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

Huang CH, Lin CH - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation and Control Engineering, Far-East University, Hsin-Shih, Tainan 744, Taiwan.

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.

No MeSH data available.


Related in: MedlinePlus

(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.
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fig8: (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.

Mentions: In this study, computer simulation was used to change traced disturbances. Voltage data were produced for varying voltage magnitudes and a variation in harmonic components varying from −50% to +50%, VTHD% ≥ 2.5%. Figure 8(a) shows the average probability versus voltage magnitude variance for 350 untrained data including nor, sa, sah, sw, swh, and int events. With training patterns for sa and sah events having a specific sag range between 0.7 and 0.9 per unit, voltage magnitudes between 0.50 and 0.90 per unit were identified as “sa” and “sah” events. The results can be also observed for “sw” and “swh” events between 1.10 and 1.50 per unit. Voltage interruptions were gradually identified for a voltage magnitude below 0.20 per unit. With the 61 sets of training patterns, the proposed method could work in a dynamic environment, with voltage fluctuation with harmonics. The experimental results show that the proposed method has high confidence in the classification of all events, using the rejection threshold θjudge.


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

Huang CH, Lin CH - ScientificWorldJournal (2014)

(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.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3934410&req=5

fig8: (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.
Mentions: In this study, computer simulation was used to change traced disturbances. Voltage data were produced for varying voltage magnitudes and a variation in harmonic components varying from −50% to +50%, VTHD% ≥ 2.5%. Figure 8(a) shows the average probability versus voltage magnitude variance for 350 untrained data including nor, sa, sah, sw, swh, and int events. With training patterns for sa and sah events having a specific sag range between 0.7 and 0.9 per unit, voltage magnitudes between 0.50 and 0.90 per unit were identified as “sa” and “sah” events. The results can be also observed for “sw” and “swh” events between 1.10 and 1.50 per unit. Voltage interruptions were gradually identified for a voltage magnitude below 0.20 per unit. With the 61 sets of training patterns, the proposed method could work in a dynamic environment, with voltage fluctuation with harmonics. The experimental results show that the proposed method has high confidence in the classification of all events, using the rejection threshold θjudge.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation and Control Engineering, Far-East University, Hsin-Shih, Tainan 744, Taiwan.

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.

No MeSH data available.


Related in: MedlinePlus