Accurate Fault Location Method Based on Time-Domain Information Estimation for Medium-Voltage Distribution Network

Sun, Guanqun; Chen, Rusi; Han, Zheyu; Liu, Haiguang; Liu, Meiyuan; Zhang, Ke; Xu, Chaozheng; Wang, Yikai

doi:10.3390/electronics12234733

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [6], addressing the limitations of traditional methods such as low sampling rates and inadequate sensitivity, a new approach for a precise localization of ground faults in distribution lines was proposed. This method leverages time-domain synchronization information for calculation.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Fault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

Lu,

Wang,

Wang

2024

Energies

View full text Add to dashboard Cite

The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system’s topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model’s troubleshooting decision-making process, reduces the model’s subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model’s effectiveness and adaptability.

show abstract

Section: Introductionmentioning

confidence: 99%

Microgrid Fault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

Lu,

Wang,

Wang

2024

Energies

View full text Add to dashboard Cite

show abstract

“…To theoretically eliminate positioning errors due to data asynchrony, parameter tests [18] and the introduction of the asynchronous phase angle difference δ at both ends are commonly used [19]. Reference [20] introduces a fault precise positioning method based on time-domain synchronous information estimation, which calculates the fault location based on estimated fault current differences at a finite number of points, thus addressing synchronization issues at low sampling rates for grounding faults in distribution lines. Reference [21] posits that time asynchrony between both ends equates to an asynchronous angle.…”

Section: Introductionmentioning

confidence: 99%

Fault Location Method for Distribution Network Using an Additional Inductance Strategy

Yang,

Xie,

Yin

2024

Electronics

View full text Add to dashboard Cite

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair.

show abstract

Accurate Fault Location Method Based on Time-Domain Information Estimation for Medium-Voltage Distribution Network

Cited by 2 publications

References 21 publications

Microgrid Fault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

Microgrid Fault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

Fault Location Method for Distribution Network Using an Additional Inductance Strategy

Contact Info

Product

Resources

About