Realization and research of self-healing technology of power communication equipment based on power safety and controllability
Abstract The reliability of power communication networks is vital to ensure uninterrupted operation in power electronics. Self-healing techniques address this need by automating fault identification and recovery. However, existing methods struggle with dynamic challenges like voltage fluctuations, t...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-024-00460-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559069814423552 |
|---|---|
| author | Danni Liu Song Zhang Shengda Wang Mingwei Zhou Ji Du |
| author_facet | Danni Liu Song Zhang Shengda Wang Mingwei Zhou Ji Du |
| author_sort | Danni Liu |
| collection | DOAJ |
| description | Abstract The reliability of power communication networks is vital to ensure uninterrupted operation in power electronics. Self-healing techniques address this need by automating fault identification and recovery. However, existing methods struggle with dynamic challenges like voltage fluctuations, thermal overloads, and multidimensional sensor data, often leading to delays in fault recovery and reduced safety. This study aims to develop the Self Heal Power Safe Predictor (SHPSP) model to overcome the limitations of prior self-healing techniques. The primary objectives include improving fault prediction accuracy, enhancing recovery speed, and ensuring resilience under diverse and high-stress operational conditions. The SHPSP model employs an ensemble-based classification strategy within a majority voting framework, focusing on multidimensional sensor data such as voltage, temperature, and safety indicators. Feature selection is optimized using ensembled filter and wrapper techniques to prioritize critical parameters. The model is validated against conventional methods using metrics like accuracy, precision, recall, F1-score, and MCC. Experimental results demonstrate that the SHPSP model significantly outperforms previous approaches, achieving higher fault detection accuracy and faster recovery, particularly during voltage drops, power surges, and thermal stress. The SHPSP classifier obtained 91.4% accuracy, 88.2% precision, 89.5% recall, 89.8% F1-score, 81.0% MCC, and a 92.0% ROC-AUC curve. The SHPSP model ensures enhanced safety, dependability, and robustness for power electronics systems, marking a significant advancement in self-healing technology. |
| format | Article |
| id | doaj-art-69dd5d2709b045a3888c85ed83ba5139 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-69dd5d2709b045a3888c85ed83ba51392025-01-05T12:48:02ZengSpringerOpenEnergy Informatics2520-89422025-01-018111810.1186/s42162-024-00460-xRealization and research of self-healing technology of power communication equipment based on power safety and controllabilityDanni Liu0Song Zhang1Shengda Wang2Mingwei Zhou3Ji Du4Jilin Information & Telecommunication Company, State Grid Jilin Electric PowerJilin Information & Telecommunication Company, State Grid Jilin Electric PowerJilin Information & Telecommunication Company, State Grid Jilin Electric PowerJilin Information & Telecommunication Company, State Grid Jilin Electric PowerJilin Jineng Electric Power Communication Co., Ltd.Abstract The reliability of power communication networks is vital to ensure uninterrupted operation in power electronics. Self-healing techniques address this need by automating fault identification and recovery. However, existing methods struggle with dynamic challenges like voltage fluctuations, thermal overloads, and multidimensional sensor data, often leading to delays in fault recovery and reduced safety. This study aims to develop the Self Heal Power Safe Predictor (SHPSP) model to overcome the limitations of prior self-healing techniques. The primary objectives include improving fault prediction accuracy, enhancing recovery speed, and ensuring resilience under diverse and high-stress operational conditions. The SHPSP model employs an ensemble-based classification strategy within a majority voting framework, focusing on multidimensional sensor data such as voltage, temperature, and safety indicators. Feature selection is optimized using ensembled filter and wrapper techniques to prioritize critical parameters. The model is validated against conventional methods using metrics like accuracy, precision, recall, F1-score, and MCC. Experimental results demonstrate that the SHPSP model significantly outperforms previous approaches, achieving higher fault detection accuracy and faster recovery, particularly during voltage drops, power surges, and thermal stress. The SHPSP classifier obtained 91.4% accuracy, 88.2% precision, 89.5% recall, 89.8% F1-score, 81.0% MCC, and a 92.0% ROC-AUC curve. The SHPSP model ensures enhanced safety, dependability, and robustness for power electronics systems, marking a significant advancement in self-healing technology.https://doi.org/10.1186/s42162-024-00460-xSelf-healing technologiesPower communication systemsFault detection and recoveryPower safety and reliabilityEnsemble-based classification |
| spellingShingle | Danni Liu Song Zhang Shengda Wang Mingwei Zhou Ji Du Realization and research of self-healing technology of power communication equipment based on power safety and controllability Energy Informatics Self-healing technologies Power communication systems Fault detection and recovery Power safety and reliability Ensemble-based classification |
| title | Realization and research of self-healing technology of power communication equipment based on power safety and controllability |
| title_full | Realization and research of self-healing technology of power communication equipment based on power safety and controllability |
| title_fullStr | Realization and research of self-healing technology of power communication equipment based on power safety and controllability |
| title_full_unstemmed | Realization and research of self-healing technology of power communication equipment based on power safety and controllability |
| title_short | Realization and research of self-healing technology of power communication equipment based on power safety and controllability |
| title_sort | realization and research of self healing technology of power communication equipment based on power safety and controllability |
| topic | Self-healing technologies Power communication systems Fault detection and recovery Power safety and reliability Ensemble-based classification |
| url | https://doi.org/10.1186/s42162-024-00460-x |
| work_keys_str_mv | AT danniliu realizationandresearchofselfhealingtechnologyofpowercommunicationequipmentbasedonpowersafetyandcontrollability AT songzhang realizationandresearchofselfhealingtechnologyofpowercommunicationequipmentbasedonpowersafetyandcontrollability AT shengdawang realizationandresearchofselfhealingtechnologyofpowercommunicationequipmentbasedonpowersafetyandcontrollability AT mingweizhou realizationandresearchofselfhealingtechnologyofpowercommunicationequipmentbasedonpowersafetyandcontrollability AT jidu realizationandresearchofselfhealingtechnologyofpowercommunicationequipmentbasedonpowersafetyandcontrollability |