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

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.