Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power System Article Swipe
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· 2018
· Open Access
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· DOI: https://doi.org/10.11591/ijra.v7i3.pp176-184
For the three phase power electronic and drive applications, vector control or the synchronous reference frame (SRF) based control concept is well accepted and settled amongst the research communities. Although the SRF concept has gained popularity and appreciation in developing the three phase controllers, still the concept has not reached the same level in case of a single phase system. The work presented in this paper is mainly concerned to the design of a hybrid Artificial Neural Network and Fuzzy Logic based controller for a single phase stand-alone photo-voltaic (PV) power system. The adaptive neuro fuzzy inference system (ANFIS) controller proposed in this paper is chiefly meant for improving the transient and steady state responses; for minimizing the distorting effect of the low order load current harmonics encountered particularly in case of switching the drive based inductive loads and to help maintain the inverter output voltage constant under different loading circumstances. The result obtained through simulation work, shows the effectiveness of the proposed controller as compared with the previously established research works.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijra.v7i3.pp176-184
- http://ijra.iaescore.com/index.php/IJRA/article/download/15995/10443
- OA Status
- diamond
- Cited By
- 2
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2896359098
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2896359098Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijra.v7i3.pp176-184Digital Object Identifier
- Title
-
Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power SystemWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-09-01Full publication date if available
- Authors
-
H. S. Thakur, R. N. PatelList of authors in order
- Landing page
-
https://doi.org/10.11591/ijra.v7i3.pp176-184Publisher landing page
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https://ijra.iaescore.com/index.php/IJRA/article/download/15995/10443Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ijra.iaescore.com/index.php/IJRA/article/download/15995/10443Direct OA link when available
- Concepts
-
Control theory (sociology), Adaptive neuro fuzzy inference system, Controller (irrigation), Computer science, Transient (computer programming), Fuzzy logic, Harmonics, Control engineering, Power (physics), Artificial neural network, Inverter, Electric power system, Fuzzy control system, Engineering, Voltage, Control (management), Artificial intelligence, Electrical engineering, Quantum mechanics, Agronomy, Physics, Biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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-
2023: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(SRF) | 16 |
| abstract_inverted_index.Fuzzy | 79 |
| abstract_inverted_index.Logic | 80 |
| abstract_inverted_index.based | 17, 81, 135 |
| abstract_inverted_index.drive | 7, 134 |
| abstract_inverted_index.frame | 15 |
| abstract_inverted_index.fuzzy | 95 |
| abstract_inverted_index.level | 52 |
| abstract_inverted_index.loads | 137 |
| abstract_inverted_index.meant | 106 |
| abstract_inverted_index.neuro | 94 |
| abstract_inverted_index.order | 123 |
| abstract_inverted_index.paper | 65, 103 |
| abstract_inverted_index.phase | 3, 42, 58, 86 |
| abstract_inverted_index.power | 4, 90 |
| abstract_inverted_index.shows | 157 |
| abstract_inverted_index.state | 113 |
| abstract_inverted_index.still | 44 |
| abstract_inverted_index.three | 2, 41 |
| abstract_inverted_index.under | 147 |
| abstract_inverted_index.work, | 156 |
| abstract_inverted_index.Neural | 76 |
| abstract_inverted_index.design | 71 |
| abstract_inverted_index.effect | 119 |
| abstract_inverted_index.gained | 34 |
| abstract_inverted_index.hybrid | 74 |
| abstract_inverted_index.mainly | 67 |
| abstract_inverted_index.output | 144 |
| abstract_inverted_index.result | 152 |
| abstract_inverted_index.single | 57, 85 |
| abstract_inverted_index.steady | 112 |
| abstract_inverted_index.system | 97 |
| abstract_inverted_index.vector | 9 |
| abstract_inverted_index.works. | 171 |
| abstract_inverted_index.(ANFIS) | 98 |
| abstract_inverted_index.Network | 77 |
| abstract_inverted_index.amongst | 25 |
| abstract_inverted_index.chiefly | 105 |
| abstract_inverted_index.concept | 19, 32, 46 |
| abstract_inverted_index.control | 10, 18 |
| abstract_inverted_index.current | 125 |
| abstract_inverted_index.loading | 149 |
| abstract_inverted_index.reached | 49 |
| abstract_inverted_index.settled | 24 |
| abstract_inverted_index.system. | 59, 91 |
| abstract_inverted_index.through | 154 |
| abstract_inverted_index.voltage | 145 |
| abstract_inverted_index.Although | 29 |
| abstract_inverted_index.accepted | 22 |
| abstract_inverted_index.adaptive | 93 |
| abstract_inverted_index.compared | 165 |
| abstract_inverted_index.constant | 146 |
| abstract_inverted_index.inverter | 143 |
| abstract_inverted_index.maintain | 141 |
| abstract_inverted_index.obtained | 153 |
| abstract_inverted_index.proposed | 100, 162 |
| abstract_inverted_index.research | 27, 170 |
| abstract_inverted_index.concerned | 68 |
| abstract_inverted_index.different | 148 |
| abstract_inverted_index.harmonics | 126 |
| abstract_inverted_index.improving | 108 |
| abstract_inverted_index.inductive | 136 |
| abstract_inverted_index.inference | 96 |
| abstract_inverted_index.presented | 62 |
| abstract_inverted_index.reference | 14 |
| abstract_inverted_index.switching | 132 |
| abstract_inverted_index.transient | 110 |
| abstract_inverted_index.Artificial | 75 |
| abstract_inverted_index.controller | 82, 99, 163 |
| abstract_inverted_index.developing | 39 |
| abstract_inverted_index.distorting | 118 |
| abstract_inverted_index.electronic | 5 |
| abstract_inverted_index.minimizing | 116 |
| abstract_inverted_index.popularity | 35 |
| abstract_inverted_index.previously | 168 |
| abstract_inverted_index.responses; | 114 |
| abstract_inverted_index.simulation | 155 |
| abstract_inverted_index.encountered | 127 |
| abstract_inverted_index.established | 169 |
| abstract_inverted_index.stand-alone | 87 |
| abstract_inverted_index.synchronous | 13 |
| abstract_inverted_index.appreciation | 37 |
| abstract_inverted_index.communities. | 28 |
| abstract_inverted_index.controllers, | 43 |
| abstract_inverted_index.particularly | 128 |
| abstract_inverted_index.applications, | 8 |
| abstract_inverted_index.effectiveness | 159 |
| abstract_inverted_index.photo-voltaic | 88 |
| abstract_inverted_index.circumstances. | 150 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.49456171 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |