Deep Learning Optimal Power Flow for Small Hydropower Grids
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Solution Overview
Problem
The operation of power grids in small hydropower areas is complex and challenging due to the non-linear and non-convex characteristics of optimal power flow models, leading to difficulties in calculating optimal power flow efficiently and accurately, which affects safe, economical, and high-quality grid operation.
Innovation Solution
An optimal power flow acquiring method based on deep learning is introduced, utilizing a convolutional neural network to predict generator active and reactive power by tracking steady-state behavior and integrating load data, node voltage amplitude, and phase angle, thereby improving calculation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional linearization method is used to simplify nonlinear power flow equation, then calculation complexity is reduced, but solution accuracy deteriorates
Solution Approach 1:
The patent replaces the conventional linearization mathematical approach with a deep learning neural network model. The neural network learns the complex nonlinear power flow relationships from training data and directly predicts optimal power flow solutions, substituting traditional iterative numerical methods with a trained intelligent model that maintains both efficiency and accuracy.
Solution Approach 2:
The patent transforms the power flow problem from solving nonlinear algebraic equations with fixed parameters to a predictive modeling problem where the neural network learns parameter mappings from historical operating data. The system changes from deterministic equation-solving to probabilistic prediction based on learned patterns in the data.
2Measurement precision
If traditional optimal power flow solver is used, then solution accuracy is maintained, but calculation efficiency deteriorates
Solution Approach 1:
The patent performs preliminary training of the neural network model using extensive historical power flow data and system operating data. This preliminary action creates a pre-trained intelligent model that can rapidly predict optimal power flow solutions without requiring repeated iterative calculations, thus improving real-time calculation efficiency while maintaining accuracy through the learned patterns.
Solution Approach 2:
The patent creates a computational copy of the power flow solution process through the neural network model. Instead of repeatedly solving the original complex nonlinear equations, the system uses the trained network as a simplified copy that replicates the essential solution behavior with much faster computation, trading detailed mathematical rigor for practical computational efficiency.
Data Source
AI summary
Disclosed is an optimal power flow acquiring method for regional distribution network of small hydropower groups based on deep learning, which specifically includes the following steps: generating required data sets by adopting continuous power flow and power flow equation calculation methods; the data set is randomly divided into training data (80 percent) and test data (20 percent); training the built convolutional neural network model with training data to learn the mapping relationship between load and generator output power; inputting test data, and directly obtaining PG and QG from the trained convolutional neural network; and solving residual variables Vi and θi with traditional power flow solver. The application can accelerate the solving speed of the optimal power flow problem with higher prediction accuracy.