Voltage measurement is an important part of power system operation, and non-intrusive voltage sensors have the advantages of low insulation difficulty, simple structure, easy loading and unloading, and high construction safety, which have become a new direction for voltage measurement. Based on the principle of electric field coupling, this paper constructs a non-intrusive floating ground three-capacitance voltage measurement model, which can complete the accurate measurement of voltage without connecting with the line to be measured and the earth in the measurement process. In non-intrusive voltage measurement, the change of the object to be measured or the measurement environment will cause the change of the coupling capacitance, which leads to the uncertainty of the transmission relationship of the sensor and the large error of measurement results. In order to solve this problem, a new method of sensor calibration is proposed in this paper. By sampling capacitance in parallel between two electrodes of the sensor, changing the capacitance value, and establishing an input output equation, the coupling capacitance value and the voltage value to be measured under different operating conditions are solved. In addition, the sampling capacitance is often several orders of magnitude larger than the sensor’s own capacitance, making the sensor’s voltage division ratio significantly higher and more conducive to the measurement of high voltages. The experimental results show that the measurement error is less than 2%, which verifies the feasibility of the method and the accuracy of the voltage measurement.
Noncontact voltage measurement has the advantages of simple handling, high construction safety, and not being affected by line insulation. However, in practical measurement of noncontact voltage, sensor gain is affected by wire diameter, wire insulation material, and relative position deviation. At the same time, it is also subject to interference from interphase or peripheral coupling electric fields. This paper proposes a noncontact voltage measurement self-calibration method based on dynamic capacitance, which realizes self-calibration of sensor gain through unknown line voltage to be measured. Firstly, the basic principle of the self-calibration method for noncontact voltage measurement based on dynamic capacitance is introduced. Subsequently, the sensor model and parameters were optimized through error analysis and simulation research. Based on this, a sensor prototype and remote dynamic capacitance control unit that can shield against interference are developed. Finally, the accuracy test, anti-interference ability test, and line adaptability test of the sensor prototype were conducted. The accuracy test showed that the maximum relative error of voltage amplitude was 0.89%, and the phase relative error was 1.57%. The anti-interference ability test showed that the error offset was 0.25% when there were interference sources. The line adaptability test shows that the maximum relative error in testing different types of lines is 1.01%.
At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO.
The responses of neural networks for uniform and normal distribution are studied, especially the BP and RBF neural networks and the question of combination between neural networks and fuzzy logical is answered by experiments. Linear relationship among sample feature components which impact the time consumption and convergence accuracy of networks has been discussed also. In the condition of feature vector included original bands and good separating degree components, BP and RBF neural networks combined with Fuzzy Reasoning have been used for TM image classification. Overall classification accuracy and Kappa coefficients reached 0.915 and 94.33% in RBF network which is higher than 0.845 and 89.67% in BP network.
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