PV Array Fault Detection Using Deep Neural Networks
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Solution Overview
Problem
Current fault detection methods in photovoltaic (PV) arrays are inadequate as they fail to detect and localize a wide range of faults, such as soiling and short circuits, leading to reduced efficiency and potential safety hazards, with existing approaches often requiring manual intervention and taking significant time to repair.
Innovation Solution
A cyber-physical system using deep neural networks and customized algorithms deployed in feedforward neural networks for fault detection and classification at individual PV modules, incorporating sensor data and machine learning techniques to identify and classify multiple fault types concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault detection methods are used in PV arrays, then the system structure remains simple, but the fault detection capability is insufficient and cannot detect a wide range of faults
Solution Approach 1:
The patent replaces traditional mechanical/electrical fault detection methods with deep neural network-based intelligent detection. The system uses neural networks to analyze I-V curve data and automatically identify various fault types, substituting complex manual analysis and traditional detection algorithms with machine learning-based automated detection that can handle multiple fault types simultaneously.
Solution Approach 2:
The patent introduces I-V curve characteristics as an intermediary medium for fault detection. Instead of directly detecting faults through complex sensors or invasive methods, the system measures electrical parameters (current and voltage) to generate I-V curves, which then serve as the basis for neural network analysis to identify faults indirectly but effectively.
2Measurement precision
If existing fault detection approaches are used, then the system remains simple to operate, but the fault localization precision is insufficient and cannot identify faults at individual module level
Solution Approach 1:
The patent segments the PV array into individual module-level detection units. Each module's I-V characteristics are analyzed independently by the neural network, enabling precise localization of faults to specific modules rather than identifying faults only at the array level. This segmentation allows the system to pinpoint exact locations of soiling, shading, or electrical faults on individual panels.
Solution Approach 2:
The patent creates a virtual model or 'copy' of the PV array's electrical behavior through I-V curve measurements and neural network simulation. This digital representation allows the system to analyze fault conditions without physically accessing or disrupting the actual modules, maintaining ease of operation while achieving high precision through computational analysis.
3Adaptability or versatility
If comprehensive fault detection covering multiple fault types is implemented, then the fault detection coverage improves, but the detection time and processing complexity increase
Solution Approach 1:
The patent merges multiple fault detection capabilities into a single unified neural network model. Instead of implementing separate detection algorithms for each fault type (ground faults, arc faults, soiling, shading, etc.), the system uses one integrated neural network that processes I-V curve data and simultaneously identifies multiple fault types, reducing detection time while maintaining comprehensive coverage.
Solution Approach 2:
The patent performs preliminary analysis by pre-processing I-V curve data and extracting key features before neural network classification. The system prepares the data in advance by calculating important electrical parameters and characteristics, so that when faults occur, the neural network can quickly classify them without needing to process raw data from scratch, thereby reducing detection time.
Data Source
AI summary
Solar array fault detection, classification, and localization using deep neural nets is provided. A fault-identifying neural network uses a cyber-physical system (CPS) approach to fault detection in photovoltaic (PV) arrays. Customized neural network algorithms are deployed in feedforward neural networks for fault detection and identification from monitoring devices that sense data and actuate each individual module in a PV array. This approach improves efficiency by detecting and classifying a wide variety of faults and commonly occurring conditions (e.g., eight faults/conditions concurrently) that affect power output in utility scale PV arrays.