MLPE-Based PV Module Parameter Validation
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Solution Overview
Problem
The existing methods for evaluating distributed energy resource (DER) performance are often biased due to differences between the as-designed and as-built configurations, leading to inaccuracies in computing system parameters such as expected energy production.
Innovation Solution
The use of module-level power electronics (MLPE) to measure and validate parameters like temperature, DC current, and DC voltage, allowing for the inference of latitude, longitude, azimuth, and tilt of PV modules, thereby confirming whether the 'as built' configuration matches the 'as designed' metadata and ensuring accurate performance expectations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DER performance evaluation methods are used, then the evaluation process is simple, but the accuracy of system parameters is biased due to configuration differences
Solution Approach 1:
The MLPE devices perform self-validation by automatically comparing their operational parameters (temperature, current, voltage) against expected values derived from design specifications. This self-service approach enables the system to detect configuration deviations without requiring external validation equipment, thereby improving measurement precision while minimizing additional system complexity
Solution Approach 2:
The system implements a feedback mechanism where MLPE data is continuously monitored and compared against expected performance parameters. When deviations are detected, the system generates alerts and facilitates corrective actions. This closed-loop feedback approach ensures accurate parameter validation while maintaining operational simplicity through automated comparison and notification processes
2Measurement precision
If MLPE data collection is implemented, then parameter validation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the critical parameters (temperature, current, voltage) from the comprehensive MLPE data stream that are necessary for validation purposes. By filtering and extracting only the essential data elements needed for configuration validation, the system achieves high validation accuracy while minimizing the complexity of data processing and analysis
Solution Approach 2:
The validation process is segmented into distinct analytical steps: data collection from MLPE devices, parameter extraction, comparison against expected values, and deviation detection. This segmentation of the validation process into modular, manageable stages reduces overall data processing complexity while maintaining comprehensive validation accuracy through systematic analysis
Data Source
AI summary
A method and apparatus for validating distributed energy resource as-designed parameters. In one embodiment the method comprises obtaining, from MLPE coupled to a PV module of the DER, data corresponding to sunrise on a particular day; obtaining, from MLPE, data corresponding to sunset on the particular day; determining, by the computer system and using the data corresponding to the sunrise and the data corresponding to the sunset, (i) the length of the particular day and (ii) the solar noon for the particular day; computing, by the computer system and using the length of the particular day and the solar noon for the particular day, an as-built latitude for the PV module and an as-built longitude for the PV module; and comparing, by the computer system, (a) the as-built latitude to an as-designed latitude for the PV module, and (b) the as-built longitude to an as-designed longitude for the PV module.