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The first 10-kV regional compensation dynamic voltage restorer (DVR) has been installed in a 110-kV substation in Shenzhen power grid, to protect plenty of sensitive loads in a wide area si...

The operation experiences of power transmission lines show the lightning outage is still the main part of the total outage rate. So it is very important to calculate the lightning overvoltage across the insulators precisely and evaluate the lightning outage of the transmission lines correctly. Lightning flashover criterion of the insulator/gap is one of the most important factors that influence the precise calculation results of the lightning withstand level of transmission lines. In China, several ±800 kV ultra high voltage direct current (UHVDC) power transmission projects have been set up in recent years, and the altitude of the regions that the transmission lines across ranges from 0-3650 m. The paper mainly analyzed the influence of the modelling methods of insulator/gap on the lightning withstand level of ±800 kV UHVDC transmission lines by PSCAD/EMTDC program. The compared lightning flashover criteria of insulator/gap include the leader propagation model recommended by CIGRE, the volt/time curve recommended by IEEE and an improved leader propagation model, which is proposed based on the lightning flashover experiments of the ±800 kV UHVDC line insulators carried out at an altitude of 2100 m. Meanwhile, the influences of lightning current waveform and insulator structure were also considered and compared. The simulation results show that the calculated back-flashover lightning withstand level is much lower when using the improved leader propagation than that using other flashover criterions in the paper, it is lower under the 2.6/50 μs double exponential lightning current than that under the 2.0/50 μs Heidler function lightning current, and it is higher when using V-string than that using I-string insulator. Besides, the influences of the lightning current waveform and the insulator structure on the calculation of shielding failure lightning withstand level are little, but the flashover criterion has obvious influences on the shielding failure lightning withstand level.

Insulator strings is the important equipment in power system. It is necessary to identify the insulator string for completing the cleaning task of insulator string. A new method is proposed to identify insulator strings by multi sensor fusion algorithm based on D-S evidence theory. Firstly, the gray characteristic curve is defined in the optical image of camera, and the gray repetitive matrix and gray threshold matrix is constructed by the gray characteristic curve. Then, the depth characteristic curve is defined in the depth image of laser radar, and the depth repetitive matrix and width feature matrix is constructed by the depth characteristic curve. The features of every matrix are fussed by the Dempster-Shafer evidence theory to complete automatic recognition of three types isolators. The experimental results show that the new method can correctly identify the insulator strings under different lighting conditions.

Substation insulator strings which exposed to the outdoor environment need to be cleaned the industrial contamination and natural contamination regularly in order to prevent the flashover phenomenon that occurs in rain and fog and other inclement weather because of the accumulation of contamination and then cause power outages and other incidents. The Substation Charged Maintenance Robot is used for cleaning the substation equipment in 110kV–220kV AIS Substation. The vibration characteristics of the robot's chassis and operating platform cannot be ignored to ensure the accuracy of the job. Statics simulation analysis and modal analysis are conducted according to the job characteristics of the robot. Stress distribution of operating platform and low-order vibration frequencies and mode shapes of telescopic arms are obtained and then vibration control schemes are proposed based on the simulation results.

We proposes a data-driven approach for voltage sag diagnosis in this paper. Rather than traditional features such as frequency, amplitude and duration, we apply temporal distribution as a new feature to distinguish whether the sag is caused by power system faults or heavy load switching. Sags caused by these two reasons have different distribution pattern, and this work interprets it from a number of perspectives. We also perform voltage sag source location by clustering. This approach don't use any physical level analysis and can find the fault source faster as well as accurately.

The dielectric loss is a small value, is also an important indicator to measure the performance of the electrical equipment insulation[1, 11]. But outside interference can easily cause a big impact to the dielectric loss measurement, and real-time response is required on the dielectric loss online monitoring system. Therefore, high accuracy, rapid digitization of the dielectric loss measurement method is a key part of the dielectric loss online monitoring system. This article describes a practical harmonic analysis method to measure the dielectric loss. Combined with the IEC61850 and IEEE1588, according to the special circumstances of the measurement site, the digital method of measuring dielectric loss is realized through the joint work of a voltage acquisition unit and another entirely different current acquisition unit. Finally the source of calculating errors is verified and the error is measured less than 0.00025 (radian) in test, which is less than 1μs when measured in sampling-time.

Mechanical fault is one of the main faults occurring during the life cycle of high-voltage circuit breakers (HVCBs), which has a significant influence on the reliability of the electrical power system. In this paper, the mechanical prediction algorithm for HVCBs based on support vector machine (SVM) was studied. Firstly, we used a sliding time window (STW) method to extract features of the travel curves of the movable contacts and coil current curves of HVCBs. Then the historic data were used to learn a support vector regression machine and finally to predict the new curves. In the end, the mechanical life experiment data of a HVCB were applied to validate the feasibility of the algorithm. The results showed that the proposed algorithm could predict the mechanical condition of HVCBs successfully.

With its significant advantages, flexible DC power distribution system has been widely recognized. Flexible DC distribution network has complex components such as distributed power generation, BESS and power load, so the active power exchanged between DC distribution network and AC grid may have large random fluctuations, which affects the stable operation of AC grid. At present, there are no standard about the rate of power fluctuations when flexible DC distribution network is connected to AC grid. A standard was made that the maximum power change within 10-min should be less than 1/3 of the capacity of converter station. Based on a two-terminal structure, this paper presented a control strategy of BESS coordinated with converter stations to smooth power fluctuations. Firstly, the power of DC distribution network is distributed between two converters according to their capacity proportion, power fluctuations get suppressed preliminarily, through charging and discharging operation of BESS, power fluctuations can be smoothed further. To ensure the charging and discharging capacity of BESS, its SOC was controlled at about 50%. Finally, simulation was carried out in EMTDC/PSCAD, and the results showed the efficiency of the proposed strategy.

Flexible DC power distribution network is a new direction of flexible HVDC technology, it is now at the research stage. The advantages and technical difficulties of flexible DC distribution network are analyzed in this paper, and the flexible DC distribution demonstration project prepared to build in Shenzhen is briefly introduced. The overvoltage and insulation coordination is one of the key technologies in the implementation of flexible HVDC projects. By introducing the research status of overvoltage and insulation coordination of flexible HVDC and the problems existing in these researches, and analyzing the differences between the flexible DC power distribution network and the flexible HVDC, the problems need noting in the research of overvoltage and insulation coordination for flexible DC power distribution network are pointed out.

One of the most important features in smart grid is power system self-healing and power quality improvement. Power quality monitoring is essential to realize this feature. Installing power quality monitors (PQM) in every component of the power network is not feasible due to economic reasons. So how to find the optimal number and locations of power quality monitors while maintaining system observability becomes an important problem. The major contribution of this paper includes providing the model for PQM optimization problem considering both system observability and fault location constraints. The model is then formulized as an integer linear problem and reduced to a group of k-median decision problems. A local search algorithm is proposed to solve the problem. The IEEE 14 bus network is utilized as a case study. Algorithm efficiency is evaluated using Matlab tools and compared with an existing branch and bound algorithm. Experimental results show that proposed algorithm is more than an order of magnitude faster than current algorithm while maintain the accuracy of results.

A transient power quality assessment method is proposed in this paper, using Naive B ayes classification method which is based on big data processing architecture. In this architecture, data sources will be extended to the aspects of power grid monitoring data, the power customer data and the public data, and the assessment severity will be classified into the normal state, the abnormal state, the critical state, and the failed state, according to the Naive Bayes classification results. Based on the data type of transient power quality assessment, big data processing architecture used in this paper can be able to process distributed data and streaming data, so that it can ensure not only updates classifier rules regularly, but also the real-time condition assessment. In the classifier training phase, we use the massive historical data as the distributed learning object, and generate assessment rules periodically. In the state assessment phase, each assessment node will update the assessment rules generated by training phase, generate real­ time evaluation of samples from stream processing fram ework, and evaluate the power quality state according to the current rule. On this basis, this paper designs a Naive Bayes classification method based on MapReduce processing, and realizes the map and reduce process method to compute the priori probability and the conditional probability in distributed way. Experiments show that the transient power quality evaluation method based on the big data analysis presented in this paper is feasible, and achieve good results both in classification accuracy and processing speed.

With development of information technology and high-tech companies, power requirements have turned from quantity to quality. Power quality issues are becoming more essential for power companies. Existing power quality standards are described by stable or transient states of current and voltage, from a perspective of power supply. In practice, it is up to customers' sensitivity and actual experiences for evaluation of power quality. In this work, power experience is proposed as a composite criterion of both existing power quality standard and related customers' information. From a perspective of customers' experience, the concept, features, and standards are proposed for power quality of experience. A case study from Shenzhen Power Supply Company is given, demonstrating that power quality of experience is more feasible in actual work for power quality evaluation.

Power quality issues are becoming more critical for high-tech enterprises and grid companies. Many power quality monitoring systems are deployed in recent years. Advanced analysis of monitoring data is not widely applied due to the lackness of data management. In this work, data cleaning technology is introduced to enable advanced study of power quality data, with detailed procedures and software implementation. With power quality monitoring data from Shenzhen Power Supply Company, the effectiveness of data cleaning technology applied for power quality data analysis is demonstrated. Cleaned data that avoid voidness and lackness is more feasible in actual usage, as a good basis for further advanced analysis of power quality.

A novel automatic seeded region growing method (SRG) for high temperature region extraction of IR image is proposed in this paper. The proposed method efficiently utilizes the RGB values of IR image. The neighborhood average method is selected to reduce image noise. Red component and green component which are more efficient to extract high temperature region because their values are positive correlated with temperature, are extracted to make segmentation using seeded region growing method. Seed points are determined as the connected pixels which exceed a certain threshold, and the grow criteria is based on the gradient of the two component images. Intersection method is used for image fusion, and high temperature region is extracted. Experiment results indicate that the method can accurately recognize the high temperature area, and the outline is clear.

This paper introduces the performance of a new type of composite insulated dry current transformer (dry CT in the paper). It is made of synthetic materials and designed distinctively from traditional oil and gas insulation. The results of the test and dissection of three 110 kV units of dry CTs which have operated for three and a half years in a power system are presented. They indicate that such components sustain excellent insulation performance, withstanding higher AC voltage, showing lower dielectric dissipation factor (tandelta), smaller magnitude of partial discharge and higher insulation margin. This provides a higher operational reliability and safety along with maintenance-free operation. This kind of dry CT is being widely adopted in the Chinese power grid as more than 10,000 of such CTs have been operating without a single failure since 1998. The causes for such distinctive reliability and low deterioration rate are discussed

The Internet of Things (IoT), which combines identification, sensing, computing, and communication technologies, is considered one of the major trends in information and communication technologies. Communication performance is critical for IoT applications. According to previous research, an internet-based overlay model is feasible for the implementation of the IoT. One important issue in the overlay routing model is the overlay node placement problem (ONPP). Once the size of overlay node set is fixed to a particular number k, the ONPP changes to k-ONPP. In this work, the IoT-based overlay node placement problem is formulized and analyzed. The major contributions of the paper include providing the time complexity of multi hop k-ONPP and its theoretical limit boundary of approximation ratio and proposing a local search algorithm. Furthermore, the time complexity and approximation ratio boundary of the local search algorithm are given. The proposed local search algorithm is evaluated by both time and efficiency where efficiency refers to the degree of approximation of algorithm results with optimal solutions. Another algorithm, TAG, is used for comparison. Finally, a simulation experiment based on network simulator EstiNet is provided. The experimental results show network delay benefits from the proposed method.