Photovoltaic String Fault Identification via Tangent Slope Analysis
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Solution Overview
Problem
Current photovoltaic power station monitoring relies heavily on manual inspections, resulting in high labor costs, heavy workloads, and low efficiency, with faults such as shading and potential-induced degradation often going undetected until they cause significant power loss.
Innovation Solution
A method that determines the characteristic curve of each photovoltaic string, calculates the slope differences of tangent lines, and judges whether the absolute value exceeds a preset threshold to automatically identify normal or faulty states, enabling automated fault detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by workers is used to monitor photovoltaic modules, then fault detection can be performed, but labor cost increases and work efficiency decreases
Solution Approach 1:
The photovoltaic string self-monitors its own工作状态 by collecting voltage and current data from its terminals, processing this data through a controller to generate characteristic curves, and automatically identifying faults without requiring external manual inspection. This self-service mechanism eliminates the need for worker intervention while maintaining continuous monitoring capability.
Solution Approach 2:
The patent replaces the mechanical manual inspection system with an automated electronic monitoring system that uses voltage and current sensors, data processing circuits, and algorithm-based fault identification. This substitution transforms physical manual examination into electronic measurement and automated analysis, dramatically improving work efficiency.
2Reliability
If manual detection of modules in parallel is performed to find faulty modules, then fault location can be identified, but detection time increases and efficiency decreases
Solution Approach 1:
The patent divides the photovoltaic array into multiple independently monitorable strings, each with its own characteristic curve and fault identification process. By segmenting the system this way, the fault identification can be performed simultaneously on multiple strings through automated data processing, rather than requiring sequential manual inspection of each module.
Solution Approach 2:
The system continuously collects voltage and current data and pre-processes it into characteristic curves before faults occur. When a fault happens, the pre-prepared data and established analysis algorithms enable immediate fault identification without requiring time-consuming manual detection procedures.
3Productivity
If automated fault detection is implemented using characteristic curve analysis, then work efficiency improves and labor cost decreases, but system complexity increases
Solution Approach 1:
The controller serves multiple functions: it collects voltage and current data from photovoltaic strings, processes this data to generate characteristic curves, performs fault identification analysis, and outputs results. This multi-functionality consolidates what could be separate complex systems into a single integrated device, reducing overall system complexity while maintaining automated detection capabilities.
Solution Approach 2:
The characteristic curve acts as an intermediary representation between the raw voltage-current data and the fault identification decision. By transforming complex electrical measurements into a standardized curve format with defined working regions, the system simplifies the analysis process and makes fault detection more straightforward despite the underlying complexity of photovoltaic system behavior.
4Ease of manufacture
If conventional monitoring methods are used, then implementation is simple, but fault detection capability is insufficient and power loss increases
Solution Approach 1:
The patent collects and analyzes more data than conventional methods by continuously monitoring voltage and current to generate complete characteristic curves. This excessive data collection and analysis approach ensures that even subtle faults that would escape simpler monitoring methods are detected, improving reliability while the added complexity is managed through automated processing.
Data Source
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AI summary
A photovoltaic string fault identification method comprises: determining a current characteristic curve of each string (S101), determining a tangent slope of each point on the current characteristic curve (S102), and calculating a difference between the tangent slopes of any adjacent two points (S103); then, determining whether an absolute value of the difference is less than a first preset value (S104); if yes, determining that the string is in a first working state (S105); if no, determining that the string is in a second working state (S106), wherein the first working state comprises a normal working state, and the second working state comprises a faulty state. Thus, the method can automatically determine whether a fault occurs on the each string and severity of the fault, effectively solving problems of high workload and low efficiency in prior art where a photovoltaic component is manually monitored.