File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/978-3-540-30561-3_38
- Scopus: eid_2-s2.0-35048884218
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: Optimizing surplus harmonics distribution in PWM
Title | Optimizing surplus harmonics distribution in PWM |
---|---|
Authors | |
Keywords | Constrained nonlinear optimization Evolutionary Algorithm Quantum-inspired Surplus harmonics |
Issue Date | 2004 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 3356, p. 366-375 How to Cite? |
Abstract | The goal of optimal pulse-width modulation (PWM) is to select the switching instances in such a way that a waveform with a particular characteristic is obtained and a certain criterion is minimized. The conventional method to solve the optimal PWM problem would usually lead to large content of surplus harmonics immediately following the eliminated frequency band, which may increase the filter loss and reduce the efficiency and performance of the whole controller. Meanwhile, it may increase the probability of resonance between line impedance and filter components. To overcome the shortcomings of conventional PWM methods, in this paper, we propose an algorithm for pushing the first crest of the surplus harmonics backward, ameliorating the amplitude frequency spectrum distribution of the output waveform, and thus reducing the impact of surplus harmonics. The problem is first formulated as a constrained optimization problem and then a Quantum-inspired Evolutionary Algorithm (QEA) algorithm is applied to solve it. Other than Newton-like methods, the enhanced QEA does not need good initial values for solving the optimal PWM problem and is not stuck in local optimum. The simulation results indicate that the algorithm is robust and scalable for a variety of application requirements. © Springer-Verlag 2004. |
Persistent Identifier | http://hdl.handle.net/10722/336060 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hu, Shiyan | - |
dc.contributor.author | Huang, Han | - |
dc.date.accessioned | 2024-01-15T08:22:27Z | - |
dc.date.available | 2024-01-15T08:22:27Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 3356, p. 366-375 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336060 | - |
dc.description.abstract | The goal of optimal pulse-width modulation (PWM) is to select the switching instances in such a way that a waveform with a particular characteristic is obtained and a certain criterion is minimized. The conventional method to solve the optimal PWM problem would usually lead to large content of surplus harmonics immediately following the eliminated frequency band, which may increase the filter loss and reduce the efficiency and performance of the whole controller. Meanwhile, it may increase the probability of resonance between line impedance and filter components. To overcome the shortcomings of conventional PWM methods, in this paper, we propose an algorithm for pushing the first crest of the surplus harmonics backward, ameliorating the amplitude frequency spectrum distribution of the output waveform, and thus reducing the impact of surplus harmonics. The problem is first formulated as a constrained optimization problem and then a Quantum-inspired Evolutionary Algorithm (QEA) algorithm is applied to solve it. Other than Newton-like methods, the enhanced QEA does not need good initial values for solving the optimal PWM problem and is not stuck in local optimum. The simulation results indicate that the algorithm is robust and scalable for a variety of application requirements. © Springer-Verlag 2004. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Constrained nonlinear optimization | - |
dc.subject | Evolutionary Algorithm | - |
dc.subject | Quantum-inspired | - |
dc.subject | Surplus harmonics | - |
dc.title | Optimizing surplus harmonics distribution in PWM | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-540-30561-3_38 | - |
dc.identifier.scopus | eid_2-s2.0-35048884218 | - |
dc.identifier.volume | 3356 | - |
dc.identifier.spage | 366 | - |
dc.identifier.epage | 375 | - |
dc.identifier.eissn | 1611-3349 | - |