PV Soiling Model Calibration for Cleaning Schedule Optimization
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Solution Overview
Problem
Accurate prediction of photovoltaic (PV) array output is hindered by soiling, which varies due to different geographic factors, leading to inefficient power generation and equipment degradation.
Innovation Solution
A system that optimizes soiling model parameters using sensors and a parameter-tuning model to predict soiling losses, adjusting parameters based on actual and predicted losses, and optimizes cleaning events to enhance power generation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If soiling model parameters are optimized using parameter-tuning model and sensors, then prediction accuracy of soiling losses is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system implements feedback by continuously comparing predicted soiling losses with actual measured losses from sensors, then using this discrepancy to iteratively adjust model parameters through the parameter-tuning model, improving prediction accuracy over time
Solution Approach 2:
The parameter-tuning model automatically optimizes soiling model parameters using data from sensors and historical performance, enabling the system to self-calibrate and improve without manual intervention, reducing operational complexity
2Productivity
If cleaning events are optimized based on predicted soiling losses, then power generation efficiency is improved, but loss of time for cleaning operations increases
Solution Approach 1:
The system performs preliminary action by predicting future soiling losses and proactively scheduling cleaning events before significant power loss occurs, allowing optimization of cleaning timing to minimize both power loss and unnecessary cleaning operations
Solution Approach 2:
The system changes parameters by dynamically adjusting cleaning event schedules based on predicted soiling rates, environmental conditions, and power loss thresholds, optimizing the balance between maintaining power generation efficiency and minimizing cleaning time investment
Data Source
AI summary
A method for optimizing a parameter set for a photovoltaic (PV) soiling model including executing the PV soiling model with a first plurality of parameter sets using as input wash data to generate first predicted energy loss data, calculating first losses based on a difference between the first predicted energy loss data and time-series sensor data, executing a parameter-tuning model using as input the first plurality of parameter sets to generate a second plurality of parameter sets, executing the PV soiling model with the second plurality of parameter sets using as input the wash data to generate second predicted energy loss data, calculating second losses based on the second predicted energy loss data and the time-series sensor data, and, in response to a loss of the second losses being below a threshold, generating a data structure including a parameter set corresponding to the loss.