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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.

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Updated: Jun 27, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Published on: December 15, 2023

A Dual-Channel Enhanced Mamba Model for Fault Detection in Grid-Connected Photovoltaic Systems.

Yu Zhu¹, Qiang Yang¹

¹College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This study introduces a novel dual-channel Mamba model for photovoltaic (PV) fault detection, overcoming data scarcity with realistic simulations. The enhanced model accurately identifies faults in grid-connected PV systems.

Keywords:

fault detection photovoltaic systems time-series data

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Published on: October 28, 2022

Area of Science:

Electrical Engineering
Renewable Energy Systems
Artificial Intelligence

Background:

Grid-connected photovoltaic (PV) systems require accurate fault detection for safe and reliable operation.
Existing data-driven methods struggle with limited labeled fault data and modeling complex temporal dependencies.

Purpose of the Study:

To develop a robust data-driven approach for fault detection in grid-connected PV systems.
To address the challenges of data scarcity and complex temporal dependencies in PV fault analysis.

Main Methods:

Developed a realistic PV system simulation to generate extensive labeled multivariate time-series fault data.
Proposed a dual-channel enhanced Mamba model to decouple temporal and variable-wise modeling.
Utilized simulated data to augment limited real-world measurements for improved model generalization.

Main Results:

The dual-channel Mamba model achieved superior performance in accuracy, precision, recall, and F1-score compared to mainstream methods.
The simulation approach effectively generated diverse fault data, enhancing fault coverage.
The proposed model demonstrated effective extraction of global temporal dependencies and intra-variable dynamics.

Conclusions:

The proposed dual-channel enhanced Mamba model offers an effective and scalable solution for PV fault detection.
Data augmentation through realistic simulation is crucial for improving the performance of data-driven fault detection models.
The model's ability to handle complex temporal dependencies enhances the reliability of grid-connected PV systems.