Condition Monitoring Test Techniques for Medium Voltage OIP Bushings

Author(s):  
Suthat Suksagoolpanya ◽  
Norasage Pattanadech
Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4272 ◽  
Author(s):  
Muhammad Shafiq ◽  
Ivar Kiitam ◽  
Kimmo Kauhaniemi ◽  
Paul Taklaja ◽  
Lauri Kütt ◽  
...  

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals.


Author(s):  
Maximilian Benker ◽  
Sebastian Junker ◽  
Johannes Ellinger ◽  
Thomas Semm ◽  
Michael F. Zaeh

AbstractDue to their critical influence on manufacturing accuracy, machine tool feed drives and the monitoring of their condition has been a research field of increasing interest for several years already. Accurate and reliable estimates of the current condition of the machine tool feed drive’s components ball screw drive (BSD) and linear guide shoes (LGSs) are expected to significantly enhance the maintainability of machine tools, which finally leads to economic benefits and smoother production. Therefore, many authors performed extensive experiments with different sensor signals, features and components. Most of those experiments were performed on simplified test benches in order to gain genuine and distinct insights into the correlations between the recorded sensor signals and the investigated fault modes. However, in order to build the bridge between real use cases and scientific findings, those investigations have to be transferred and performed on a more complex test bench, which is close to machine tools in operation. In this paper, a condition monitoring test cycle is developed for such a test bench. The developed test cycle enables the recording of a re-producible data basis, on which models for the condition monitoring of BSDs and LGSs can be based upon.


Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1738 ◽  
Author(s):  
Ghulam Amjad Hussain ◽  
Ashraf A. Zaher ◽  
Detlef Hummes ◽  
Madia Safdar ◽  
Matti Lehtonen

Partial discharge (PD) measurements have proved their reliability for health monitoring of insulation systems in power system components including synchronous generators, power transformers, switchgear and cables etc. Online condition monitoring and pro-active detection of PD faults have been highly demanded over the last two decades. This paper provides results from a research project to develop advanced non-intrusive sensing technologies that are cost effective, reliable and efficient for early detection of PD faults in medium voltage (MV) and high voltage (HV) air-insulated switchgear. Three sensors (high frequency E-field (D-dot) sensor, Rogowski coil and loop antenna) have been developed and tested under various PD faults and their performance were evaluated in comparison with high frequency current transformer (HFCT) which is being used commercially for PD testing and measurement. Among these three sensors, it is shown that D-dot sensor and Rogowski coil are more dependable when it comes to the PD measurements due to their high signal to noise ratio and hence high accuracy. These sensors can be customized according to a specific application and can be connected together with one data acquisition device while developing an online condition monitoring system.


Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2099 ◽  
Author(s):  
Martin W. Hoffmann ◽  
Stephan Wildermuth ◽  
Ralf Gitzel ◽  
Aydin Boyaci ◽  
Jörg Gebhardt ◽  
...  

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.


2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Wenxian Yang ◽  
Chong H. Ng ◽  
Jiesheng Jiang

Increasing deployment of wind turbines requires efficient condition monitoring to ensure the safety and availability of these machines. Grid code also requests the operator to enhance the monitoring of the quality of the power generated by the wind turbine. Most commercially available wind turbine condition monitoring systems are supported by a number of vibration transducers. They consequently are complex in hardware, expensive in price, but inefficient in computation and particularly lack of power quality monitoring ability. In view of this, an innovative electrical signal analysis-based wind turbine condition and power quality monitoring technique is developed in this paper by the approach of individual harmonic extraction. The proposed technique has been verified in the lab by applying to detecting both the mechanical and electrical faults emulated on a specially designed wind turbine condition monitoring test rig. Experiments show that the proposed technique is not only sensitive to the faults, but alert to the degradation of power quality.


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