Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids
Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/4/908 |
| _version_ | 1826582610892029952 |
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| author | Amir Hossein Poursaeed Farhad Namdari |
| author_facet | Amir Hossein Poursaeed Farhad Namdari |
| author_sort | Amir Hossein Poursaeed |
| collection | DOAJ |
| description | Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems. |
| first_indexed | 2025-03-14T15:08:42Z |
| format | Article |
| id | doaj.art-5ab2d46b1a324d069a941e27899faf0a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2025-03-14T15:08:42Z |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5ab2d46b1a324d069a941e27899faf0a2025-02-25T13:27:12ZengMDPI AGEnergies1996-10732025-02-0118490810.3390/en18040908Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC MicrogridsAmir Hossein Poursaeed0Farhad Namdari1Department of Electrical Engineering, Faculty of Engineering, Lorestan University, Khorram Abad 68151-44316, IranDepartment of Engineering, Faculty of Environment, Science, and Economy, University of Exeter, Exeter EX4 4QF, UKFault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems.https://www.mdpi.com/1996-1073/18/4/908DC microgridsfault locationquantum neural networksexplainable artificial intelligencehigh-order synchrosqueezing transformtraveling waves |
| spellingShingle | Amir Hossein Poursaeed Farhad Namdari Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids Energies DC microgrids fault location quantum neural networks explainable artificial intelligence high-order synchrosqueezing transform traveling waves |
| title | Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids |
| title_full | Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids |
| title_fullStr | Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids |
| title_full_unstemmed | Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids |
| title_short | Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids |
| title_sort | explainable ai driven quantum deep neural network for fault location in dc microgrids |
| topic | DC microgrids fault location quantum neural networks explainable artificial intelligence high-order synchrosqueezing transform traveling waves |
| url | https://www.mdpi.com/1996-1073/18/4/908 |
| work_keys_str_mv | AT amirhosseinpoursaeed explainableaidrivenquantumdeepneuralnetworkforfaultlocationindcmicrogrids AT farhadnamdari explainableaidrivenquantumdeepneuralnetworkforfaultlocationindcmicrogrids |