微型电流互感器计量绕组误差智能检测OA

[Objective]Miniature current transformers are a kind of device adopted for current measurement,and their core components are composed of a primary winding,a secondary winding,and a magnetic circuit system.The primary winding is directly connected in series with the measured current circuit,mainly undertaking the task of inducing the magnetic field of the measured current,while the secondary winding is connected to measuring instruments or protective devices,and is employed to output a signal proportional to the primary current.The magnetic circuit system is composed of high-performance magnetic materials,such as high-permeability ferrites or nanocrystalline alloys,which have excellent magnetic properties and can effectively guide and concentrate magnetic fields,ensuring that the transformer can maintain stable performance in complex electromagnetic environments.However,in practical applications,due to the significant nonlinear characteristics of the excitation winding of the miniature current transformer in the saturation region,significant errors will be generated in exciting voltage calculation in conventional linear modeling methods,which seriously restricts the measurement accuracy and stability of the transformer in high-requirement application scenarios such as smart grids.An intelligent detection method for measuring winding errors of miniature current transformers was proposed to improve the measurement accuracy of miniature current transformers and overcome existing technological bottlenecks.[Methods]In response to the nonlinear saturation characteristics of the excitation winding of miniature current transformers,a segmented linearization modeling method was developed to construct an equivalent circuit of the miniature current transformer for acquiring real-time signals of the transformer under the operation status.On this basis,the problem of insufficient applicability of linear models in the saturation region was solved and more accurate data support was provided for subsequent error analysis.A hybrid filtering algorithm combining the Sine Tapers window function and discrete wavelet transform was designed to conduct filtering processing on the acquired signals.Additionally,the Wiener filter and wavelet threshold denoising technology were combined to improve the signal-to-noise ratio,achieve precise separation of high-frequency noise and effective signals,and enhance signal quality.Meanwhile,correlation analysis was conducted on the filtered data,the principal component subspace was extracted via singular value decomposition,and statistical measures were constructed in the residual subspace.Meanwhile,the principal component analysis method was adopted to decompose the signal into the principal component subspace and residual subspace,with statistical measures and contribution rate calculations performed to achieve quantitative detection and accurate positioning of errors.Additionally,expected value operation was introduced to compensate for temperature drift,with fast transient response achieved by error fluctuation modeling,and real-time monitoring and intelligent detection of measurement winding errors realized via combining statistical changes.[Results]The experimental results show that the error detection method for miniature current transformers based on multi-spectral adaptive wavelet filtering and principal component space decomposition proposed in this paper has significant advantages over the traditional methods.Its signal acquisition results have a higher degree of agreement with the voltage current characteristic curve,and show extremely high accuracy in ratio and angle difference detection.[Conclusions]By deeply integrating multidisciplinary technologies,the key technical difficulties in error detection of miniature current transformers are solved,which can achieve highly accurate detection and fast positioning of measurement winding errors of miniature current transformers and improve the measurement stability and safety of power system operation.