Implementation of a hybrid neural network control technique to a cascaded MLI based SAPF Article Swipe

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Rashmi Rekha Behera , Ashish Ranjan Dash , Satyasis Mishra , Anup Kumar Panda , Demissie Jobir Gelmecha ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1038/s41598-024-58137-0

This paper presents a naïve back propagation (NBP) based $$i\cos \emptyset$$ technique implemented to a cascaded multilevel inverter (MLI) based shunt active power filter (SAPF). The recommended control algorithm is applied to extract the fundamental component of load current and to decide the compensating current reference for harmonic elimination. The performance of the SAPF using the proposed NBP-based $$i\cos \emptyset$$ technique is compared with another two classical control techniques, such as, $$i_{d} - i_{q}$$ technique and $$i\cos \emptyset$$ technique. The accuracy of the proposed control technique depends on the tuned estimation of active and reactive weights. The performance study of the proposed SAPF with the proposed control technique is investigated under non-linear conditions, with balanced and unbalanced loading conditions. The results reveal that the recommended SAPF is efficient enough to reduce the harmonics from the source current with smooth variation in DC link voltage. The effectiveness of the proposed method is validated by simulation using MATLAB Simulink, and the real-time results are also validated by the experimental setup.

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Type: article
Language: en
Landing Page: https://doi.org/10.1038/s41598-024-58137-0
PDF: https://www.nature.com/articles/s41598-024-58137-0.pdf
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DOI: https://doi.org/10.1038/s41598-024-58137-0

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Title: Implementation of a hybrid neural network control technique to a cascaded MLI based SAPF

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Language: en

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Publication year: 2024

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Publication date: 2024-04-14

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Authors: Rashmi Rekha Behera, Ashish Ranjan Dash, Satyasis Mishra, Anup Kumar Panda, Demissie Jobir Gelmecha

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Landing page: https://doi.org/10.1038/s41598-024-58137-0

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Concepts: Artificial neural network, Computer science, Control (management), Artificial intelligence, Data mining

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abstract_inverted_index.$$i_{d}	88
abstract_inverted_index.(SAPF).	33
abstract_inverted_index.another	81
abstract_inverted_index.applied	39
abstract_inverted_index.control	36, 84, 123, 145
abstract_inverted_index.current	47, 53, 175
abstract_inverted_index.depends	125
abstract_inverted_index.extract	41
abstract_inverted_index.i_{q}$$	90
abstract_inverted_index.loading	156
abstract_inverted_index.results	159, 199
abstract_inverted_index.Abstract	0
abstract_inverted_index.accuracy	119
abstract_inverted_index.balanced	153
abstract_inverted_index.cascaded	24
abstract_inverted_index.compared	79
abstract_inverted_index.harmonic	56
abstract_inverted_index.inverter	26
abstract_inverted_index.presents	3
abstract_inverted_index.proposed	65, 122, 140, 144, 187
abstract_inverted_index.reactive	133
abstract_inverted_index.voltage.	182
abstract_inverted_index.weights.	134
abstract_inverted_index.<mml:math	12, 69, 91, 109
abstract_inverted_index.NBP-based	66
abstract_inverted_index.Simulink,	195
abstract_inverted_index.algorithm	37
abstract_inverted_index.classical	83
abstract_inverted_index.component	44
abstract_inverted_index.efficient	166
abstract_inverted_index.harmonics	171
abstract_inverted_index.real-time	198
abstract_inverted_index.reference	54
abstract_inverted_index.technique	20, 77, 105, 124, 146
abstract_inverted_index.validated	190, 202
abstract_inverted_index.variation	178
abstract_inverted_index.<mml:mrow>	14, 71, 93, 111
abstract_inverted_index.<mml:msub>	94, 99
abstract_inverted_index.estimation	129
abstract_inverted_index.multilevel	25
abstract_inverted_index.non-linear	150
abstract_inverted_index.simulation	192
abstract_inverted_index.technique.	117
abstract_inverted_index.unbalanced	155
abstract_inverted_index.</mml:math>	19, 76, 104, 116
abstract_inverted_index.</mml:mrow>	18, 75, 103, 115
abstract_inverted_index.</mml:msub>	97, 102
abstract_inverted_index.\emptyset$$	11, 68, 108
abstract_inverted_index.conditions,	151
abstract_inverted_index.conditions.	157
abstract_inverted_index.fundamental	43
abstract_inverted_index.implemented	21
abstract_inverted_index.performance	59, 136
abstract_inverted_index.propagation	7
abstract_inverted_index.recommended	35, 163
abstract_inverted_index.techniques,	85
abstract_inverted_index.compensating	52
abstract_inverted_index.elimination.	57
abstract_inverted_index.experimental	205
abstract_inverted_index.investigated	148
abstract_inverted_index.effectiveness	184
abstract_inverted_index.<mml:mi>d</mml:mi>	96
abstract_inverted_index.<mml:mi>i</mml:mi>	15, 72, 95, 100, 112
abstract_inverted_index.<mml:mi>q</mml:mi>	101
abstract_inverted_index.<mml:mo>-</mml:mo>	98
abstract_inverted_index.<mml:mi>∅</mml:mi>	17, 74, 114
abstract_inverted_index.<mml:mo>cos</mml:mo>	16, 73, 113
abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML">	13, 70, 92, 110
cited_by_percentile_year
corresponding_author_ids	https://openalex.org/A5065744244
countries_distinct_count	2
institutions_distinct_count	5
corresponding_institution_ids	https://openalex.org/I865572129
citation_normalized_percentile.value	0.0541631
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False