DocumentCode :
403765
Title :
Real-time power quality waveform recognition with a programmable digital signal processor
Author :
Wang, M. ; Rowe, G.I. ; Mamishev, A.V.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Volume :
2
fYear :
2003
fDate :
13-17 July 2003
Abstract :
Power quality (PQ) monitoring is an important issue to electric utilities and many industrial power customers. This paper presents a DSP-based hardware monitoring system based on a recently proposed PQ classification algorithm. The algorithm is implemented with a Texas Instruments (TI) TMS320VC5416 digital signal processor (DSP) with the TI THS1206 12-bit 6 MSPS analog to digital converter. A TI TMS320VC5416 DSP starter kit (DSK) is used as the host board with the THS1206 mounted on a daughter card. The implemented PQ classification algorithm is composed of two processes: feature extraction and classification. The feature extraction projects a PQ signal onto a time-frequency representation (TFR), which is designed for maximizing the separability between classes. The classifiers include a Heaviside-function linear classifier and neural networks with feedforward structures. The algorithm is optimized according to the architecture of the DSP to meet the hard realtime constraints of classifying a 5-cycle segment of the 60 Hz sinusoidal voltage/current signals in power systems. The classification output can be transmitted serially to an operator interface or control mechanism for logging and issue resolution.
Keywords :
Texas Instruments computers; analogue-digital conversion; classification; digital signal processing chips; feature extraction; feedforward neural nets; power supply quality; real-time systems; time-frequency analysis; DSP-based hardware monitoring system; Heaviside-function linear classifier; TI TMS320VC5416 DSP starter kit; TMS320VC5416 digital signal processor; Texas Instrument; analog to digital converter; control mechanism; daughter card; electric utilities; event classification; feature extraction; feedforward structure; host board; industrial power customer; neural network; operator interface; power quality monitoring; realtime constraint; time-frequency representation; waveform recognition; Classification algorithms; Digital signal processing; Digital signal processors; Feature extraction; Hardware; Instruments; Monitoring; Power industry; Power quality; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering Society General Meeting, 2003, IEEE
Print_ISBN :
0-7803-7989-6
Type :
conf
DOI :
10.1109/PES.2003.1270511
Filename :
1270511
Link To Document :
بازگشت