Haiwen Yuan

Nonintrusive Pressure Measurement With Capacitance Method Based on FLANN

Nonintrusive pressure measurement has the crucial role to failure diagnosis and detection of hydraulic system where the conventional measurement instruments fail to make the necessary pressure readings within the sealed pipelines. The relationship between hydraulic oils dielectric constant, temperature, and pressure was studied, and the feasibility of pressure measurement with capacitance method was demonstrated. In order to make the capacitance method be suit for the hydraulic system where pipelines enhancement layer was made of tinsel, a capacitive transducer device (CTD) was designed. The functional link artificial neural network (FLANN) has been applied for analyzing the input-output characteristics of the CTD. Also, the FLANN-based modeling of the CTD was built. To verify the validity of the proposed method, an experimental platform was developed. The results indicate that the capacitance method is feasible for nonintrusive pressure measurement.

Noise Suppression of Corona Current Measurement From HVdc Transmission Lines

The complexity and diversity of noise in corona current measurements of high-voltage direct current (HVdc) transmission lines leads to challenges in estimating corona performance. This paper proposes an offline noise suppression method for HVdc corona current measurements. Given that the corona current and background noise processes coexist within the same frequency band, we develop a novel multifaceted filtering approach for HVdc corona currents. Specifically, a cross correlation function-based center frequency recognition method is presented to provide filter design specification for a multiple notch filter to suppress narrow-band radio interference. To then suppress the residual full-band noise, wavelet denoising techniques with hard and soft thresholds are applied and compared. The proposed noise suppression method is applied to field-measured corona current data from the HVdc Experimental Base in China. We assert that the noise suppression method has versatile implementation and can be readily applied for corona loss estimation of HVdc systems.

Development and Application of High-Frequency Sensor for Corona Current Measurement under Ultra High-Voltage Direct-Current Environment

Corona current measurement is essential for research into corona discharge under ultra high-voltage direct-current (HVDC) environment. The sensor is extremely important due to the small (microampere) amplitude of the corona current and the wide (dc to tens of megahertz) range in frequency. In this paper, a high-frequency sensor is described, and its application is introduced for accurate measurement of corona current under HVDC environment. Based on the finite-element method and the charge simulation method, the structure of the sensor is determined considering a ±1200-kV environment. The sensor is immune to the effects from the external environments such as vibration and temperature changes. Results indicate that the sensor is feasible for corona current measurement.

Finite Element Analysis and Design on Calibrating Device of Rotary Electric Field Sensor

The accurate measured value of the electric field strength under the UHVDC (Ultra High Voltage Direct Current) transmission line is one of the important indexes for assessing whether the UHVDC is laid properly. The accurately calibrated electric field sensor is the premiss for correctly measuring electric filed strength. Based on the finite element method, this article presents the finite element analysis on the calibrating device of the electric field strength sensor. Based on the results of the finite element analysis, this article also presents the optimal design on the calibrating device.

A fault detection scheme for a class of distributed discrete-time non-linear systems with abrupt and incipient faults

In this note, a consensus fault detection architecture for non-linear dynamical systems is investigated in discrete-time framework. Here, the discussed faults include abrupt and incipient faults. Moreover, the unstructured modelling uncertainty is taken into account in a large-scale system, which can be modelled as a set of subsystems, and the physical interaction between subsystems is described by non-linear functions, which can be learned on-line by neural networks. A network of local fault detectors (LFDs) is built so that each LFD monitors a single subsystem. For some overlapping components of subsystems, a co-operative estimator (CE) is embedded to avoid the consensus-less or non-co-operative case with respect to the LFDs. With the help of the designed CE, LFDs can be allowed to decide collectively on the presence of faults and the capability of detecting faults affecting overlapping variables may be improved by receiving more local diagnostic information. In addition, the derivation of rigorous analytical results for detectability properties is provided. Simulation results are given to show the effectiveness of the proposed scheme.

Automatic Calibration of Field Mill Using Virtual Instrument

Field mill instruments are widely employed for the measurement of atmospheric electric fields, dc voltages, and electric fields around electric transmission line. The key point of an electric field sensor is calibration. There is some shortcoming in traditional manual calibration. A set of automatic calibration system is simulated and designed herein based on the virtual instrument technology and applied to improve the calibration efficiency and accuracy of the field mill.