Paul Gregor Nikolic

Investigations on the dielectric strength of carbon dioxide and carbon dioxide mixtures for the application in gas insulated switchgear

The ongoing debate on climate change and emission restrictions for greenhouse gases also forces the search for SF6 alternatives (sulphur hexafluoride) in the energy transmission and distribution sector. Hence several challenges for the asset manufactures result as changing the insulating medium implies a redesign of the switchgear and its components. Here one effect in gas insulated switchgear is the reduction of the dielectric strength in case of contamination of the gas compartments with metal particles. In this paper the dielectric strength of the promising SF6 substitute CO2 (carbon dioxide) and its mixtures with non-toxic gases under particle contamination is investigated. For this purpose an arrangement designed according to a real gas insulated switchgear is used. From the comparison of the experimental investigations results a statement on the dielectric strength of CO2 under non-uniform field conditions and on the enhancement of this dielectric strength by the admixture of additional gaseous components. These results can serve as a means for the dielectric design of a SF6-free gas insulated switchgear.

Non-invasive diagnosis of SF6 high voltage Selfblast circuit breaker nozzles

The maintenance of high voltage gas circuit breakers requires high personal as well as monetary efforts for the asset operator. Furthermore, a faulty reassembly of a circuit breaker during maintenance can lead to a circuit breaker failure during operation. One possibility to reduce those efforts as well as the failure risk is the application of non-invasive diagnostic techniques. This research work examines such a non-invasive technique for assessing the wear of the insulating nozzle inside the switching chamber of a circuit breaker. The approach applied is based on the measuring of the transient pressure signal at the gas connector of the circuit breaker during a switching operation without electrical load. The pressure signal is investigated regarding characteristically features which yield information for the determination of the condition of the switching chamber. This contribution addresses two different approaches for the evaluation of the measured pressure signals: firstly a comparative analysis of the signals and secondly an evaluation based on machine learning algorithms. Both of them use a dataset containing measurements with different variations of the nozzle shape representing the wear of the nozzle. In the first approach, the characteristics of measurements with different nozzle diameters are compared and assessed with regard to deviations of the characteristics. In the second approach, machine learning algorithms are used to automatically scan for dependencies in the given dataset obtained from the measurement data. Applied on the results of exemplary tests, both approaches provide a clear indication with regard to a prediction of the nozzle wear and show a proof of concept for the new non-invasive diagnostic technique. Taking into account a large database from field tests planned in the future, especially the machine learning algorithms can show a valuable benefit for the application of this test measure during circuit breaker maintenance.

Minimum-intrusive diagnostic system for SF 6 high voltage selfblast circuit breaker nozzles

Circuit breakers are important components in the electrical power supply grid. The maintenance of high voltage gas circuit breakers requires high personnel as well as monetary efforts for the asset operator. Furthermore, a faulty reassembly of the circuit breaker during maintenance can lead to a circuit breaker failure during operation. For this reason the maintenance strategy changed from a periodic schedule to a condition-based strategy. One option to realize condition-based maintenance strategies and to reduce the failure risk is the use of minimum-intrusive diagnostic techniques. This research work examines such techniques for assessing the wear of the insulation nozzle inside the switching chamber of a circuit breaker. The approach applied here is based on the measurement of the transient pressure signal at the main filling valve of the circuit breaker during a switching operation without electrical load. The pressure signal is investigated regarding characteristic features which yield information for the determination of the switching chamber condition. Characteristics are identified in the pressure waveform and are used for further analysis. In this process the nozzle condition as the most influencing factor is varied. Additionally, the influence of electrode ablation and filling pressure variations is analyzed as well. The nozzle ablation on multiple poles is considered. A machine learning algorithm applying the k-Nearest-Neighbor-method is used for the determination of the nozzle and electrode condition, while the characteristic features are utilized as input parameters. Thus it is possible to classify new unknown measurements with an already known data basis. The classification is successfully applied with and highly reliable for different circuit breaker types. For the validation of the method field measurements from different circuit breaker types are evaluated.

Experimental study of a helical coil operating mechanism for future switchgear applications

A key challenge for future switching technologies, especially in the context of the substitution of the strong greenhouse gas sulphur hexafluoride and the ongoing development of DC grids, is the increase of the contact opening velocity within switching devices. Thus, in this contribution a compact helical coil operating mechanism is developed and first experimental investigations are performed to characterize the accelerating and breaking performance of the mechanism. The helical coil arrangement is supplied by currents up to values of several kilo-amperes and the movement of the mechanism is analyzed.

Investigations on the influence of the switching chamber design on the thermal interruption performance of carbon dioxide in high voltage circuit breakers

The search for alternatives for the strong greenhouse gas SF6 (sulphur hexafluoride) in high voltage gas insulated switchgear (GIS) and circuit breakers lead to the identification of CO2 (carbon dioxide) as possible substitute for use as quenching gas. Nevertheless the first prototypes of circuit breakers filled with CO2 up to rated voltages of Ur = 145 kV show that an optimization of the CO2 technology with regard to the size of the switching chamber and the energy of the operating mechanism is needed. This requires a deep understanding of the interruption process in CO2 and an identification of the main influencing factors on the interruption performance. For this purpose the thermal interruption performance of two model switching chambers - optimized for the use of CO2 and SF6 - is experimentally investigated and compared. From the comparison with CFD (computational fluid dynamics) simulation results, gas temperature and flow velocity are determined as main influencing parameters on the interruption performance.

Modeling of the dielectric recovery of hot air in insulating nozzles

In standard high voltage gas circuit breakers -important safety elements in todays power grids-, a small surface layer of polytetrafluoroethylene (PTFE) vaporizes at the high temperatures occurring in the arcing zone during the high current phase of the interruption process. This ablation continues even some hundred microseconds after the arc has been quenched and it actually changes the total gas composition as well as the temperature profile in the arcing zone. Therefore it delays the fast cooling of the arcing zone, which is necessary to prevent dielectric failures. The experimental determination of the dielectric recovery of hot air in insulating nozzles was investigated previously with focus on the substitution of SF6 (sulfur hexafluoride) in circuit breakers for future power grids. The present investigation gives a physical model for the calculation of the previously measured recovery characteristics. CFD-simulations (computational fluid dynamics) are performed for the determination of the relevant physical properties of the decaying quenching gas (density, temperature etc.). These properties serve as input parameters for the developed model. Based on the gas properties resulting from the CFD-simulations it is possible to calculate the effective ionization coefficients and thus the breakdown voltage applying the streamer criterion. Afterwards the calculated breakdown voltages are compared to the measured recovery characteristics. The comparison shows a good agreement between the measurements and the calculation.