FPGA-based online power quality monitoring system for electrical distribution network

Abstract In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.

[1]  Huseyin Eristi,et al.  Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines , 2013 .

[2]  José Seixas,et al.  A method based on independent component analysis for single and multiple power quality disturbance classification , 2015 .

[3]  Irene Yu-Hua Gu,et al.  Support Vector Machine for Classification of Voltage Disturbances , 2007, IEEE Transactions on Power Delivery.

[4]  Asghar Akbari Foroud,et al.  A new hybrid pattern recognition scheme for automatic discrimination of power quality disturbances , 2014 .

[5]  Zhigang Liu,et al.  A new detection approach of transient disturbances combining wavelet packet and Tsallis entropy , 2014, Neurocomputing.

[6]  Juan José González de la Rosa,et al.  A web-based distributed measurement system for electrical power quality assessment , 2010 .

[7]  E. Gunther,et al.  A survey of distribution system power quality-preliminary results , 1995 .

[8]  Belkis Eristi,et al.  Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine , 2014 .

[9]  Aslam P. Memon,et al.  A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network , 2017 .

[10]  Prasanta Kundu,et al.  Power quality index based on discrete wavelet transform , 2013 .

[11]  Om Prakash Mahela,et al.  Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers , 2017, Appl. Soft Comput..

[12]  Elpidio Oscar Benitez Nara,et al.  An embedded system approach for energy monitoring and analysis in industrial processes , 2016 .

[13]  Zhigang Liu,et al.  An Approach to Recognize the Transient Disturbances With Spectral Kurtosis , 2014, IEEE Transactions on Instrumentation and Measurement.

[14]  Ming Zhang,et al.  A Power Quality Monitoring System over the Internet , 2009, 2009 First International Conference on Information Science and Engineering.

[15]  Mohammad A. S. Masoum,et al.  Power Quality in Power Systems and Electrical Machines , 2008 .

[16]  Madhuri A. Chaudhari,et al.  Conjugate gradient back-propagation based artificial neural network for real time power quality assessment , 2016 .

[17]  Marcelo Godoy Simões,et al.  Power Quality Enhancement for a Grid Connected Wind Turbine Energy System , 2017, IEEE Transactions on Industry Applications.

[18]  Geun-Jun Kim,et al.  System Design for Real-Time Measuring of Power Quality and Harmonics Distortion using Digital Signal Processor , 2016 .

[19]  Qing Zhang,et al.  A power quality online monitoring system oriented ZigBee routing optimization strategy , 2016, Wirel. Networks.

[20]  Pradipta Kishore Dash,et al.  Multiclass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine-based feature selection , 2018 .

[21]  Carmine Landi,et al.  Power-Quality Monitoring Instrument With FPGA Transducer Compensation , 2009, IEEE Transactions on Instrumentation and Measurement.

[22]  Zhigang Liu,et al.  A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM , 2015, IEEE Transactions on Smart Grid.

[23]  Hui Liu,et al.  Power Quality Disturbances Classification Using Compressive Sensing and Maximum Likelihood , 2018 .

[24]  M. B. Hughes,et al.  Canadian National Power Quality Survey results , 1996, Proceedings of 1996 Transmission and Distribution Conference and Exposition.

[25]  Daniel Morinigo-Sotelo,et al.  Smart sensor network for power quality monitoring in electrical installations , 2017 .

[26]  Gang Chen,et al.  A new classification method for transient power quality combining spectral kurtosis with neural network , 2014, Neurocomputing.

[27]  Azah Mohamed,et al.  Power quality impacts of high-penetration electric vehicle stations and renewable energy-based generators on power distribution systems , 2013 .

[28]  Steven M. Blair,et al.  Automatically Detecting and Correcting Errors in Power Quality Monitoring Data , 2017 .

[29]  Hui Liu,et al.  Power quality disturbances classification based on curvelet transform , 2017 .

[30]  Bhim Singh,et al.  Dual-Tree Complex Wavelet Transform-Based Control Algorithm for Power Quality Improvement in a Distribution System , 2017, IEEE Transactions on Industrial Electronics.

[31]  Kui Wu,et al.  A Machine Learning Approach to Meter Placement for Power Quality Estimation in Smart Grid , 2016, IEEE Transactions on Smart Grid.

[32]  Abdelkader Bousselham,et al.  The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid , 2015, FNC/MobiSPC.

[33]  Yi Qin,et al.  A Novel Series Power Quality Controller With Reduced Passive Power Filter , 2017, IEEE Transactions on Industrial Electronics.

[34]  Zhang Ming,et al.  DSP-FPGA beads real-time power quality disturbances classifier , 2010 .