Photovoltaic I-V Curve Clustering for Fast Partial-Shading Simulation
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Solution Overview
Problem
Existing photovoltaic system simulation methods struggle to accurately and efficiently model dynamic conditions, such as partial shading and varying environmental factors, while also accounting for reconfigurable modules with complex topologies and hardware components, leading to computational inefficiencies and inaccuracies.
Innovation Solution
A method and system that utilize a database of current-voltage characteristic curves, clustered based on similarity, to efficiently simulate photovoltaic systems under varying conditions, incorporating electrical-optical and thermo-optical models, and account for internal temperatures and irradiations, reducing computational complexity through clustering and parameterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed white-box models are used for accurate simulation of photovoltaic systems under dynamic conditions, then measurement precision is improved, but productivity deteriorates due to computational slowness
Solution Approach 1:
The patent pre-calculates and stores current-voltage characteristic curves for various operating conditions (irradiance levels, temperatures, partial shading scenarios) in a database before actual simulation runs. During runtime, the system retrieves and interpolates from these pre-computed curves rather than performing detailed physical calculations, thus achieving both accuracy and speed.
Solution Approach 2:
The patent creates simplified representative copies of the complex photovoltaic system behavior through current-voltage characteristic curves that capture essential electrical properties. These curves serve as lightweight models that replicate the system's response under different conditions without requiring full detailed modeling, enabling fast evaluation of multiple configurations.
2Adaptability or versatility
If conventional static photovoltaic module designs are used, then device complexity is reduced, but adaptability deteriorates under non-uniform dynamic conditions such as partial shading
Solution Approach 1:
The patent enables dynamic reconfiguration of photovoltaic modules by incorporating switchable connections that allow cells to be reorganized into different series-parallel configurations during operation. This dynamic adaptability allows the system to optimize performance under varying conditions such as partial shading, where static configurations would be suboptimal.
Solution Approach 2:
The patent changes the electrical configuration parameters of the photovoltaic module by adjusting the number of cells in series and parallel connections based on operating conditions. The system evaluates different configuration parameters (topologies) and selects the optimal one for current conditions, effectively adapting the module's electrical characteristics to match environmental conditions.
3Adaptability or versatility
If reconfigurable modules with multiple topology options are implemented, then adaptability is improved for dynamic conditions, but device complexity increases leading to larger configuration spaces that are difficult to optimize
Solution Approach 1:
The patent pre-evaluates and stores performance characteristics for multiple possible module configurations and topologies in a database before actual operation. By pre-computing the current-voltage curves for various configurations under different conditions, the system reduces the online optimization problem to a simpler selection and interpolation task, making real-time control feasible despite the large configuration space.
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
Enables accurate and efficient simulation of photovoltaic systems under dynamic conditions, allowing for rapid exploration of large configuration spaces and reduced computational burden, while maintaining high accuracy in energy yield predictions.
Implementation Method 1
simulating an electrical-optical model or an electrical-thermo-optical model of the photovoltaic cell group
Implementation Method 2
simulating an electrical-optical model or an electrical-thermo-optical model of the photovoltaic cell group
Data Source
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AI summary
A method (20) for generating/ updating a database of current-voltage characteristic curves is disclosed. This method comprises simulating (25), for at least one combination of a topology of a photovoltaic cell group, an internal cell temperature(s) and a cell irradiation(s), a model of the photovoltaic cell group to provide a current-voltage characteristic curve representative of that combination, and clustering (24) the current-voltage characteristic curves to identify at least one plurality of similar current-voltage characteristic curves. The method also comprises generating (26) a many-to-one mapping in the database to map query requests that correspond to each of the at least one plurality of similar current-voltage characteristic curves onto a single representative current-voltage characteristic curve for that plurality of similar current-voltage characteristic curves, each query request identifying a topology of a photovoltaic cell group, at least one internal temperature for the photovoltaic cells in the photovoltaic cell group and at least one cell irradiation for the photovoltaic cells in the photovoltaic cell group.