Classification and identification of electric shock current for safety operation in power distribution network

Liu, Yongmei; Du, Songhuai; Sheng, Wanxing

doi:10.1049/iet-cps.2019.0072

Cited by 5 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…rough different structures and sensor combinations, grid data monitoring could be achieved [5]. To effectively identify the electric shock current in the uninterrupted operation of the distribution network, Liu proposed a support vector machine method to classify and monitor it [6]. ese monitoring systems and methods can identify and monitor the security risks of the power grid to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Safety Monitoring System and Method for Unmanned Operation of Unmanned Aerial Vehicle Distribution Network Based on Learning Control Robot Technology

Jia

Jiang

et al. 2022

Journal of Robotics

View full text Add to dashboard Cite

The continuous expansion of the scale of power grid construction has led to the emergence and development of the uninterrupted operation of the unmanned aerial vehicle (UAV) distribution network. However, in the actual power production process, due to the limitations of operating conditions and environment, power safety accidents are inevitable. With the support of intelligent technology, the monitoring effect and application level of the safety monitoring system for the uninterrupted operation of the UAV distribution network have been improved. Unified planning and scheduling were carried out throughout the job implementation process. The efficiency of equipment safety management has been strengthened, which is of great practical significance to the safe production of electric power. Based on the analysis of the environmental characteristics and implementation difficulties of the unmanned aerial vehicle distribution network, this paper studied the safety monitoring system and method by combining the high intelligence and high-efficiency characteristics of the learning control robot technology. The designed unmanned aerial vehicle distribution network safety monitoring system and method were tested. In order to understand the effect of the system intuitively, this paper analyzed from three aspects: system sensitivity, anti-interference, and data acquisition and management. It also contrasted with traditional monitoring systems. The test data showed that the sensitivity test value of the traditional monitoring system in the R1 circuit with the lowest voltage intensity was 71.3%. In the R4 circuit with the strongest voltage intensity, the test value only reached 84.4%. The test values of the system in this paper in the two types of circuits were 85.6% and 93.3%, respectively. In the data acquisition and management test, the acquisition and management rates under the highest numerical information could reach more than 97%. It can be seen that it has strong feasibility in actual operation.

show abstract

Section: Introductionmentioning

confidence: 99%

Safety Monitoring System and Method for Unmanned Operation of Unmanned Aerial Vehicle Distribution Network Based on Learning Control Robot Technology

Jia

Jiang

et al. 2022

Journal of Robotics

View full text Add to dashboard Cite

show abstract

“…When a small part of the input data is abnormal data, it can minimise the negative impact and make an actual prediction. The SVR has a good predictive ability for short-period, non-linear data, although it has a strong dependence on the choice of the kernel function [15]. LSTM is more applicable when the input data emphasises temporal changes.…”

Section: Framework Of Water Quality Prediction Methods Based On the Prmentioning

confidence: 99%

Water quality prediction method based on preferred classification

Sheng

Zhou

et al. 2020

IET cyber-phys. syst.

View full text Add to dashboard Cite

“…Researches prove that only the impedance of mammals which follows the dispersion characteristics is of complexity [6]. Hence, researches focusing on the detection method of biological electric shock characteristics (BESC) have been carried out [7][8][9]. On the one hand, human body impedance models are constructed to simulate electric shock effects [7].…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, human body impedance models are constructed to simulate electric shock effects [7]. On the other hand, local mean decompositions (LMD) based method [8] and support vector machine (SVM) based method [9] are applied to extract BESC. However, these methods cannot characterize the complexity of BESC and are easily affected by several factors (such as noise, normal leakage, environment, and the operation way of power grid).…”

Section: Introductionmentioning

confidence: 99%

A detection method for biological electric shock based on improved sample entropy

Xiong

Shi²

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Biological electric shock detection is the key to safeguarding of persons in grids. However, present methods cannot make protectors trip correctly by extracting the characteristics of living things. To improve the situation, this paper presents an innovation by which the protectors can be adaptive to trip correctly when the biological electric shock happens. The basic principle of proposed method is extracting biological electric shock characteristics (BESC) from measured current based on the sample entropy (SampEn). It can distinguish whether the electric shock is caused by mammals or not. Besides, for practical need, a fast BESC calculation method is proposed in order to reduce redundant computation. Moreover, an execution module for electric shock protection based on DSP is developed. And electric shock experiments have been carried out to validate the effectiveness of proposed method. The results show that accuracy of biological electric shock detection is improved and proposed method is practical even in strong noise environment.

show abstract

Classification and identification of electric shock current for safety operation in power distribution network

Cited by 5 publications

References 26 publications

Safety Monitoring System and Method for Unmanned Operation of Unmanned Aerial Vehicle Distribution Network Based on Learning Control Robot Technology

Safety Monitoring System and Method for Unmanned Operation of Unmanned Aerial Vehicle Distribution Network Based on Learning Control Robot Technology

Water quality prediction method based on preferred classification

A detection method for biological electric shock based on improved sample entropy

Contact Info

Product

Resources

About