TY - GEN
T1 - Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems
AU - Soliman, Hammam
AU - Davari, Pooya
AU - Wang, Huai
AU - Blaabjerg, Frede
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/3
Y1 - 2017/11/3
N2 - Reliability of dc-link capacitors in modern design of power electronic converters is an important aspect that needs to be considered. The requirement of applying condition monitoring for health status estimation in many reliability-critical applications have been a focused demand. The existing capacitor condition monitoring methodologies are suffering from shortcomings such as, low estimation accuracy, extra hardware, and increased cost, and thereby, they are rarely adopted by industry. Therefore, development of new methods that are based on advanced software algorithms and data processing techniques requiring no extra hardware will be more attractive to industry. In this paper, a condition monitoring methodology is proposed and applied on the dc-link capacitor in a three phase Front-End diode bridge motor drive. The proposed condition monitoring methodology estimates the capacitance value of the dc-link capacitor based on Artificial Neural Network (ANN) algorithm. Two ANNs (ANN1 and ANN2) are proposed, trained and evaluated based on time-domain and frequency-domain parameters. Experiments are conducted to validate the proposed methodology and the effectiveness of the proposed method is examined through an error analysis.
AB - Reliability of dc-link capacitors in modern design of power electronic converters is an important aspect that needs to be considered. The requirement of applying condition monitoring for health status estimation in many reliability-critical applications have been a focused demand. The existing capacitor condition monitoring methodologies are suffering from shortcomings such as, low estimation accuracy, extra hardware, and increased cost, and thereby, they are rarely adopted by industry. Therefore, development of new methods that are based on advanced software algorithms and data processing techniques requiring no extra hardware will be more attractive to industry. In this paper, a condition monitoring methodology is proposed and applied on the dc-link capacitor in a three phase Front-End diode bridge motor drive. The proposed condition monitoring methodology estimates the capacitance value of the dc-link capacitor based on Artificial Neural Network (ANN) algorithm. Two ANNs (ANN1 and ANN2) are proposed, trained and evaluated based on time-domain and frequency-domain parameters. Experiments are conducted to validate the proposed methodology and the effectiveness of the proposed method is examined through an error analysis.
UR - http://www.scopus.com/inward/record.url?scp=85041329914&partnerID=8YFLogxK
U2 - 10.1109/ECCE.2017.8096961
DO - 10.1109/ECCE.2017.8096961
M3 - Conference contribution
AN - SCOPUS:85041329914
T3 - 2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
SP - 5795
EP - 5802
BT - 2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017
Y2 - 1 October 2017 through 5 October 2017
ER -