Photovoltaic Module Grouping for Diagnostic Reference Value Calculation
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Solution Overview
Problem
Current methods for diagnosing photovoltaic systems are either costly due to the need for irradiance sensors or require extensive historical data for accuracy, leading to inefficiencies in detecting anomalies in complex systems.
Innovation Solution
A method that groups photovoltaic modules of the same orientation and inclination into sets, each with a sensor to measure electrical characteristics, calculates a reference value through iterative statistical processing, and compares measurements to detect deviations in energy production, allowing for efficient failure detection without the need for extensive sensor deployment or historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If irradiance sensors are deployed for each photovoltaic module to measure actual solar energy received, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent merges the diagnostic functions for multiple photovoltaic modules into a single centralized processing unit. Instead of deploying individual sensors and processing systems for each module, the invention combines all measurement data collection and diagnostic analysis into one system, reducing overall device complexity while maintaining measurement precision through centralized statistical processing of electrical quantity data from multiple modules.
Solution Approach 2:
The processing unit serves multiple functions: it collects electrical quantity data from multiple photovoltaic modules, performs iterative statistical processing to calculate reference values, detects anomalies through comparison, and generates diagnostic information. This multi-functional approach eliminates the need for separate dedicated systems for each module, reducing device complexity while maintaining comprehensive diagnostic capability.
2Measurement precision
If extensive historical data is collected to create an operating model for accurate anomaly detection, then measurement precision is improved, but loss of time increases due to data accumulation requirements
Solution Approach 1:
The system performs preliminary iterative statistical processing to establish reference values from collected electrical quantity data before actual anomaly detection begins. By pre-calculating these reference values through iterations that filter out abnormal measurements, the system prepares the diagnostic baseline in advance, enabling immediate accurate anomaly detection without requiring extensive additional historical data accumulation during operation.
Solution Approach 2:
The iterative statistical processing method allows the system to quickly skip through the data accumulation phase by using iterations to rapidly converge on accurate reference values. Instead of requiring long-term historical data collection, the system rushes through the reference value establishment process by iteratively processing available data, filtering abnormalities, and converging on reliable baselines much faster than traditional historical data accumulation methods.
3Measurement precision
If iterative statistical processing is performed to calculate reference values from multiple module measurements, then measurement precision is improved, but calculation time increases
Solution Approach 1:
The iterative statistical processing continues continuously with each iteration building upon the previous one, refining the reference values progressively. The process maintains continuous useful action by systematically processing electrical quantity data through multiple iterations, each iteration improving the accuracy of reference values while efficiently filtering out abnormal measurements, thereby achieving high measurement precision without excessive calculation time through optimized continuous processing.
4Productivity
If photovoltaic modules are grouped into sets with sensors for measuring electrical quantities, then productivity of diagnosis is improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent merges the diagnostic functions for multiple photovoltaic modules into a single centralized processing unit. Instead of deploying individual sensors and processing systems for each module, the invention combines all measurement data collection and diagnostic analysis into one system, reducing overall device complexity while maintaining measurement precision through centralized statistical processing of electrical quantity data from multiple modules.
Solution Approach 2:
The processing unit serves multiple functions: it collects electrical quantity data from multiple photovoltaic modules, performs iterative statistical processing to calculate reference values, detects anomalies through comparison, and generates diagnostic information. This multi-functional approach eliminates the need for separate dedicated systems for each module, reducing device complexity while maintaining comprehensive diagnostic capability.
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
This approach enables reliable and efficient detection of failures in photovoltaic systems, reducing costs and improving accuracy by using statistical analysis to identify anomalies in real-time, facilitating quick corrective actions and maintaining optimal energy production.
Implementation Method 1
a photovoltaic system comprising at least one photovoltaic sub-field comprising photovoltaic modules
Data Source
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AI summary
Method for diagnosing a photovoltaic system (1) comprising at least one photovoltaic subfield (2) comprising photovoltaic modules of the same orientation and inclination, grouped into several sets of at least one comparable photovoltaic module (3), each set of at least one module (3) being associated with at least one sensor (4, 4') for measuring an electrical quantity characteristic of said set of at least one module (3), characterized in that it comprises the following steps: (E1) - measurements of at least one electrical quantity by each sensor (4, 4') at several times of a period considered;(E2) - analysis of electrical quantity measurements comprising the following sub-steps: (E21) - calculation of a reference value from the electrical quantity measurements associated with several sets of at least one module (3) of the same photovoltaic subfield (2) by iterations allowing to set aside at each iteration measurements corresponding to drops in energy production of a set of at least one module; (E22) - comparison of the measurements associated with a set of at least one module (3) with the reference value over a chosen period in order to deduce or not a failure of said set of at least one module (3) over said period.