Low-complexity wireless sensor system for partial discharge localisation

This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition-based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low-complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K-nearest neighbour and weighted K-nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation.

[1]  Pubudu N. Pathirana,et al.  Entropy-based method to quantify limb length discrepancy using inertial sensors , 2018, IET Wirel. Sens. Syst..

[2]  Hirokazu Ishimaru Member,et al.  Locating multiple partial discharge sources using MAP estimation and ray tracing , 2014 .

[3]  H. Mokhlis,et al.  Partial discharge phenomena within an artificial void in cable insulation geometry: experimental validation and simulation , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  Konstantinos N. Plataniotis,et al.  Kernel-Based Positioning in Wireless Local Area Networks , 2007, IEEE Transactions on Mobile Computing.

[5]  Jeril Kuriakose,et al.  Analysis of Maximum Likelihood and Mahalanobis Distance for Identifying Cheating Anchor Nodes , 2014, ArXiv.

[6]  Gehao Sheng,et al.  Localization Algorithm for the PD Source in Substation Based on L-Shaped Antenna Array Signal Processing , 2015, IEEE Transactions on Power Delivery.

[7]  Wei Wang,et al.  A semi-definite relaxation approach for partial discharge source location in transformers , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[8]  Ilenia Tinnirello,et al.  Channel estimation and transmit power control in wireless body area networks , 2015, IET Wirel. Sens. Syst..

[9]  P. Vieu,et al.  k-Nearest Neighbour method in functional nonparametric regression , 2009 .

[10]  H. Mohamed,et al.  A Supervisory System for Partial Discharge Monitoring , 2018, 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC).

[11]  Linqing Gui,et al.  RSS-based indoor localisation using MDCF , 2017, IET Wirel. Sens. Syst..

[12]  P.J. Moore,et al.  RF-Based Partial Discharge Early Warning System for Air-Insulated Substations , 2009, IEEE Transactions on Power Delivery.

[13]  P.J. Moore,et al.  Radiometric location of partial discharge sources on energized high-Voltage plant , 2005, IEEE Transactions on Power Delivery.

[14]  Yang Yang,et al.  Development and validation of a Simulator for Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) , 2017 .

[15]  Martin D. Judd,et al.  Low power radiometric partial discharge sensor using composite transistor-reset integrator , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[16]  Chun-Yao Lee,et al.  Wind Prediction Based on General Regression Neural Network , 2012, 2012 Second International Conference on Intelligent System Design and Engineering Application.

[17]  Weidong Liu,et al.  Research on the Typical Partial Discharge Using the UHF Detection Method for GIS , 2011, IEEE Transactions on Power Delivery.

[18]  Chahe Nerguizian,et al.  Neural network and fingerprinting-based localization in dynamic channels , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[19]  Alfred O. Hero,et al.  Relative location estimation in wireless sensor networks , 2003, IEEE Trans. Signal Process..

[20]  Jinbao Zhang,et al.  Overview of received signal strength based fingerprinting localization in indoor wireless LAN environments , 2013, 2013 5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[21]  Pavlos I. Lazaridis,et al.  Radio location of partial discharge sources: a support vector regression approach , 2018 .

[22]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[23]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[24]  B. T. Phung,et al.  Partial discharge localization in transformers using UHF detection method , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[25]  Chong Shen,et al.  Implementation of herd management systems with wireless sensor networks , 2009, IET Wirel. Sens. Syst..

[26]  Shengli Wu,et al.  Effective Neural Network Ensemble Approach for Improving Generalization Performance , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[27]  H. Mohamed,et al.  Low Power High-Speed Folding ADC Based Partial Discharge Sensor for Wireless Fault Detection in Substations , 2018, 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC).

[28]  Hossein Borsi,et al.  Partial discharge localisation on power transformers using neural networks combined with sectional winding transfer functions as knowledge base , 2001, Proceedings of 2001 International Symposium on Electrical Insulating Materials (ISEIM 2001). 2001 Asian Conference on Electrical Insulating Diagnosis (ACEID 2001). 33rd Symposium on Electrical and Ele.