Insulator Pollution Research Articles

Monitoring of insulator pollution discharge in overhead distribution network based on acoustic emission technology

Abstract During the continuous operation of the distribution network, dirt often accumulates on the surface of insulators. Once encountering a humid climate environment, it may cause accidental discharge, leading to the occurrence of flashover pollution accidents. In order to effectively monitor this issue, this study adopted a monitoring method based on acoustic emission technology. This article introduces the discharge process and acoustic emission phenomenon of insulators in overhead distribution networks under pollution conditions. By using emission signal detection devices, acoustic signals generated during insulator discharge are captured. And the wavelet denoising algorithm was used to process its signal, based on which the discharge characteristics of insulator pollution in the overhead distribution network were extracted. The emission amount of insulator pollution in the overhead distribution network was determined according to the set threshold, achieving monitoring of pollution emissions. The results indicate that the proposed method can accurately monitor the discharge of insulators in overhead distribution networks, provide scientific basis for timely maintenance measures, and improve the operational safety and reliability of distribution networks.

Performance of insulators under variation of pollution, inclined angle, and temperature based on the design of experiment

The environmental conditions significantly impact the performance of the transmission line insulators. The current work investigated some variables that influence the flashover and breakdown voltages, such as insulator pollution (P), insulator inclined angles with the cross-arm (A), and insulator temperature (T). A prediction equation can be developed using Box-Behnken Design (BBD) to relate the input variables (P, A, and T) and the flashover voltage (VFO). BBD is a rotatable second-order design requiring three levels of each variable, influencing a specific experiment response. A statistical analysis was carried out to identify the significant variables affecting voltage VFO. So, the main objective of the work is to investigate the effect of P, A, and T on the VFO and use Box Behnken Design (BBD) to predict the value of VFO when the information about P, A, and T is available without requiring any experimental works. The results illustrated a prediction equation that facilitates the prediction of the voltage VFO if new values of the input parameters are known. The prediction error of the constructed equation was less than 5 % for a total number of 21 new data samples used to test and verify the robustness of the prediction equation.

A probabilistic neural network assessment method for insulator pollution level based on discharge noise

The insulators of a transmission line are exposed to air for a long time, a mount of dust particles will be deposited on the surface. When there rain, fog or high humidity, the surface of the insulator will discharge or even flashover to breakdown. For this, in this paper, the characteristic parameters of insulator discharge noise under different pollution levels, ambient temperature and relative humidity are extracted, and the influence of insulator discharge noise are analyzed respectively. Probabilistic neural network (PNN) model training samples are used to propose an insulator pollution level assessment method based on discharge noise and verify it. The results show that the pollution level has obvious influence on the characteristic parameters of insulator discharge noise. The higher the pollution level, the more obvious the discharge phenomenon and the greater the discharge intensity. When the relative humidity ranges from 70 % to 90 %, the greater the relative humidity, the greater the pollution discharge intensity of the insulator. The energy of the noise signal gradually concentrated in the 5.5 kHz to 8.3 kHz frequency band, and the pollution discharge in the 5.5 kHz to 16.5 kHz frequency band reflected in the low-frequency part of the information increased. Ambient temperature has no obvious effect on insulator discharge noise. The precision rate of the assessment method for pollution levels Ⅰ, Ⅱ, Ⅲ and Ⅳ are 91.5 %, 88.2 %, 86.5 % and 94.0 %, respectively.

A Probabilistic Neural Network Assessment Method for Insulator Pollution Levels Based on Infrared Images

A Probabilistic Neural Network Assessment Method for Insulator Pollution Levels Based on Infrared Images

A Controllable Response Power Beam Localization Method and System Implementation

Sound source localization technology has gradually become one of the main methods for fault source target localization in dangerous and difficult-to-discover applications. In order to locate the target fault sound source more intuitively, accurately and in real time in practical applications, an audio-visual positioning system is developed. A 64-channel microphone array is designed, which is evenly distributed in a circular shape, and has a video acquisition target at the same time. The system uses GCC-PHAT to estimate the relative time delay between the signal source arriving at two microphones, and then uses SRP-PHAT algorithm to obtain weighted sum beamforming. A spatial grid search strategy based on spherical coordinate spatial contraction method is proposed. When the output power is maximum, the direction corresponding to the beam is regarded as the sound source direction. Using the same sound source signal, the proposed positioning method is compared with the direct Newton iterative method and the Cartesian coordinate scanning SRP-based maximum method. The simulation results show that the Cartesian coordinate value and spherical coordinate value of the positioning algorithm in this article are closer to the real coordinates of the sound source position than the other two methods, and the positioning operation time is the shortest. In addition, in the actual corona discharge power source positioning experiment, the system can accurately locate the insulator pollution discharge point, which is consistent with the infrared detection and positioning results and has good robustness. Therefore, the system location method can provide a new choice for sound source location applications.

Research on the prediction method of porcelain insulator pollution degree based on electric field monitoring

The online monitoring of insulator surface pollution states is crucial for predicting/preventing pollution flashover accidents. This paper conducts artificial pollution tests on porcelain insulators at five pollution levels to obtain the electric field signal during the flashover process. Subsequently, the time-frequency performance of the electric field signal waveform at various discharge stages is discussed in detail. Finally, a method for predicting pollution degrees by combining back propagation (BP) neural networks and non-contact electric field signal monitoring is proposed and validated. Research results indicate that during the pollution flashover process of the porcelain insulator, the peak value of the electric field signal shows an upward trend in sections. As the equivalent salt deposit density (ESDD) increases, the curves of electric field values rise. The development of dry-band arcing clearly distorts the electric field signal waveform at its crest and trough parts, increasing the total harmonic distortion (THD) rate. The THD can reach 100–130 % when closing to flashover, with the third harmonic acting as the main component. By taking kmax (the rising rate of the E-field signal amplitude), g (the ratio of Emax to Erms), K3 (the third harmonic coefficient), and THD as inputs to the BP neural network model, an ESDD prediction with a relative error lower than 10.63 % is realized. This research can offer novel technical support for predicting pollution degrees.

Novel criteria for silicone rubber insulators condition monitoring based on leakage current analysis: Considering asymmetric aging and pollution

Insulators' flashover due to pollution is the biggest challenge of power systems. This phenomenon will become more intense with the aging of the insulator. Precise and prompt determination of the severity of pollution and the insulator status in terms of aging is essential for preventing pollution flashovers. Therefor monitoring insulator leakage current is essential for preventing the pollution flashover phenomenon. This article proposes innovative criteria based on the insulator's leakage current analysis for the classification of aging and pollution severity. The leakage current signal of the virgin, fully aged, and asymmetric aged insulators have been examined under different pollution conditions. Based on the experimental results analysis, two new criteria have been proposed for assessing the silicon rubber insulator condition which have resulted from the first-order harmonic of leakage current (It1), the third and fifth-order harmonics ratio of leakage current (It3/5), and the cosine of first-order harmonic phase angle (PI). Analyzing the proposed criteria demonstrates that two proposed criteria have a strong correlation to aging and pollution, respectively. These two new criteria can be implemented to identify the early signs of pollution and aging of insulators. The ability of proposed criteria has been established based on random forest, support vector machine, decision tree algorithm, multi-layer perceptron, and decision-making level fusion in Python language. The obtained criteria based on experimental data in different conditions have been employed for classifier training which can be applied in industrial settings without the need for laborious data labeling, ensuring fast and accurate outcomes.

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

Modelling and simulation study on dynamic pollution accumulation process of composite insulator

AbstractWith the widespread application of composite insulators in transmission lines, exploring the accumulation mechanism of pollution particles on composite insulator surfaces is of importance to ensure the safe and steady operation of the power system. Addressing the current theoretical shortcomings, this study categorises the accumulation process of particles on the insulator surface into three stages, namely ‘spatial motion’, ‘surface collision’, and ‘surface motion’. The motion and rotation velocities in a multi‐physics field are calculated in the spatial motion stage. In the surface collision stage, a parameter called ‘neck height’ is introduced to determine the optimum mechanics theory, and the normal deposition criterion is established. For the surface motion stage, the sliding displacement and rolling displacement on the surface are calculated based on the rotation speed of the particles. A dynamic pollution accumulation model of the composite insulator is established based on the normal deposition criterion and tangential displacement. Finally, numerical simulations are performed by using the finite element method. Simulation results show that the proposed model agrees with the actual insulator pollution accumulation, and the deposition model is still applicable for various types of composite insulators operating in different applied voltages. The deposition probability of particles increases with the increasing particle size. In the surface motion stage, particle displacement increases with particle size and wind velocity.

Insulator pollution in transmission lines.

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Application of Hyperspectral Image Recognition Technology in Monitoring the Pollution Level of Insulators

Abstract The pivotal aspect of ensuring the secure and stable functioning of Electrical Power Systems (EPS) lies in the online monitoring of transmission line insulator pollution levels, aimed at forestalling pollution flashover incidents. These flashovers typically stem from the buildup of contaminants on insulator surfaces, which, under humid conditions, establish a conductive layer, gravely jeopardizing grid security. Traditional pollution detection approaches, inclusive of manual inspections and offline sampling analyses, suffer from drawbacks like inefficiency, inadequate real-time capabilities, and vulnerability to human intervention, thereby struggling to align with the demands of contemporary power grid intelligence and automation. In view of this, this article innovatively proposes an insulator pollution monitoring model based on hyperspectral image recognition technology. This model fully utilizes the unique spectral integration feature of hyperspectral images, which can provide rich spectral data while obtaining image information, covering a wide spectral range from visible light to infrared and even more, and has extremely high spectral resolution. The experimental results show that the model not only significantly improves the accuracy and real-time performance of pollution detection, but also overcomes many shortcomings of traditional methods.

Switching Impulse Flashover Performance of Nonuniform Pollution Insulators in Natural Environment

Under natural conditions, insulators show a nonuniform pollution state, which has a great influence on the switching impulse flashover voltage of insulators. Meanwhile, the insulation level of transmission lines is selected according to the switching impulse flashover voltage of nonuniform pollution insulators. For this purpose, in this article, the wet pollution method is used to simulate nonuniform pollution conditions. Then, the switching impulse flashover tests and the switching impulse flashover tests with preloading ac voltage are conducted on two types of insulators, XP-70 and LXY-70, in natural environment. The flashover voltage of insulators is obtained, flashover performances are analyzed, and the flashover processes of insulators are photographed. Moreover, the equations of flashover voltage gradient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{{50}}$ </tex-math></inline-formula> under nonuniform pollution conditions are established. The results indicate that there is a conspicuous polarity effect and a polarity reversal phenomenon under different conditions. Besides, as the increase of the ratio of bottom surface pollution to top surface pollution, the flashover voltage changes in the opposite case of preloading and non-preloading ac voltage. Finally, the switching impulse flashover voltage of ordinary porcelain and glass suspension insulators under nonuniform pollution conditions can be calculated based on the established equations of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{{50}}$ </tex-math></inline-formula> . The results can provide theoretical support for the reasonable selection of the insulation level of transmission lines, which in turn can greatly reduce the cost of the transmission lines.

Outdoor detection of the pollution degree of insulating materials based on hyperspectral model transfer

Accurate detection of the insulator pollution degree is the premise to realize pollution flashover warning. In the laboratory, the pollution degree can be accurately evaluated by using hyperspectral imaging technology (HSI). However, laboratory model is difficult to be applied to outdoor due to the influence of natural light. Therefore, this study attempts to combine HSI and model transfer to extend the laboratory model to outdoor application. Spectra of pollution samples were collected indoor and outdoor. Multivariate scattering correction and the Savitzky–Golay filter were adopted to eliminate spectral noise. Laboratory model was established based on back propagation (BP) neural network, the direct standardization algorithm was used to model transfer and its effect was verified by principal component analysis (PCA). After the model transfer, the accuracy of outdoor detection reached 82.5% in cloudy day and 80% in sunny day, which is significative for the field non-contact detection of insulator pollution degree.

Design and Implementation of Transmission Line Insulator Online Monitoring Platform Based on Image Analysis

The safe operation of insulators directly determines the safety level and reliability of the entire system. In this paper, digital image processing technology is used to analyze and process insulator images, and the performance of composite insulators is comprehensively evaluated from three aspects: degree of deterioration, surface pollution, and degree of water repellency, which is used as the basis for judging insulator detection. For this purpose, an insulator online monitoring system based on telemetry image analysis is specially designed to determine the degree of aging, surface pollution, and hydrophobicity of insulators and to conduct practical tests. At the same time, the system can realize the offline operation of the insulator live, avoid the danger of climbing and live trial operation, and ensure the personal safety of power maintenance personnel. The system hardware uses advanced integration technology and high-performance and reliable equipment. The software is developed using popular development tools, is simple to operate, and has important engineering value.

Space Electric Field Characteristics of Silicone Rubber Insulator Pollution Flashover and Its Application in Flashover Prewarning

Pollution flashover accidents are still happening in areas with severe environmental conditions, and the existing pollution flashover warning methods are still very simple. In this article, the space electric field characteristics of silicone rubber insulators during pollution flashover were revealed, and the application of the space electric field signal (SEFS) for pollution flashover prewarning was proposed for the first time. During the research, artificial pollution tests were conducted to obtain the SEFS waveform, and then its time-frequency characteristic parameters under different pollution flashover stages were extracted and analyzed. Research results indicate that, according to the SEFS amplitude variation, the development of pollution flashover can be divided into three zones: safe zone, dangerous zone, and critical zone. The four characteristic parameters, the rms increase factor, maximum value increase factor, peak factor, and third harmonic factor change obviously during different flashover stages. Combining the SEFS parameters and the pattern recognition algorithm, the recognition of pollution flashover zones is realized, which can be directly used to form the pollution flashover prewarning strategy.

Research on early warning method for pollution flashover based on phase angle of insulator leakage current

Pollution flashover is a systematic disaster of power system. It is significant to early warning insulator pollution flashover in real time and accurately. The phase angle of the insulator leakage current is a critical parameter for insulator pollution flashover early warning. However, in field applications, the operating voltage waveform cannot be obtained so that this characteristic parameter cannot be applied to the insulator pollution flashover early warning system. This paper proposes a method to obtain the phase angle of the leakage current of the online insulator. This method avoids the direct measurement of the operating voltage waveform, but uses the stray capacitance between the signal plate of the early warning device metal shell and the transmission line to obtain the operating voltage signal indirectly. In this paper, the method and the early warning mechanism of pollution flashover are analyzed theoretically, and then the early warning device for pollution flashover is designed. Finally, the effectiveness of the early warning device is verified by experiments.

Sensing Method Using Dielectric Loss Factor to Evaluate Surface Conditions on Polluted Porcelain Insulator

Insulator diagnostics is still a topical issue. No one has yet found out how to accurately determine the condition of all insulators and decide when replacement or maintenance is required. Insulators are one of the main components of a transmission and distribution system and must withstand high voltages in all weather conditions. Moisture and dirt are the main factors influencing the insulating properties of insulators. This article deals with the effect of pollution on a porcelain insulator. An Omicron MI 600 measuring system monitors the changes in the dielectric loss factor and leakage current in a wide frequency range (10 Hz to 1 kHz) to evaluate the contamination level. We applied three high voltage levels (5 kV, 7.5 kV, and 10 kV) to the porcelain insulator to monitor changes in the mentioned quantities with various frequencies. The measurement results confirmed the usability of the dielectric loss factor and leakage current for the diagnosis of insulator pollution. The dielectric loss factor showed more promising results than the leakage current.

Assessment of Insulator Pollution Degree Based on Contamination Moisture With Temperature Change

For the safe operation of the power grid, online monitoring of insulator pollution degree is of great significance to the power system. Considering that the insulator contamination mostly is accumulated in the dry season with little rain, it is very valuable to assess the pollution degree with the small leakage current signal measured at low humidity. To this end, this article analyzed the moisture characteristics of contaminated insulators when the insulator temperature is lower than the ambient temperature and found that the water absorption rate of the contamination layer is proportional to the “apparent humidity” at the interface. Then, an assessment method for insulator pollution degree based on contamination moisture at unsaturated humidity (RH < 100%) was proposed, which has application prospects in arid areas.

Study on Initial Discharge Process of Insulator Pollution Flashover Considering Quantum Transition

Insulator pollution flashover has a high probability in the faults of transmission and distribution system. Due to its complex formation process and various influencing factors, the relevant studies are mostly based on experimental tests and empirical formulas, which do not fully consider the impact of random factors on the flashover process. The quantifiable algorithms are lacking, and it is difficult to effectively predict pollution flashover. In this article, the quantum transition theory is introduced in the streamer theory to calculate the energy-level transition probability of the particle on the surface of polluted insulator. The randomness of particle collision cross section is considered, and the initial discharge voltage is calculated by probability theory. The effects of active particle content, ambient temperature, and relative humidity on the initial charge number of insulator pollution flashover are analyzed. On this basis, the initial discharge process of insulator pollution flashover is studied. The experimental results show that the calculated results of insulator pollution flashover initial discharge voltage based on quantum energy-level transition probability are consistent with the experimental values, which has engineering application value for insulator pollution flashover prediction.

Compilation of a Local Electrical Insulation Pollution Map in the Sheikh-Najar Industrial Region of Syria

A method for improving the performance reliability of electrical installations under the conditions of polluting the insulation of the 66 and 230 kV overhead lines (OHL) and outdoor switchgears (OSG) in the Sheikh-Najar industrial region near Aleppo, the economic capital of Syria, is considered. According to the relevant regulatory documents, the most efficient approach to selecting the insulation levels is the one based on compiling regional and local insulation pollution maps (IPMs) in the territory where the grid facilities are located. An electronic local IPM in the Sheikh-Najar industrial region was compiled using the characteristics of industrial pollution sources. The article presents the climatic characteristics of the Aleppo city area, the electrical circuit diagram and insulation characteristics of the existing 66 and 230 kV OHLs and OSGs, the characteristics of industrial pollution sources (in terms of produced goods types and amounts), estimated pollution degrees (PD) at various distances from the enterprise boundaries with a circular wind rose, and those adjusted for the real wind rose in the city of Aleppo, and the local IPM of the Sheikh-Najar region drawn using the AutoCAD software. It has been established that there are zones with insulation pollution degrees 2, 3 and 4 in the Sheikh-Najar industrial region territory. Based on the study results, recommendations have been elaborated for increasing the specific effective creepage distance of the OHL and OSG insulation by adding linear insulators to the strings, for choosing the optimal types of substation electrical equipment, and for taking preventive measures. To refine the boundaries of local IPM zones, it is recommended to study the features characterizing the pollution of electrical installations insulation caused by both industrial and natural factors, as well as to generalize the experience gained from the operation of electrical installations in the Sheikh-Najar industrial region.