PV Module Soiling Detection via Power Deviation Trends

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Solution Overview

Problem

Existing methods for detecting the degree of soiling on PV modules are not precise and are dependent on additional measuring devices, leading to inefficiencies in cleaning and forecasting, particularly in areas with water shortages.

Innovation Solution

A method that calculates the degree of contamination by determining deviations in string power values from a reference value, using historical trends and accounting for sensor misalignment and aging, allowing for precise contamination detection without additional equipment and minimizing water usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If additional measuring devices are used to detect soiling levels, then measurement precision is improved, but device complexity increases

Engineering Contradiction:
Improvesoiling detection precisionVSAvoidmeasuring device complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The PV system uses its own operational data (power output, irradiation measurements) to detect soiling levels without requiring external specialized measuring devices. The system self-monitors its performance and compares actual output against expected output based on irradiation conditions, thereby detecting soiling through its existing operational parameters.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent introduces an intermediary calculation method that uses the ratio between actual power output and expected power output (under clear sky conditions) as a mediator to infer soiling levels. This intermediary metric bridges the gap between measurable electrical parameters and the unmeasurable soiling condition.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Productivity

If continuous monitoring with high temporal resolution is implemented, then productivity is improved, but use of energy increases

Engineering Contradiction:
Improvecontamination detection speedVSAvoidenergy consumption for monitoring
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs contamination detection at periodic intervals (e.g., daily or at specific times) rather than continuously monitoring. It calculates contamination levels by comparing operational data at discrete time points, which reduces computational load and energy consumption while maintaining sufficient temporal resolution for practical cleaning scheduling.

Inventive Principle:
Principle #19Periodic action

3Productivity

If cleaning is performed more frequently to maintain high efficiency, then productivity is improved, but loss of substance increases

Engineering Contradiction:
ImprovePV system efficiencyVSAvoidwater consumption
Core Design Contradiction:
ProductivityVSLoss of substance

Solution Approach 1:

The system implements feedback-based cleaning scheduling by continuously monitoring contamination levels and triggering cleaning operations only when contamination exceeds a threshold or when performance degradation becomes significant. This feedback mechanism optimizes cleaning frequency to match actual contamination accumulation rates, avoiding unnecessary cleaning operations and water consumption.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The patent changes the operational parameter from fixed-schedule cleaning to condition-based cleaning by introducing contamination level as a dynamic parameter. Cleaning decisions are made based on real-time or near-real-time contamination measurements, allowing the system to adapt cleaning frequency to actual environmental conditions and soiling rates.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP2950446B8Method for detecting the contamination level of pv modules
Publication Date: 2018.09.12 SKYTRON ENERGY

AI summary

A method for detecting the degree of pollution of PV modules (2) of a string (3) comprises the following steps: - Determining the deviations (33) of the string power values ​​(31) from a calculated reference value (30) over the last year; - Calculating a historical trend line (34) from the deviations (33); - Determining a maximum difference (35) between the trend line (34) and the deviations (33); - Calculating final deviations (36) of the power values ​​by subtracting the maximum difference (35) from the trend line (34); and - Determining the degree of pollution (39) by subtracting the final deviations from the deviations.