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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...

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.

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.

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.

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.