A Review of On-line Condition Monitoring in Power System

Guo, Chenxi; Dong, Ming; Yang, Xiaoxi; Wang, Wensen

doi:10.1109/apap47170.2019.9225022

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Accurate transmission line current measurement needs to ensure the effectiveness of Equation (1). In other words, the primary current of the CT has a linear relationship with the secondary current under the utility-frequency condition.…”

Section: System Structure and Circuit Designmentioning

confidence: 99%

“…The equivalent circuit is shown in Figure 12. Since R 1 and R 2 are much smaller than (jωL m + R m ), the current through R 1 and R 2 follows Equation (1). The voltage values of R 1 and R 2 are sensed and entered into the ADC sampling pin of MCU after differential amplification.…”

Section: Mode Switch and System Workflowmentioning

confidence: 99%

“…The voltage values of R 1 and R 2 are sensed and entered into the ADC sampling pin of MCU after differential amplification. The current value of R 1 and R 2 is calculated by MCU, and the transmission line current is further calculated according to Equation (1). Finally, the results are sent via the wireless communication module to realize the further function.…”

Section: Mode Switch and System Workflowmentioning

confidence: 99%

“…Power transmission and distribution networks has high requirements for reliability and security, and a reliable online condition monitoring system (OCMS) can provide the guarantees [1]. Meanwhile, OCMS is also a prerequisite for the development of smart grid [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Time-Multiplexed Self-Powered Wireless Current Sensor for Power Transmission Lines

et al. 2021

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Current measurement is a key part of the monitoring system for power transmission lines. Compared with the conventional current sensor, the distributed, self-powered and contactless current sensor has great advantages of safety and reliability. By integrating the current sensing function and the energy harvesting function of current transformer (CT), a time-multiplexed self-powered wireless sensor that can measure the power transmission line current is presented in this paper. Two operating modes of CT, including current sensing mode and energy harvesting mode, are analyzed in detail. Through the design of mode-switching circuit, harvesting circuit and measurement circuit are isolated using only one CT secondary coil, which eliminates the interference between energy harvesting and current measurement. Thus, the accurate measurement in the current sensing mode and the maximum energy collection in the energy harvesting mode are both realized, all of which simplify the online power transmission line monitoring. The designed time-multiplexed working mode allows the sensor to work at a lower transmission line current, at the expense of a lower working frequency. Finally, the proposed sensor is verified by experiments.

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Section: System Structure and Circuit Designmentioning

confidence: 99%

Section: Mode Switch and System Workflowmentioning

confidence: 99%

Section: Mode Switch and System Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Multiplexed Self-Powered Wireless Current Sensor for Power Transmission Lines

et al. 2021

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“…The measurement of PD signals is conducted using two main approaches: on-line PD (ONPD) measurement and off-line PD (OFPD) measurement. ONPD detection is conducted while the equipment is live, requiring no de-energization or disruption of normal operation [ 3 , 4 , 5 ]; hence, the equipment is subjected to actual conditions that can affect PD activity such as HV stress, temperature, and vibration. On the other hand, OFPD detection is conducted with the equipment disconnected from the network, bringing a complete halt to normal operation [ 6 , 7 , 8 ], and requires the equipment to be energized via external means.…”

Section: Introductionmentioning

confidence: 99%

Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching

Abubakar,

Zachariades

2024

Sensors

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This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

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