Industrial IoT based condition monitoring for wind energy conversion system Article Swipe
Md Liton Hossain
,
Ahmed Abu‐Siada
,
S. M. Muyeen
,
Md Mubashwar Hasan
,
Md Momtazur Rahman
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.17775/cseejpes.2020.00680
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.17775/cseejpes.2020.00680
Wind energy has been identified as the second dominating source in the world renewable energy generation after hydropower. Conversion and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system (WECS) including multilevel inverters (MLIs). The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link capacitor. This paper proposes a simplified proportional integral (PI)-based space vector pulse width modulation (SVPWM) to minimize the output waveform ripples, resolve the voltage balancing issue and produce better-quality output waveforms. WECS experiences various types of faults particularly in the DC-link capacitor and switching devices of the power converter. These faults, if not detected and rectified at an early stage, may lead to catastrophic failures to the WECS and continuity of the power supply. This paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real time. Since most wind power plants are located in remote areas or offshore, WECS condition monitoring needs to be developed over the internet of things (IoT) to ensure system reliability. In this paper, an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment. © 2015 CSEE.
Related Topics
Concepts
Wind power
Waveform
Rectifier (neural networks)
Renewable energy
Fault (geology)
Electrical engineering
Power (physics)
Voltage
Capacitor
Computer science
Pulse-width modulation
Engineering
Electronic engineering
Physics
Seismology
Artificial neural network
Quantum mechanics
Recurrent neural network
Stochastic neural network
Machine learning
Geology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17775/cseejpes.2020.00680
- OA Status
- diamond
- Cited By
- 30
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3163480113
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3163480113Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17775/cseejpes.2020.00680Digital Object Identifier
- Title
-
Industrial IoT based condition monitoring for wind energy conversion systemWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Md Liton Hossain, Ahmed Abu‐Siada, S. M. Muyeen, Md Mubashwar Hasan, Md Momtazur RahmanList of authors in order
- Landing page
-
https://doi.org/10.17775/cseejpes.2020.00680Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.17775/cseejpes.2020.00680Direct OA link when available
- Concepts
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Wind power, Waveform, Rectifier (neural networks), Renewable energy, Fault (geology), Electrical engineering, Power (physics), Voltage, Capacitor, Computer science, Pulse-width modulation, Engineering, Electronic engineering, Physics, Seismology, Artificial neural network, Quantum mechanics, Recurrent neural network, Stochastic neural network, Machine learning, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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30Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 7, 2023: 6, 2022: 9, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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