High Frequency Current Transformer Sensors Research Articles

Locating Insulation Defects in HV Substations Using HFCT Sensors and AI Diagnostic Tools.

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown.

Novel Reference Method for the Characterization of PD Measuring Systems Using HFCT Sensors.

During their lifespan, high-voltage (HV) electrical systems are subjected to operating conditions in which electrical, mechanical, thermal and environmental-related stresses occur. These conditions over time lead to unforeseen failures caused by various types of defects. For this reason, there are several technologies for measuring and monitoring the electrical systems, with the aim of minimizing the number of faults. The early detection of defects, preferably in their incipient state, will enable the necessary corrective actions to be taken in order to avoid unforeseen failures. These failures generally lead to human risks and material damage, lack of power supply and significant economic losses. An efficient maintenance technique for the early detection of defects consists of the supervision of the dielectrics status in the installations by means of on-line partial discharge (PD) measurement. Nowadays, there are numerous systems in the market for the measurement of PD in HV installations. The most efficient with a reasonable cost will be those that offer greater security guarantees and the best positioned in the market. Currently, technology developers and users of PD measuring systems face difficulties related to the lack of reference procedures for their complete characterization and to the technical and economic drawback of performing the characterization tests on site or in laboratory installations. To deal with the previous difficulties, in this paper a novel method for the complete and standardized characterization of PD measuring systems is presented. The applicability of this method is mainly adapted for the characterization of systems operating in on-line applications using high-frequency current transformer (HFCT) sensors. For the appropriate application of the method, an associated and necessary scale modular test platform is used. In the test platform, the real on-site measuring conditions of an HV insulated distribution line are simulated in a controlled way. Practical characterizations, showing the convenience and advantages of applying the method using the modular test platform, are also presented.

Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors.

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system's functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems.

AI기법과 고주파 전류 변환센서를 활용한 배전반 on board 진단시스템

This paper presents an on-board switchboard diagnosis system based on the Ai algorithm for the purpose of establishing a real-time partial discharge monitoring diagnosis system. For classifying pattern of partial discharge, FCM-based RBFNN is applied to on-board diagnosis system and K-means, NN(Neural Networks) are used for comparative analysis. The data are obtained by HFCT(High Frequency Current Transformer) sensor from designed partial discharge simulation environment and its phase is divided into 128 degree. Commonly used partial discharge data analysis methods such as statistical analysis, PRPS(Phase Resolved Pulse Sequence) and PRPD(Phase Resolved Partial Discharge) are introduced. In this paper PRPDA(Phase Resolved Partial Discharge Analysis) and PCA(Principal Component Analysis) are used as a pre-processing. The Ai algorithm comparison is performed twice in total. Firstly, a comparison analysis of the test data validation of each model trained in the Python environment is conducted through a confusion matrix. Afterwards, an on-board diagnostic device is added to the partial discharge simulation circuit, and the judgment results for the actual operation are compared. Through comparative analysis in virtual and real environments, it is confirmed that the case in which FCM RBFNN is mounted shows excellent performance.

The Investigation of Detect Position of Partial Discharge in Cast-Resin Transformer Using High-Frequency Current Transformer Sensor and Acoustic Emission Sensor

The lifetime of a cast-resin transformer mainly depends on the condition of insulation material. Partial discharge (PD) is an important reason for insulation deterioration in cast-resin transformers. Identifying the position of PD is very necessary for damage assessment while the transformer is still operating, and the transformer is covered by housing. This paper proposes the investigation of a cast-resin transformer using an AE sensor and HFCT sensor to specify the precise source of PD. In this study, four AE sensors were used to find PD sources, and the high-frequency current transducer (HFCT) technique was used to identify the PD source and the criteria level. The experiment, in the first two parts, identified the possibility of PD, which includes the position of PD. The final part of the experiment verified the position of the PD source of a cast-resin transformer and confirmed the inspection results. AE and HFCT sensors can be used to detect the location of PD sources, confirming the position of the PD source by sensor detection. In addition, the evident partial discharge picture on the insulator surface of high voltage side. The process successfully and accurately identifies and locates the PD source.

Current sensor optimization based on simulated transfer function under partial discharge pulses

The time measurement efficiency of the partial discharge (PD) relies on the signal-to-noise ratio (SNR) and gain of the high-frequency current transformer (HFCT) sensor. However, the PD's time measurement efficiency decreases with the noise coupled to the HFCT in onsite measurements. To overcome that setback, this paper proposes one pre-processing, through modelling and simulation, considering the physical effects, features of the electrical circuit and coil construction parameters of the HFCT. The main goal is to reach reasonable high SNR under the strong influence of background noises. This investigation aims to validate the hypothesis of improvement or deterioration of the HFCT signal response through a transfer function optimization. This research effort's contributions are threefold: 1. Generation of PD pulse signal and noise addition; 2. HFCT modelling, simulation, and frequency response analysis; and 3. Models performance evaluation and validation of hypothesis. In conclusion, the pre-processing approach stands out as a means to robustify and provide freedom to the electric utility, making up for an eventual need to redefine the physical and geometrical parameters of the HFCT sensor under specific background noise for maintenance tests purpose. According to a cyber-physical system framework, experiments corroborate the project's goals to contribute to the PD pattern monitoring in onsite measurements and incorporate robustness to signals with low SNRs.

Principles of Charge Estimation Methods Using High-Frequency Current Transformer Sensors in Partial Discharge Measurements.

This paper describes a simplified model and a generic model of high-frequency current transformer (HFCT) sensors. By analyzing the models, a universal charge estimation method based on the double time integral of the measured voltage is inferred. The method is demonstrated to be valid irrespective of HFCT sensor, assuming that its transfer function can be modelled as a combination of real zeros and poles. This paper describes the mathematical foundation of the method and its particularities when applied to measure nanosecond current pulses. In practice, the applicability of the method is subjected to the characteristics and frequency response of the sensor and the current pulse duration. Therefore, a proposal to use the double time integral or the simple time integral of the measured voltage is described depending upon the sensor response. The procedures used to obtain the respective calibration constants based on the frequency response of the HFCT sensors are explained. Two examples, one using a HFCT sensor with a broadband flat frequency response and another using a HFCT sensor with a non-flat frequency response, are presented.

Novel Core Airgap Profiles Design Scheme for Winding and Thermal Loss Reduction in High-Frequency Current Transformer Sensors

Novel Core Airgap Profiles Design Scheme for Winding and Thermal Loss Reduction in High-Frequency Current Transformer Sensors

From the editors' desk

The first article in this issue is "Special Requirements of High Frequency Current Transformers in the On-Line Detection of Partial Discharges in Power Cables," by Yang Xu, Xiao Gu, Bin Liu, and Baojun Hui of the State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, China; Zhigang Ren of the Beijing Electric Power Research Institute, Beijing, China; and Shaoxin Meng of the China Electrical Power Research Institute, Wuhan, China. The article discusses some special requirements of high frequency current transformers and the instrumentation in the on-line detection of partial discharge (PD) in cables. Four types of defects representing typical situations in the field were created in a 110-kV cable and joint for PD detection. Based on the frequency analysis of the PD signals, the upper frequency of a measuring instrument needs to be higher than 40 MHz and the lower frequency no less than 500 kHz. The lower frequency limit is recommended to avoid power line interference. Provided that the high frequency current transformer satisfies the bandwidth requirement, a high transfer impedance is preferred. A sensitivity better than 100 pC is expected in on-site PD detection and in high noise environments. The article compares eight PD measuring instruments based on high frequency current transformer detection with respect to lower and upper limit frequencies, sensitivity, PD pattern recognition functions, and PD signal attenuation. Among the existing functions, the PD pattern recognition function needs to be improved for on-line cable tests. In addition, the article concludes that there still is a need for an on-line calibration method based on high frequency current transformer sensors.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).