RBF AND SVM NEURAL NETWORKS FOR POWER QUALITY DISTURBANCES ANALYSIS Przemysław Janik, Tadeusz Łobos Wroclaw University of Technology Peter Schegner Dresden University of Technology

2 Contents Increased Interest in Power Quality RBF and SVM Neural Networks Space Phasor Basic Disturbances Simulation of Voltage Sags Conclusion

3 Interest in Power Quality Deregulation of the electric energy market Growing need for standardization Equipment has become more sensitive Equipment causes voltage disturbances Power quality can be measured

4 Interconnections

5 Space division by classical BP algorithm and RBF network Back Propagation AlgorithmRBF Neural Network

6 Radial Basis Function

7 Radial Basis Function RBF Neural Network Formulation of the Classification Problem X+, X- classes xinput vector  radial function

8 SVM Neural Networks Support Vector Machines Formulation of the Classification Problem

9 Learning of SVM networks Hyperplane Equation Finding the Minimum

10 Dividing hyperplane and separation margin Separation Margin Support Vectors

11 SVM characteristics linearly not separable data sets can be transformed into high dimensional space to be separable (Cover’s Theorem) Avoiding of local minima (quadratic programming) Learning complexity doesn't depend on data set dimension (support vectors) SVM network structure complexity depends on separation margin (to be chosen)

12 Space Phasor (SP)

13 Basic Disturbances Outages (Duration and Frequency) Sags Swells Harmonics Flicker (Voltage Fluctuation) Oscillatory transients Frequency variation Symmetry

14 Parametric equations of basic disturbances Oscillatory Transient Flicker Harmonics Sudden Swell Sudden Sag Pure Sinusoid EquationEvent

15 Parameters variation Signals number In each class: 50 Totally:300 EventParameters variation Pure SinusoidAll parameters constant Sudden Sagduration 0-9 T, amplitude pu Sudden Swellduration 0-8 T, amplitude pu Harmonicsorder 3,5,7, amplitude pu Flickerfrequency pu, amplitude pu Oscillatory Transient time const s, period pu

16 Voltage sags Sags deepness 0.4 Sags duration s time [s] U [p.u.] real part imaginary part

17 Oscillations Time constant s Oscillations period s time [s] U [p.u.] real part imaginary part

18 Flicker Flicker amplitude 0.12 Frequency 8 Hz time [s] U [p.u] real part Imaginary part

19 Classification results of SVM

20 Classification results of RBF Classification results of SVM

21 Sags originating in faults

22 Voltage sags x 10 4 time [s] U [V]

23 Conclusion and future prospects Automated PQ assessment needed SVM based classifier appropriate for automated PQ disturbances recognition Network models for wide parameter changes Research work do be done with real signal