Novel Current Unbalance Estimation and Diagnosis Algorithms for Condition Monitoring With Wireless Sensor Network and Internet of Things Gateway

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 11; pp. 6080 - 6090
• Main Authors: Hamici, Zoubir, Abu Elhaija, Wejdan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer networks; Condition monitoring; Customization; Data acquisition; diagnosis estimation; Estimation; Fault detection; Fault diagnosis; Frequency estimation; Gateways; Induction motors; industrial machines; Internet of Things; Lower bounds; Machinery condition monitoring; Microcontrollers; Monitoring; Parameter estimation; Performance evaluation; Power systems; Remote monitoring; Remote sensors; Servers; Signal processing algorithms; Unbalance; Voltage measurement; Wireless sensor networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2935743

This article presents two novel algorithms for the estimation and diagnosis of the current unbalance factor (CUF) for three-phase power systems from single period of three-phase acquired data samples. The CUF is evaluated by an algorithm named circular phase shift (CPS), and the three-phase parameters are estimated by a circular cross-correlation (CCC) algorithm for unbalance diagnosis. The estimated CUF along with multisensory data is transmitted and monitored through a remote web server for diagnosis and detection of incipient three-phase power systems faults including three-phase machines. The CPS algorithm estimation has an accuracy that exceeds 95% for CUF values exceeding 5%. The CCC time-complexity and the Cramer-Rao Lower Bound analysis are presented for performance evaluation. The CCC algorithm outperforms the IEEE-Standard-1057 estimation method in both phase accuracy and processing memory requirements compatible with low-cost microcontrollers. Experimental results on condition monitoring of industrial induction machines (1.5 to 7.5 KW) are also presented with custom designed 2.4-GHz wireless sensor network and an IEEE 802.11 Internet of Things gateway with multisensory data which carries out the effectiveness of the system.