 2021-04-01 |
| EP4035246A1 (en) | 2022-08-03 |
| SE544845C2 (en) | 2022-12-13 |
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