Comprehensive study of optimization algorithms at power electronics
DC-DC converters are extensively applied in lots of electronics devices to provide a stable voltage output. The control of converters and parameter estimation are two subjects of many researches. For simplicit, PID controllers become popular in various application. Based on the mathematical definiti...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/141475 |
| _version_ | 1811694183893696512 |
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| author | Wu, Yinghao |
| author2 | Jack Zhang Xin |
| author_facet | Jack Zhang Xin Wu, Yinghao |
| author_sort | Wu, Yinghao |
| collection | NTU |
| description | DC-DC converters are extensively applied in lots of electronics devices to provide a stable voltage output. The control of converters and parameter estimation are two subjects of many researches. For simplicit, PID controllers become popular in various application. Based on the mathematical definition, the application of PID controller greatly influences the overall performance of converters. As the improvement of Artificial intelligence (AI) technology, many evolutionary algorithms are introduced to deal with parameters optimization of PID controllers. This paper selects a DC-DC buck converter as a study target, applying Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to design the parameters of the PID controller. The results are evaluated by performance indexes like overshoot and steady-state time. On the other hand, parameter estimation plays a crucial part in fault prediction. BP Neural Network (BPNN) is applied to estimate passive components of buck converter by measuring output voltage and current ripples. With the aim of accuracy enhancement of the network, GA is combined with BPNN to search the optimal initialization of the network. |
| first_indexed | 2024-10-01T07:03:32Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/141475 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:03:32Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1414752023-07-04T15:35:51Z Comprehensive study of optimization algorithms at power electronics Wu, Yinghao Jack Zhang Xin School of Electrical and Electronic Engineering jackzhang@ntu.edu.sg Engineering::Electrical and electronic engineering DC-DC converters are extensively applied in lots of electronics devices to provide a stable voltage output. The control of converters and parameter estimation are two subjects of many researches. For simplicit, PID controllers become popular in various application. Based on the mathematical definition, the application of PID controller greatly influences the overall performance of converters. As the improvement of Artificial intelligence (AI) technology, many evolutionary algorithms are introduced to deal with parameters optimization of PID controllers. This paper selects a DC-DC buck converter as a study target, applying Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to design the parameters of the PID controller. The results are evaluated by performance indexes like overshoot and steady-state time. On the other hand, parameter estimation plays a crucial part in fault prediction. BP Neural Network (BPNN) is applied to estimate passive components of buck converter by measuring output voltage and current ripples. With the aim of accuracy enhancement of the network, GA is combined with BPNN to search the optimal initialization of the network. Master of Science (Power Engineering) 2020-06-08T10:36:30Z 2020-06-08T10:36:30Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/141475 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wu, Yinghao Comprehensive study of optimization algorithms at power electronics |
| title | Comprehensive study of optimization algorithms at power electronics |
| title_full | Comprehensive study of optimization algorithms at power electronics |
| title_fullStr | Comprehensive study of optimization algorithms at power electronics |
| title_full_unstemmed | Comprehensive study of optimization algorithms at power electronics |
| title_short | Comprehensive study of optimization algorithms at power electronics |
| title_sort | comprehensive study of optimization algorithms at power electronics |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/141475 |
| work_keys_str_mv | AT wuyinghao comprehensivestudyofoptimizationalgorithmsatpowerelectronics |