Solar Power Abnormality Detection via Segmented Radiation Modeling
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Solution Overview
Problem
Existing solar power generation systems face challenges in accurately determining abnormalities due to variations in solar radiation caused by season and weather, leading to false detection of power generation losses.
Innovation Solution
A solar power generation system that constructs a generated power estimation model based on the correlative relationship between solar radiation intensity and generated power, allowing for accurate abnormality determination by comparing expected and actual power generation amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a generated power estimation model is constructed using accumulated solar radiation amount and generated power amount data, then it is possible to detect power generation losses, but false detection of abnormalities occurs due to natural variations in solar radiation caused by season and weather
Solution Approach 1:
The patent segments the solar radiation data by dividing it into multiple time periods (e.g., morning, noon, afternoon) and constructs separate estimation models for each segment. This allows the system to account for natural variations in solar radiation patterns throughout the day and across different seasons, reducing false detections while maintaining reliable abnormality detection capability.
2Ease of manufacture
If the generated power estimation model uses general accumulated data without time-based segmentation, then the model construction is simple, but the model cannot accurately reflect daily and seasonal variations in solar radiation
Solution Approach 1:
The patent divides the solar radiation data into multiple time periods and constructs separate estimation models for each segment. This segmentation approach maintains reasonable construction simplicity while significantly improving reliability by capturing the temporal patterns of solar radiation variation throughout the day and across seasons.
3Productivity
If the system determines abnormalities based on comparison between expected and actual generated power, then it can identify power generation issues, but it produces false positives when no abnormality exists and false negatives when an abnormality does exist
Solution Approach 1:
By segmenting the estimation model into multiple time periods, the system improves detection precision for each specific period. This reduces both false positives (detecting abnormalities when none exist) and false negatives (missing actual abnormalities) while maintaining efficient monitoring 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 more precise detection of abnormalities, reducing false positives and negatives, and improving the monitoring and maintenance of solar power generation systems.
Implementation Method 1
a solar power generation unit configured to generate power by receiving sunlight
Data Source
AI summary
The present disclosure relates to a solar power generation system, an abnormality determination processing device, an abnormality determination processing method, and a program, according to which it is possible to construct a generated power estimation model that can more accurately determine the occurrence of an abnormality. A generated power estimation model to be used for estimating an expected generated power amount that is expected to be generated by a solar power generation module is constructed based on a daily correlative relationship between a generated power amount generated by a solar power generation module that generates power by receiving sunlight, and an energy amount of sunlight emitted to the solar power generation module. Furthermore, the expected generated power amount is estimated based on the generated power estimation model, and it is determined whether or not an abnormality has occurred by comparing the expected generated power amount and the generated power amount actually generated by the solar power generation module. This technique can be applied to a solar power generation system, for example.