PV Module Useful Life Prediction Using Hygrothermal Stress
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Solution Overview
Problem
Current methods fail to accurately predict the useful life of photovoltaic modules due to inadequate consideration of hygrothermal stress and environmental factors, leading to inefficient maintenance and performance evaluation.
Innovation Solution
An information processing apparatus and method that predicts the useful life of photovoltaic modules by acquiring and processing data on hygrothermal stress, including daily maximum temperatures and humidity, to estimate the period of effective power output, using algorithms and correction coefficients to account for environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional useful life prediction methods are used for photovoltaic modules, then the prediction process is simple, but the prediction accuracy is insufficient due to inadequate consideration of hygrothermal stress and environmental factors
Solution Approach 1:
The patent applies parameter changes by transforming environmental data (temperature, humidity) into hygrothermal stress parameters that quantify the degradation effect. The system calculates hygrothermal stress based on temperature and humidity measurements, then uses these stress parameters to predict useful life, thereby improving prediction accuracy through physically meaningful parameter transformation
Solution Approach 2:
The patent introduces hygrothermal stress as an intermediary parameter that mediates between environmental conditions (temperature, humidity) and module degradation. This intermediary concept allows the system to account for combined environmental effects without directly modeling complex degradation mechanisms, balancing accuracy with computational feasibility
2Measurement precision
If detailed environmental data collection and processing is implemented to improve prediction accuracy, then the useful life prediction becomes more accurate, but the data processing requirements and computational resources increase
Solution Approach 1:
The patent extracts only the essential environmental parameters (temperature and humidity) that most significantly affect photovoltaic module degradation. By focusing on these key parameters rather than collecting and processing all possible environmental data, the system achieves acceptable prediction accuracy with reduced computational energy consumption
Solution Approach 2:
The system transforms detailed environmental time-series data into aggregated hygrothermal stress parameters that capture the essential degradation effect. This parameter transformation reduces the dimensionality of the data processing task while preserving the information needed for accurate useful life prediction
Data Source
AI summary
An apparatus for predicting useful life of a photovoltaic module includes an input and an output. The input receives first information indicating an amount of hygrothermal stress that a photovoltaic module undergoes from a start until an end of a period during which the photovoltaic module outputs predetermined electric power. The input further receives second information indicating an amount of hygrothermal stress that the photovoltaic module undergoes per a predetermined time in a field where the photovoltaic module is deployed. The second information is generated based on information about daily maximum temperatures of the photovoltaic module in the field where the photovoltaic module is deployed. The output outputs result information about a predicted period during which the photovoltaic module is expected to output the predetermined electric power when the photovoltaic module is deployed in the field.