PV Module Fault Detection via Spatial Sunshine Correction
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Solution Overview
Problem
Existing photovoltaic electricity generating systems face challenges in efficiently detecting faulty modules due to high manufacturing costs of sensors and limitations in conventional fault detection methods, which often rely on comparing electrical outputs of strings, making it difficult to identify and isolate faulty modules quickly.
Innovation Solution
A fault detection apparatus and method that utilizes a module positional data storage unit, electrical output data storage unit, output characteristic model storage unit, sunshine condition estimation unit, sunshine condition spatial correction unit, and faulty electrical output detection unit to estimate sunshine conditions and detect faulty modules by comparing actual and expected electrical outputs, improving spatial continuity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are provided for all electricity generating modules to measure electrical outputs, then fault detection accuracy is improved, but manufacturing cost increases
Solution Approach 1:
The system segments the monitoring function by assigning unique identification values to each electricity generating module and calculating expected output values individually based on their specific sunshine conditions, rather than using a single average value for all modules. This allows precise fault detection without requiring sensors on each module.
Solution Approach 2:
The system introduces an intermediary calculation mechanism that uses identification values and sunshine condition data to derive expected electrical output values. This intermediary process acts as a virtual sensor, providing measurement capability without physical sensing devices on each module.
2Device complexity
If conventional string-level comparison methods are used to detect faults, then device complexity is reduced, but fault detection precision deteriorates
Solution Approach 1:
The system transitions from string-level aggregate monitoring to module-level individual monitoring by calculating expected output values for each module separately using their unique identification values. This segmentation enables precise identification of which specific module is faulty while maintaining relatively simple system architecture.
Solution Approach 2:
The system applies local quality by tailoring the expected output calculation to each specific module's sunshine conditions and identification value, rather than applying a uniform standard to all modules. This localized approach improves detection precision without significantly increasing overall system complexity.
3Ease of operation
If average electrical output per string is used for fault detection, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system enables each module to essentially monitor itself by using its unique identification value to calculate its own expected output value based on its specific sunshine conditions. The comparison between actual and expected values provides self-diagnostic capability without complex operational procedures.
Solution Approach 2:
The system changes the monitoring parameter from aggregate string-level averages to individual module-level expected values calculated using identification values and sunshine conditions. This parameter transformation maintains operational simplicity while dramatically improving detection precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system accurately detects faulty modules by correcting electrical output data to enhance spatial continuity and estimating the position of faulty modules, enabling timely identification and replacement, thereby improving the efficiency and reliability of photovoltaic electricity generation.
Implementation Method 1
A photovoltaic electricity generating system comprises strings, in which multiple electricity generating modules are connected in series
Data Source
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Figure 4A~4B
AI summary
According to an embodiment, an apparatus includes a sunshine condition estimation unit (204) configured to receive an actual electrical output from a string and an output characteristic model based on which an electrical output is estimated depending on a sunshine condition affecting electricity generation, and to set a value of sunshine condition in the output characteristic model which is closest to the actual electrical output as a sunshine condition estimate for each string based on the received actual electrical output and output characteristic model, a sunshine condition spatial correction unit (206) configured to calculate a first total estimate that is a sum of sunshine condition estimates of all output characteristic models included in a target string, to calculate a second total estimate that is a sum of sunshine condition estimates of all electricity generating modules included in each of first adjacent strings adjacent to both lengthwise surfaces of the target string, and to correct the sunshine condition estimates of the target string so that the first total estimate falls within a range determined by the second total estimates, and an electrical output fault detection unit (207) configured to identify a faulty electricity generating module when a difference between the actual electrical output and a sum of expected electrical outputs of the electricity generating modules calculated by using the output characteristic model and the corrected sunshine condition estimates within each string is greater than or equal to a first threshold, and the actual electrical outputs is smaller than the sum of the expected electrical outputs.