Insulator Detection Research Articles

The Application of Surface Potential Test on Hand-making Insulation for Generator Stator End-winding

This paper presents the advantage of surface potential test on hand-making insulation for generator stator end-winding insulation detection, compared with DC or AC withstand voltage test, also details the test principle, connection method and test notes. And through the case, surface potential test on hand-making insulation proved effective for insulation quality detection after generator stator end-winding maintenance, and the experimental data is useful and reliable for the electrical equipment operation and maintenance in the power plant.

Research and Application of Insulator Detection Method Based on Information Fusion of Inspection Characteristic Quantity

The insulator image recognition is an important prerequisite to achieve its operational state monitoring and fault diagnosis. Based on the complex features of the aerial image background, this paper proposes a kind of insulator detection method based on the information fusion of image characteristic quantity. The detection method improves the traditional Canny edge detection method and Hough transformation image recognition algorithm in order to give a digitized description of its characteristics and achieve automatic recognition of the insulator image. The experimental results show that, the proposed segmentation method can distinguish the target and background, with complete and continuous edge, less voids in the target, high positioning accuracy and rapid speed. This method can lay a foundation for the insulator state monitoring and fault diagnosis without human intervention.

Study on Insulator Deterioration Mechanism of ±800kV Transmission Lines and Live Detection Method of Faulty Insulator

According to the deterioration characteristic of insulators, leakage current changes in direct proportion to voltage distribution of insulators, however, presence of fault insulator directly affects the insulating property of transmission lines, thus a more accurate, economic and intelligent fault insulator detecting method needs to be studied. This paper designs a detection robot for live detection of faulty insulators of ±800kV ultra-high voltage direct current transmission lines. The detection approach of the device includes shorting out two insulators, collecting detection current generated by a 2500kV power supply of a detection module and leakage current of an insulator string, achieving precise data through an improved algorithm to ensure accuracy of detection results, and developing a prototype to perform simulated experiments. In accordance with experimental data, error of insulation resistance of an insulator detected by the device is within 5% so that whether an insulator of a line in normal operation is a low resistance insulator or zero resistance insulator can be effectively determined, accordingly quality status of the insulator can be judged to achieve the aim of experiments. The equipment is highly practicable and has high practical value in engineering.

Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images

Insulator detection using an infrared image is challenged by variance of temperature, orientations, and a cluttered background. A robust and discriminative representation of insulators in electric power systems is needed. This paper proposes a novel method for generating this type of representation in infrared images by taking advantage of high-level discriminative Convolutional Neural Networks (CNNs) to feature the extraction framework and the deformation invariant nature of the Vector of Locally Aggregated Descriptors (VLAD) aggregator. Different from existing methods, we delve deep into the convolutional feature maps. We first extract deep activation maps from convolutional layers of a pretrained deep model and replace the last three fully-connected layers with a VLAD pooling layer to generate the representation of an insulator. Then, we train a Support Vector Machine (SVM) for binary classification. To further verify the effectiveness and robustness of our proposed feature, an insulator detection pipeline based on an object proposal is introduced. The experimental results show that our proposed method can achieve an accuracy of 93%. Meanwhile, the detection results demonstrate that our insulator detection pipeline has satisfied performance goals.

Fault detection of insulator based on saliency and adaptive morphology

We study the problem of glass insulator fault detection from image in this work. It is a challenging task as no reliable electromagnetism cues are available. Since the characteristics of insulator fault are not clear and the positions of the insulator fault are uncertain. Previous efforts have been focusing on insulator classification and the insulator location. Recently, there is mounting evidence that saliency detection are setting new records for various vision applications. On the other hand, considering the particularly structure of insulator, fault detection can be naturally enhanced by morphology problem. Therefore, we in this paper present a saliency and adaptive morphological based insulator fault detection algorithm, aiming to jointly explore the capacity of saliency and morphology. Specifically, we propose an adaptive learning scheme which learns the saliency and morphology in a unified adaptive framework. According to the experiment, we can process most of the circumstances with 92 % accuracy in 0.5 second on average, which suits the Unmanned Aerial Vehicle (UAV) patrol device well.

Optimal Design of a Portable Arc-Shaped NMR Sensor and Its Application in the Aging-Level Detection of Silicone Rubber Insulator

Silicone rubber insulators (SRIs) are widely used in power systems because of their excellent hydrophobicity, high mechanical strength, and light weight; however, detecting SRI degradation remains problematic. Many studies have focused on SRI detection, but reports on rod-sheath detection are limited. We designed a novel nuclear magnetic resonance sensor that could be used to detect the sheath of arc-shaped SRI rods. First, the magnetic structure that generates the main magnetic field (B 0) was designed and optimized, and B 0 was designed purposely for an arc-shaped distribution that can match the sheath of SRI rod well. Second, an arc-shaped spiral radio-frequency coil was designed and optimized. Third, three SRIs serving on a 220-kV power transmission line for different time points were measured using this sensor. The transverse-relaxation decay curves were obtained using the Carr–Purcell–Meiboom–Gill (CPMG) sequence. Inverse Laplace transformation was then used to obtain the T 2 distribution from the CPMG decay curves. T 2 distribution demonstrated that the long component of T 2 decreased with increased serving time. Therefore, the T 2,long mean obtained from the T 2 distribution can be used as an index to reflect the aging level of the sheath of SRI rod.

Simulation and detection of photonic Chern insulators in a one-dimensional circuit-QED lattice

We introduce a simple method to realize and detect photonic topological Chern insulators with one-dimensional circiut quantum electrodynamics arrays. By periodically modulating the couplings of the array, we show that this one-dimensional model can be mapped into a two-dimensional Chern insulator model. In addition to allowing the study of photonic Chern insulators, this approach also provides a natural platform to realise experimentally Laughlin's pumping argument. Based on scattering theory of topological insulators and input-output formalism, we show that the photonic edge state can be probed directly and the topological invariant can be detected from the winding number of the reflection coefficient phase.

A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

The detection of targets with complex backgrounds in aerial images is a challenging task. In this letter, we propose a robust insulator detection algorithm based on local features and spatial orders for aerial images. First, we detect local features and introduce a multiscale and multifeature descriptor to represent the local features. Then, we get several spatial orders features by training these local features, it improves the robustness of the algorithm. Finally, through a coarse-to-fine matching strategy, we eliminate background noise and determine the region of insulators. We test our method on a diverse aerial image set. The experimental results demonstrate the precision and robustness of our detection method, and indicate the possible use of our method in practical applications.

Direct probe of topological order for cold atoms

Cold-atom experiments in optical lattices offer a versatile platform to realize various topological quantum phases. A key challenge in those experiments is to unambiguously probe the topological order. We propose a method to directly measure the characteristic topological invariants (order) based on the time-of-flight imaging of cold atoms. The method is generally applicable to detection of topological band insulators in one, two, or three dimensions characterized by integer topological invariants. Using detection of the Chern number for the 2D anomalous quantum Hall states and the Chern-Simons term for the 3D chiral topological insulators as examples, we show that the proposed detection method is practical, robust to typical experimental imperfections such as limited imaging resolution, inhomogeneous trapping potential, and disorder in the system.

Research on Detection of Transmission Line Porcelain Insulator Based on Infrared Technology

With the continuous development of infrared technology, infrared-based detection and diagnosis technology has become a new modern power equipment detection technology. Based on infrared technology, this paper proposed detection and diagnosis method of the transmission line porcelain insulators, including analysis on detection theory of IR imaging sensor, and infrared imaging map introduction. Through establishing porcelain insulator circuit calculation model, computing heating power of trouble insulators, this paper minutely analyzed the heating causes of trouble insulators. The paper provided a reference to the detection of transmission line porcelain insulators, which made it an important measure to ensure the safety and economic operation of electrical equipment.

Transmission Line Insulators Detection Based on KAZE Algorithm

Transmission Line obstacle detection technology is one of the hot spots of computer vision and transmission line inspection robot system.As the most common obstacle of inspection robot,correctly identify of insulators can help improve the reliability and accuracy of self-inspection. Low robustness of traditional detection algorithm can not meet the requirements on insulators detection when dealing with complex environment. To solve the above problem, a transmission line insulators detection method based on KAZE algorithm is given. The feature extraction is done in the nonlinear scale space, and feature vectors are formed with M-SURF algorithm, what’s more, feature vectors detecting is worked through the nearest neighbor algorithm as matching criteria and RANSC generated algorithm. Experimental results show that the effect of insulators detection algorithm based on KAZE detect faster and also make a good performance on accuracy and robustness.

Development of a Portable XLPE Cable Insulation Detection Device

In this article, a portable XLPE cable insulation detection device is introduced. Such a device utilizes electromagnetic coupling, UHF electromagnetic wave and acoustic emission to detect partial discharge signals in power cables. By analyzing the partial discharge signals and cable temperatures, the insulation status of XLPE power cables is judged.

Potential and Electric Field Distribution of 220kV Insulator String with a Faulty Insulator under Windage Yaw Condition

Faulty insulators could appear in the HV transmission line insulator string under the comprehensive effect of electrical, mechanical and environmental factors and they can be detected according to the space potential and electric field distribution variation characteristics around the insulator string. Finite Element Method (FEM) was used to study the potential and electric field distributions of a 220kV suspension insulator string contained a zero-value insulator in windage condition, comparing with a fine insulator string. The results show that the variation of the space potential and electric field distributions of insulator string is the same as that under no windage condition. The curve of synthetic electric field along the central axis around the good insulator string is U-shape. The 10th and 11th insulators from the high-voltage end are the sensitive insulators where the distortion ratio of synthetic field strength is higher than 3%, when a faulty insulator is in the string. This result can provide preferences for the online detection of faulty insulators.

Research on Insulator Detection by Nuclear Magnetic Resonance

Long-term used insulator for power transmission may suffer heavy aging problems for outdoor environment, such as rain corrosion. For Si-Rubber material, the chemical attributes under bad dielectric condition are good indicators for on-line equipment safety alert. In this work, both principle and experiment research were undertaken for the T2 measurements by NMR method. The problems of un-quantitative for other convenient methods had been solved. Most importantly, the chemical reasons for insulator aging has been analyzed for the first time. The results show that the relaxation time would decrease sensitively as the aging increases. In future NMR data scaling will be accomplished by virtue of DC impedance measurements.

Electric Insulation Detection Method for High-voltage Insulators

The principle of partial discharge detection is that through partial bridged discharge under high voltage electric field, it detects the inner air-filled cavity of high-voltage insulators. And it is a nondestructive detection method based on discharge magnitude to judge the insulation quality. The detecting system that adopts the partial discharge detection is more rigorous than testing system for electricity products, which must have small discharge capacity and higher sensitivity. This paper describes the principles of partial discharge detection and analysis insulation detection. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2862 Full Text: PDF

Summary of Study on the GIS Partial Discharge Detection Technology and SF<sub>6</sub> Alternative Gas

Gas insulated switchgears occupy an important position in the power system. But the partial discharge would occur because of insulation defect, PD will lead to the GIS insulation damage after a long time, and will lead to serious consequences without treatment in time, therefore, the PD detection is particularly important. PD is detected by its secondary physical phenomena, including the phenomena of sound, light, electromagnetic waves and gas decomposition products and so on. At present, many scholars in China and foreign countries have done a comprehensive study on insulation detection technology of GIS and have achieved a large number of results. The author do a detailed overview on the detection technical of GIS insulation state, including GIS platform and the simulation of typical insulation defectspartial discharge detection methods, SF6 alternative gas and pattern recognition. These works provide a valuable reference for follow-up study of GIS.

Design of Automatic Detection System for Temperature Sensor Based on LabVIEW

The examination of temperature sensor is a complicated process. Generally speaking that the examination is done by manual. But the manual detection has its disadvantages, such as connection trifles, inefficient and so on. In order to solve above disadvantages, this paper introduces a design of automatic detection system for temperature sensor based on virtual instrument. It elaborates the choice of hardware and the design of software. The system is mainly aimed at temperature sensor for resistance and insulation detection. Then we can determine whether the main parameters of temperature sensor are up to standard or not. The system realizes the automation of detection process. In the practical application, it has achieved better effect. In addition, this system can be used by other sensor detection when it has been part of the modification. So it has wide application range and higher practicability.

Stress Analysis and Detection of Composite Electrical Insulators with Embedded Fiber Bragg Grating Sensors

Stress Analysis and Detection of Composite Electrical Insulators with Embedded Fiber Bragg Grating Sensors

A Study on the Acoustic Fault Detection System of Insulators from Their Radiation Noises

To detect the insulator in the fault state on the electric poles, we first measured radiation sounds from normal state insulators and error state insulators in the anechoic chamber. We processed the signals in frequency domain to find the features with filter bank, narrow band and wide band analysis. So we could found two apparent results from their frequency spectrums - one was 120Hz harmonic components, the other was high average noise level than normal state ones. Then we also introduced a technique for the direction detection of the fault state insulator using the cross correlation from the three dimensional array microphones. To eliminate the noise signal from unexpected directions, we suggested the zero padding technique in cross correlation function. From these, we could conclude that acoustic fault detection techniques are useful of the detection of insulators' faults and the estimation of the direction of the fault state insulators.

New method of insulation detection for electrical vehicle

New method of insulation detection for electrical vehicle