Multiple chaos synchronization system for power quality classification in a power system

Abstract

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.

Figure 1

Architecture of multiple chaos synchronization (CS) systems.

Figure 2

The nonlinear electronic circuits of Chen-Lee based CS system.

Figure 3

Experimental simulation results. (a) Coupling signal: ideal periodic voltage signal and voltage sag signal, (b) dynamic errors Φ₁, Φ₂, and Φ₃, and (c) phase diagram of CS system.

Figure 4

One-line diagram of the 14-bus power system.

Figure 5

Various butterfly patterns at Bus13. (a) Butterfly patterns for harmonics, (b) butterfly patterns for normal voltage and voltage interruption, (c) and (d) butterfly patterns for sag events, and (e) and (f) butterfly patterns for swell events.

Figure 6

(a) Voltage sag involving harmonics in the time-domain, (b) dynamic errors for har and sah events, (c) motion trajectories of butterfly patterns for har and sah events, (d) voltage swell involving harmonics in the time-domain, (e) dynamic errors for har and swh events, and (f) motion trajectories of butterfly patterns for har and swh events.

Figure 7

The maximum matching likelihood among different patterns. Note: har: Number 1~Number 7, sa: Number 8~Number 18, sah: Number 19~Number 29, sw: Number 30~Number 40, swh: Number 41~Number 51, nor: Number 52~Number 58, and int: Number 59~Number 61.

Figure 8

(a) Average probability versus voltage magnitude variant. (b) Covariance versus the number of training pattern for voltage fluctuation phenomena. Note: (1) symbol “+” means voltage magnitude increase and symbol “−” means voltage magnitude decay, (2)  har: Number 1~Number 7, sa: Number 8~Number 18, sah: Number 19~Number 29, sw: Number 30~Number 40, swh: Number 41~Number 51, nor: Number 52~Number 58, and int: Number 59~Number 61.