Wide-Area Phase-Angle Measurements for Islanding Detection—An Adaptive Nonlinear Approach

The integration of an ever-growing proportion of large-scale distributed renewable generation has increased the probability of maloperation of the traditional RoCoF and vector shift relays. With reduced inertia due to nonsynchronous penetration in a power grid, system-wide disturbances have forced the utility industry to design advanced protection schemes to prevent system degradation and avoid cascading outages leading to widespread blackouts. This paper explores a novel adaptive nonlinear approach applied to islanding detection, based on wide-area phase-angle measurements. This is challenging since the voltage phase angles from different locations exhibit not only strong nonlinear but also time-varying characteristics. The adaptive nonlinear technique, called moving window kernel principal component analysis, is proposed to model the time-varying and nonlinear trends in the voltage-phase angle data. The effectiveness of the technique is exemplified using DigSilent simulated cases and real test cases recorded from the Great Britain and Ireland power systems by the OpenPMU project.

[1]  R. Cattell The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[2]  Ria Nandi,et al.  Islanding detection in distributed generation , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[3]  Edmund R. Malinowski,et al.  Factor Analysis in Chemistry , 1980 .

[4]  Nina F. Thornhill,et al.  A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis , 2015, IEEE Transactions on Power Systems.

[5]  George W. Irwin,et al.  RBF principal manifolds for process monitoring , 1999, IEEE Trans. Neural Networks.

[6]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[7]  G. Joós,et al.  A Fuzzy Rule-Based Approach for Islanding Detection in Distributed Generation , 2010, IEEE Transactions on Power Delivery.

[8]  Y. Xue,et al.  Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches , 2015, IEEE Transactions on Power Delivery.

[9]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[10]  M. Kramer Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[11]  S. Imai,et al.  Islanding protection system based on synchronized phasor measurements and its operational experiences , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[12]  Paul Smith,et al.  Studying the Maximum Instantaneous Non-Synchronous Generation in an Island System—Frequency Stability Challenges in Ireland , 2014, IEEE Transactions on Power Systems.

[13]  G. Joos,et al.  Data Mining Approach to Threshold Settings of Islanding Relays in Distributed Generation , 2007, IEEE Transactions on Power Systems.

[14]  H. Akaike A new look at the statistical model identification , 1974 .

[15]  Johan A. K. Suykens,et al.  Efficiently updating and tracking the dominant kernel principal components , 2007, Neural Networks.

[16]  N. Perera,et al.  Investigation of a fast islanding detection methodology using transient signals , 2009, 2009 IEEE Power & Energy Society General Meeting.

[17]  Le Xie,et al.  Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis , 2014, IEEE Transactions on Power Systems.

[18]  M. A. M. Ariff,et al.  Coherency identification in interconnected power system - an independent component analysis approach , 2013, 2013 IEEE Power & Energy Society General Meeting.

[19]  X. Liu,et al.  Principal Component Analysis of Wide-Area Phasor Measurements for Islanding Detection—A Geometric View , 2015, IEEE Transactions on Power Delivery.

[20]  Fushuan Wen,et al.  Application of wide area measurement systems to islanding detection of bulk power systems , 2013, IEEE Transactions on Power Systems.

[21]  Uwe Kruger,et al.  Recursive partial least squares algorithms for monitoring complex industrial processes , 2003 .

[22]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[23]  A. Dysko,et al.  Experience with accumulated phase angle drift measurement for islanding detection , 2012, 2012 47th International Universities Power Engineering Conference (UPEC).

[24]  M. O'Malley,et al.  A Study of Principal Component Analysis Applied to Spatially Distributed Wind Power , 2011, IEEE Transactions on Power Systems.

[25]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[26]  J Thambirajah,et al.  A Multivariate Approach Towards Interarea Oscillation Damping Estimation Under Ambient Conditions Via Independent Component Analysis and Random Decrement , 2011, IEEE Transactions on Power Systems.

[27]  E. Barocio,et al.  Detection and visualization of power system disturbances using principal component analysis , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

[28]  U. Kruger,et al.  Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .

[29]  Robert Best,et al.  Islanding detection based on probabilistic PCA with missing values in PMU data , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[30]  G. Joos,et al.  Intelligent-Based Approach to Islanding Detection in Distributed Generation , 2007, IEEE Transactions on Power Delivery.

[31]  Freyr Sverrisson,et al.  Renewables 2014 : global status report , 2014 .

[32]  Luigi Vanfretti,et al.  The OpenPMU Platform for Open-Source Phasor Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

[33]  D. John Morrow,et al.  Loss-of-mains protection system by application of phasor measurement unit technology with experimentally assessed threshold settings , 2015 .

[34]  B. Chaudhuri,et al.  Coherency identification in power systems through principal component analysis , 2005, IEEE Transactions on Power Systems.

[35]  Graeme Burt,et al.  Islanding detection using an accumulated phase angle drift measurement , 2010 .

[36]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[37]  Wei Lee Woon,et al.  A Bayesian Passive Islanding Detection Method for Inverter-Based Distributed Generation Using ESPRIT , 2011, IEEE Transactions on Power Delivery.

[38]  Zhe Chen,et al.  Review on islanding operation of distribution system with distributed generation , 2011, 2011 IEEE Power and Energy Society General Meeting.