Optimization sampling method of electric power digital power meter, power meter and equipment
By using neural network algorithms to construct a sampling prediction model in the power digital power meter, predicting the optimal sampling point position of the power signal, solving the problem of degradation of measurement accuracy caused by the fixation of sampling points in the prior art, and achieving more efficient power signal sampling and measurement.
CN119691636AActive Publication Date: 2025-03-25NORTHWEST BEARING CO LTD
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Patent Information
- Application Number
- CN202411652066.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-03-25
- Estimated Expiration
- 2044-11-19
AI Technical Summary
Technical Problem
The existing power digital power meter uses fixed sampling point positions during sampling, which cannot accurately capture the dynamic changes of the power signal, resulting in a decrease in measurement accuracy.
Method used
A neural network algorithm is used to build and train the sampling prediction model, and the optimal sampling point position in each sampling period is predicted by preprocessing and feature extraction of historical power signal data.
Benefits of technology
It significantly improves the measurement accuracy of the power meter, reduces the demand for high-cost hardware, reduces the complexity of subsequent signal processing, and improves overall efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of electric power data measurement, and discloses an optimization sampling method of an electric power digital power meter, the power meter and equipment, and the method comprises the steps: firstly constructing and training a sampling prediction model through employing a neural network algorithm, and enabling the model to predict the optimal sampling point position in each sampling period according to an electric power signal generated by a circuit; the method comprises the following steps: collecting historical power signal data, extracting signal features, preprocessing and standardizing, and then marking an optimal sampling point position to form a training data set; and selecting a long-short term memory (LSTM) network model for training, and evaluating the performance of the model by using the verification set. And after training is completed, model deployment is used for subsequent sampling position prediction. And collecting electric power signal data in a tested circuit in real time, extracting features and inputting the features into a prediction model, obtaining an optimal sampling point position list, and applying the optimal sampling point position list to a sampling strategy of a power meter.
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