电容器的电容量辨识方法及相关设备
By constructing a pre-charge model of the capacitor and using the stochastic Newton recursive algorithm for parameter estimation, the problems of noise and high sampling frequency in capacitor capacitance estimation are solved, achieving accurate capacitance identification at low sampling frequency and reducing equipment costs.
CN115169114BActive Publication Date: 2026-07-17CENT SOUTH UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CENT SOUTH UNIV
- Filing Date
- 2022-07-06
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing capacitor capacitance estimation methods are limited by noise and high sampling frequency, especially in high voltage environments, making accurate estimation difficult and resulting in inaccurate capacitor lifetime predictions, which increases economic costs.
Method used
A pre-charge model of the capacitor is adopted, and the parameters are estimated by the stochastic Newton recursive algorithm to construct a discrete model, reduce the influence of noise, achieve accurate identification of capacitance, and avoid the need for high sampling frequency.
Benefits of technology
It achieves accurate identification of capacitance at low sampling frequencies, reduces equipment costs, improves robustness, and is suitable for capacitance monitoring of DC-supported capacitors.
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Abstract
本申请提供了一种电容器的电容量辨识方法及相关设备，其中，所述方法包括：构建电容器的预充电模型；对所述预充电模型进行形式变换，得到离散模型；基于随机牛顿递推算法对所述离散模型进行参数估计，得到估计结果；根据采样值、所述估计结果以及所述离散模型计算得到所述电容器的电容量辨识结果，其中，所述采样值为预先采样的所述电容器的电参数值。本申请提供的方法无需基于纹波分量进行电容量的辨识，需要的采样频率较低，可以有效降低测量噪声与信噪比对辨识结果的影响，在传感器出现偏移故障时辨识结果也能具有较好的鲁棒性，实现了对电容器电容量的准确辨识。
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