Adaptive Switching Redundant-Mode Multi-Core System for Photovoltaic Power Generation

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system's reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system's performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system's reliability while ensuring a good performance is maintained.

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Publication Details

Journal
Sensors
Published
2024-11-27
DOI
https://doi.org/10.3390/s24237561
Citations
2
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.20
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article

Adaptive Switching Redundant-Mode Multi-Core System for Photovoltaic Power Generation

Zixuan Guo, Xige Zhang, Jiahui Zhou, Meng Zhang et al.
2 citations
Sensors
Photovoltaic System Optimization Techniques
0.20
article

Adaptive Switching Redundant-Mode Multi-Core System for Photovoltaic Power Generation

Zixuan Guo, Xige Zhang, Jiahui Zhou, Meng Zhang, Kai Niu, Liang Liu, Yawen Zhao
article en
2 citations

Abstract

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system's reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system's performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system's reliability while ensuring a good performance is maintained.

SensorsVol. 24(23)
Northwestern Polytechnical University (CN), Beijing Microelectronics Technology Institute (CN)
Affordable and clean energy
Openalex Percentile: Top 57%
Photovoltaic System Optimization Techniques
0.20
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Adaptive Switching Redundant-Mode Multi-Core System for Photovoltaic Power Generation — Zixuan Guo, Xige Zhang, et al. · Sensors (2024) | TGRS Research Map | TGRS