Different methods have been proposed to monitor the conditions of On-Load Tap Changer (OLTC) which is one of the most important components of power transformers. Among the different faults of OLTC, the contact wear fault is the common one. To improve the condition understanding and maintain the health of OLTC, an identification method for on-load tap changer contact wear faults based on the neural network response surface model is proposed. Firstly, a fault simulation model of the on-load tap changer is established using the finite element method. Then, based on simulation tests and uniform experimental design, training samples for the response surface model are generated. The neural network response surface model is constructed via the sample training. Finally, a multi-objective optimization algorithm is employed to identify contact wear faults. The identification results of UCL-type on-load tap changer contact wear faults have been verified through the simulations. The study indicates that the neural network response surface model can effectively identify the contact wear faults. The maximum relative error between the identification results and the reference values is 0.77%, confirming the effectiveness of this method.

At present, the power supply reliability of high-voltage cable lines is a technical hotspot and difficulty that is closely watched in the world. The effect of cable thermal field is an important factor causing defects and failures, so a comprehensive analysis of it is an important part of monitoring and diagnosis. In this paper, the cable thermal field is firstly modeled through theoretical analysis. Secondly, the calculation theory and influencing factors of cable ampacity are analyzed, and then its calculation formulas under various working conditions are given. Finally, a simulation model is established to verify the theory. The non-contact online diagnosis of cable core temperature is realized, which is helpful for early detection of early hidden dangers of cables and comprehensively improves the power supply reliability of the power grid.

At present, the reliability of power supply of high-voltage cables is a technical hotspot and difficulty that is closely watched in the world. This paper reviews the running condition monitoring techniques applied to high-voltage cables. First, the principle analysis and characteristic comparison of various PD detection techniques are reviewed; secondly, the overview and principles of five running temperature monitoring techniques are reviewed; in addition, the theory and application of ground current detection techniques are reviewed; finally, the cable condition diagnosis technology is reviewed. To monitor the running condition of high-voltage cables, make reasonable judgments, find defects in time, and then improve the reliability of power supply. This paper provides theoretical and application guidance.

Polypropylene (PP) is an excellent environmental-friendly cable insulation material with a high melting point, low density, excellent electrical insulation properties, and recyclability. The mechanical properties of PP have an essential influence on its application in cable insulation and play an important role in determining the use of cable. The mechanical properties of the material are usually evaluated using film samples, but they are significantly different from those of actual cable. This paper simulated and analyzed the mechanical properties of pure PP and grafted PP cable insulation materials to compare the mechanical properties of the two insulation materials in cable use. The simulation results showed that the stress and strain of different parts of the cable were different in the force process, and the stress distribution of cable insulation in different radial positions was uneven. The simulation has given a good indication of the stress on the various parts of the cable during deformation, with the surface layer requiring higher mechanical properties due to the more significant deformation. Considering the maximum deformation of the cable during manufacture and use, the stress on the cable was calculated for both insulation materials, and the distribution was analyzed. This study provides a good basis for the development and application of grafted PP cable.

As the key equipment in power system, the running state of overhead transmission lines is affected by various complex and random factors, and the maintenance workload of the line is too heavy to overhaul regularly. Therefore, it is very necessary to build a refined condition assessment system to improve the diagnosis accuracy and maintenance efficiency of overhead transmission lines. Recent years, State Grid Corporation of China (SGCC) has recorded mass of monitoring reports of transmission lines. The natural language processing (NLP) with deep learning model provides an effective way to extract the key defect information from the monitoring reports. In this paper, the joint model of intention classification and slot filling (ICSF) based on bidirectional encoder representation from transformers (BERT) is introduced. To improve the precision of defect information extraction, two optimization models of BERT are presented. The results show that Robustly Optimized BERT Pre-Training Approach (RoBERTa) has achieved better effects on ICSF with the extraction accuracy of 92.22%. Then, the hierarchical weighted scoring method is introduced to score the status of overhead transmission line based on the results of ICSF-RoBERTa. And the assessment results of the overhead transmission line state and the corresponding maintenance strategies are provided according to the scores. Finally, the feasibility of the proposed method is validated by practical cases of line inspection reports.

The running status of the power transformer has a crucial impact on the security and stability of the entire power system. The high-precision equipment diagnosis method is the most important part in judging the transformer condition. Consequently, a transformer fault diagnosis model based on optimized deep belief network with random forest and sparrow search algorithm (SSA-RF-DBN) is proposed. The deep belief network (DBN) is exploited to mine information from historical fault data of transformers and provide diagnosis results. The input and model parameters of DBN are optimized and determined by random forest (RF) and sparrow search algorithm (SSA) respectively. The multi-layer diagnosis structure including fault type diagnosis, electrical and thermal fault location diagnosis is established based on diagnosis models. The superiority of SSA-RF-DBN is verified by comparison experiments with other algorithms. And the diagnosis structure is applied on a transformer in service. The experiment results prove that the method of fault diagnosis and location diagnosis of power transformer proposed in this paper not only possesses better accuracy and reliability, but also makes up for the weakness of traditional methods in evaluating the fault location.

Polypropylene (PP) is regarded as a rather potential insulation material alternative for the next generation HVDC cable system. Grafting styrene is proved to be an effective method to further enhance the DC insulation properties especially under high temperature. The trap introduced by grafting modification is believed to be the key issue on such enhancement. In this paper, the quantum chemistry analysis based on DFT method is adopted to computationally investigate the chemical trap originating from grafting modification. The results indicate that grafting styrene introduces new trap orbitals within the HOMO–LUMO gap of PP, and the grafted aromatic ring, especially the delocalized Pi bond is responsible for it. Besides, the delocalized Pi bond can also lead to the local high negative electrostatic potential area on the PP chain, thus affecting the microscopic charge transportation in PP. This work is expected to provide a reference for investigating the mechanisms of charge transportation and macroscopic electrical properties enhancement of PP-based insulation for HVDC cables.

Gas insulated switchgear (GIS) is widely used because of its advantages of high reliability, convenient maintenance and economic floor space. In this paper, the application of X-ray imaging technology in GIS equipment detection is studied. The use of X-ray digital imaging detection system can effectively carry out visual online detection of GIS equipment, and it is relatively easy to detect the structural defects existing in the coordination and movement of bus line, disconnector, circuit breaker and other components. In addition, this paper also puts forward a robotic X-ray detection method for GIS, which plays a positive role in improving the security and automation level of X-ray detection.

Both gas insulated switchgear (GIS) and gas insulated transmission line (GIL) are applications of gas-insulated metal closure technology, while GIL is very close to the coaxially placed bus bars in GIS. This paper systematically investigates the detection method of X-ray imaging technology for mechanical type defects inside GIS/GIL. The X-ray visual detection system is used to carry out visual online detection of GIS/GIL equipment, enabling timely and effective detection of structural defects in bus bars, switchgear components and other parts. A GIS/GIL oriented X-ray detection robot system is also designed, and the X-ray visual detection system is mounted on a detection robot arm, which has a positive effect on ensuring the safety and efficiency of field detection.

The detection of foreign objects inside the electric equipment is an important task to maintain the normal operation of the power grid. Aiming at the problems of low efficiency and easily missed detection in the current manual inspection, this paper proposes an improved multi-channel electric equipment foreign objects segmentation method MU-Net based on U-Net. It fuses information from multimodal foreign objects images and uses this information to segment foreign objects. Two different networks of MU-Net perform shallow feature extraction and feature fusion on the original image and the filtered image respectively; at the same time, we use Transformer for deep feature extraction. This attention mechanism can enhance the feature learning of foreign pixels, make the feature extraction more perfect, thereby improving the accuracy of foreign objects segmentation in electric equipment. The experimental results show that MU-Net achieves 92.5 in F1 and 85.24 in mIou on our dataset. Compared with other excellent segmentation networks, the segmentation accuracy has been improved, which fully verifies that MU-Net can effectively Perform foreign objects detection in electric equipment.

为了明确GIS内部过热缺陷的暂态过程及红外成像诊断方法,文中在试验室搭建了550 kV GIS母线触头过热缺陷模型,分别设置了不同的接触不良情况,在不同的电流下,得到了故障点温度及外壳的温度分布情况,并与接触良好情况的隔离开关及其分支母线气室进行了对比.同时,采用有限元方法,计算了试验室无法开展的其他试验工况.研究结果表明:只要外壳局部温度高于环境温度,通过红外成像仪就可以清晰地观测到;接触电阻、负荷电流和环境温度对故障点外壳的温升影响较大,盆式绝缘子类型对故障点外壳温升无影响;起初,接触电阻与其产生的局部高温是一个线性的交互过程,当温度接近导体熔点时,从导通到最终熔化经历的时间很短,这一瞬态过程不能通过带电检测发现.

With the proposal of “peak carbon dioxide emissions” and “carbon neutrality” goals, photovoltaic power generation as a representative of green renewable energy, its strategic position is prominent. Photovoltaic industry will face more opportunities and challenges. However, in the traditional mode of photovoltaic power station operation and inspection, a large number of operation and inspection personnel need to be configured, so it is difficult to obtain equipment information timely and comprehensively. In this paper, we propose a photovoltaic power station intelligent operation and maintenance system based on digital twin. The mapping of real photovoltaic power station is constructed in virtual space to realize intelligent operation and maintenance of photovoltaic power station. We build a 3D scene model to simulate the real environment. Two artificial intelligence algorithms are designed to realize the real-time power prediction and fault diagnosis of the digital twin system. This paper discusses the different components of this Digital twin photovoltaic power station operation and maintenance system.

通过某新建220 kV变电工程整体不均匀沉降案例,阐述变电工程在开展土方回填及设备基础沉降观测的重要性与注意事项,分析了土方回填施工在基底处理、土料含水率控制和土方压实中的重要监测点,并开展沉降观测的目的、频率方法等,从投运验收角度出发,提出类似情况的整改意见,最终实现预防因设备基础沉降而导致设备损坏、漏气跳闸等事故发生.

Traditional status evaluation for main equipment of power transmission and transformation has some shortages, such as low timeliness, low data quality and difficulty for evaluation model construction. Based on digital twin technology system, this paper presents technology of equipment status, and constructs digital twin for power transmission and transformation equipment. According to operation characteristics of power transmission and transformation equipment, fusion and cleansing of perception data is realized. Relying on big data analysis and data mining, status evaluation differentiation, accurate fault diagnosis and status prediction for power transmission and transformation equipment is realized. Further more, this paper analyzes the application of digital twin technology in on-line status evaluation for transformer equipment, expounds specific application of digital twin technology including data governance and model building, and summarizes application prospect of digital twin technology in on-line status evaluation for main equipment of power transmission and transformation.

The proportion of internal foreign matter is the largest in the insulation fault of GIS (Gas Insulation Switchgear) equipment, and when the foreign objects inside the long-tube structure GIS equipment are detected, the traditional vacuum cleaner cleaning and manual wiping methods are difficult to play an effective role. Based on the intelligent inspection and cleaning robot platform of the GIS equipment, this paper designs the dual-light source light filling technology which relies on the combination of ultraviolet light source and visible light source in order to clean the foreign objects in the GIS pipeline more effectively, and studies the foreign matter recognition algorithm to process images taken by robot using dual light source compensation, which can be used as a completely new scheme to check and verify the foreign matter defects of GIS equipment and to clean up foreign matter. Under the two kinds of light sources, the images taken inside the pipeline are processed to make foreign matter recognition judgment, which provides guidance for the robot to clean foreign matter. In this paper, while realizing the visualization of the foreign matter inside the GIS cavity, the problem that the single light source of the intelligent inspection and cleaning robot inside the GIS equipment is difficult to find some foreign particles is solved. It also avoids the problem that the robot needs manual experience to discriminate the foreign object, and improves the efficiency of the foreign object inspection inside the GIS equipment.

为了确定某252 kV HGIS母联三工位隔离开关动触头卡滞故障的原因,避免类似事件再次发生,文中通过现场检测、返厂解体与试验和理论计算等手段,对造成该事故的原因进行了详细分析.分析表明:动触头及静触座尺寸超差使动触头导体卡滞,无法操作,最终导致齿轮轴与齿轮咬合的平键处承受的力矩大于许用剪切力,最后被切断.

At present, UHV surge arresters have a large number of sections and complex structures. The existing live detection and online monitoring technologies can no longer effectively determine the problem of deterioration of the internal resistance of the arrester. This paper studies the structure, power supply, temperature and humidity measurement and wireless transmission methods of measuring probes that can be placed in the arrester for a long time. By measuring the temperature, humidity and current data of each node, and sending it to the background wirelessly, it is more effective to judge the UHV Internal defects of the arrester. The method of internally measuring the temperature and humidity of the arrester node can improve the diagnostic ability of the internal defect and defect location of the UHV arrester, and realize the effective identification and location of the internal fault of the UHV arrester.

为了明确水平布置,断口采用封闭绝缘筒支撑的超高压及特高压罐式断路器操作次数对其主断口绝缘的影响,以某电厂在并网过程中,发变组断路器主断口绝缘击穿为例,首先,建立了操作次数与绝缘击穿概率模型;其次,利用有限元方法,计算了断路器主触头磨损产生碎屑的量、碎屑形状、碎屑分布形式对主断口绝缘强度的影响;最后,针对防止主触头机械磨损对主断口绝缘水平的影响,提出了改进措施.研究表明:在反相电压下,操作次数达到211～571之间时,主断口击穿概率为99.74％;当操作次数小于211时,碎屑积累较快,但分布间隙较大,主断口之间的最大场强小于击穿场强;当操作次数在211～571之间时,碎屑增长变缓,分布间隙较小,主断口之间的最大场强大于击穿场强,绝缘击穿为肯定事件.

为了研究某550 kV换流站断路器合闸电阻发生多起烧损事故的原因,避免事故再次发生和扩大,文中通过录波分析、返厂解体分析和仿真计算等手段,获取了较多的支撑原因的证据.研究表明:滑动触指部位涂抹了过多的润滑脂,在触头高速运动过程中,掺杂着银屑的润滑脂随着气流被带到绝缘拉杆和保护管,多次累积引起沿面放电,在触头预击穿的暂态电压下发生了击穿,最终使合闸电阻烧损.

针对550 kV GIS内隔离开关在实际操作中出现的传动链失效以及传动机构堵塞等问题,文中搭建了隔离开关等效模型,计算了等效负载转矩以及等效转动惯量,分析了隔离开关与电机操动机构的运动配合关系.在对其结构进行对比分析的基础上,采用Ansoft软件对2种不同故障下GIS内隔离开关分合闸驱动电机的转矩、电流以及加速度进行仿真分析.结果 表明,驱动电机在超过正常换相时刻范围后仍然没有检测到电流换相且电流基本稳定,则说明发生卡塞堵转故障;若第一次换相时刻较正常情况有较大差异,说明负载发生较大变化,时刻提前则有可能发生传动部件松脱而导致负载下降,时刻延迟则有可能发生部件卡涩致使摩擦加剧,负载上升.