PV-Energy Storage Leakage Risk Modeling Under Rainstorm Water Accumulation
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Solution Overview
Problem
Existing leakage current awareness technologies do not consider the influence of accumulated water in rainstorms on distributed power supply systems and fail to analyze the spatio-temporal distribution characteristics of rainstorms, leading to inadequate electricity-related security measures.
Innovation Solution
A method is developed to derive a multi-dimensional parallel parasitic capacitance analysis model for distributed photovoltaic-energy storage power supply systems, incorporating accumulated water depth and micro-terrain environment, to establish a leakage current calculation model. This involves dataset preprocessing, spatio-temporal correlation analysis, and deep meta-learning for predicting leakage current probabilities, considering rainfall intensity, duration, and micro-terrain factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing leakage current awareness technology is used, then the system structure remains simple, but it fails to account for accumulated water influence and spatio-temporal rainstorm distribution, leading to inaccurate security risk assessment
Solution Approach 1:
The patent segments the rainstorm influence into multiple independent parasitic capacitance components (C1, C2, C3, C4, C5) corresponding to different water accumulation zones and structural elements. This segmentation allows the complex rainstorm effect to be modeled as a sum of manageable capacitance components, improving measurement precision while maintaining model tractability
Solution Approach 2:
The patent introduces spatio-temporal dimensions to the leakage current awareness system by incorporating rainfall intensity, duration, and water accumulation depth as time-varying parameters. This dimensional expansion transforms static leakage current detection into dynamic risk assessment, significantly improving accuracy without excessive complexity increase
2Reliability
If multi-dimensional parasitic capacitance analysis model is established considering accumulated water and micro-terrain, then leakage current risk awareness accuracy is improved, but the calculation complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by assigning different capacitance characteristics to different locations within the photovoltaic system. Each parasitic capacitance component (C1-C5) represents a specific local water accumulation zone with unique electrical properties. This localized modeling approach improves reliability by capturing spatial variations in water influence while keeping each local model relatively simple
Solution Approach 2:
The patent creates a universal parasitic capacitance model that can be applied to various distributed photovoltaic system configurations. The five-capacitance framework (C1-C5) serves multiple functions: modeling water accumulation effects, accounting for micro-terrain variations, and predicting leakage current under different rainfall conditions. This multi-functionality improves reliability without proportionally increasing complexity
3Measurement precision
If spatio-temporal correlation analysis of rainstorms is performed, then the prediction of leakage current risk becomes more accurate, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-establishing the parasitic capacitance analysis model and its relationship with rainfall parameters before actual rainstorm events. The model structure, including all five capacitance components and their spatial relationships, is prepared in advance. This preliminary modeling reduces real-time processing time during actual security awareness operations while maintaining high prediction accuracy
Solution Approach 2:
The patent replaces complex mechanical or computational rainstorm monitoring systems with an electrical parameter-based model. Instead of directly measuring and analyzing spatio-temporal rainstorm patterns, the system substitutes these with equivalent electrical parameter changes (parasitic capacitance variations) that can be processed more efficiently. This substitution maintains prediction accuracy while reducing computational burden
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
The method provides comprehensive and accurate leakage current risk awareness for distributed power supply systems, accounting for spatio-temporal rainstorm distribution and micro-terrain conditions, enhancing safety by predicting electricity-related security risks and preventing electric shocks.
Implementation Method 1
deriving a multi-dimensional parallel parasitic capacitance analysis model of a distributed photovoltaic-energy storage power supply system (DPSPSS) considering accumulated water depth and micro-terrain environment
Data Source
AI summary
A method for electricity-related security awareness of distributed power supply systems considering spatio-temporal distribution of rainstorms, including: establishing a multi-dimensional parallel parasitic capacitance calculation model of the distributed photovoltaic-energy storage power supply system considering accumulated water depth and micro-terrain environment; performing multi-source spatio-temporal hierarchical correlation analysis between rainstorm spatio-temporal distribution characteristics (including rainfall peak position, cloud movement, rainfall intensity and rainfall duration) and an operating state of the distributed power supply system; constructing a leakage current probability prediction model considering unevenness and randomness of the rainstorm spatio-temporal distribution; and establishing an electricity-related security awareness model based on deep meta-learning.