Although the gas insulated structures have a high degree of reliability, the unavoidable defects are primary reason of their failures. Partial discharge (PD) has been regarded as an effective indication for condition monitoring and diagnosis of gas insulated switchgears (GISs) to ensure their reliable and stable operation. Among various PD detection methods, the ultra-high frequency (UHF) technique has the advantages of on-line motoring and defect classification. In this paper, there are presented 7 types of artificial electrode systems fabricated for simulation of real insulation defects in gas insulated structures. A real-time measurement system was developed to acquire defect patterns in a form of phase-resolve partial discharge (PRPD) intensity graph, using a UHF sensor. Further, the discharge distribution and statistical characteristics were extracted for defect identification using a neural network algorithm. In addition, a conversion experiment was proposed by detecting the PD pulse simultaneously using a non-induction resistor and a UHF sensor. A relationship between the magnitude of UHF signal and the amplitude of apparent charge was established, which was used for evaluation of PD using the UHF sensor.
Ambient concentrations of CO, as well as NOx and O3, were measured as a part of the PARADE campaign conducted at the Taunus Observatory on the summit of the Kleiner Feldberg between the 8th of August and 9th of September 2011. These measurements were made in an effort to provide insight into the characteristics of the effects of both biogenic and anthropogenic emissions on atmospheric chemistry in the rural south-western German environment. The overall average CO concentration was found to be 100.3±18.1 ppbv (within the range of 71 to 180 ppbv), determined from 10-min averages during the summer season. The background CO concentration was estimated to be ~90 ppbv. CO and NOx showed bimodal diurnal variations with peaks in the late morning (10:00-12:00 UTC) and in the late afternoon (17:00-20:00 UTC). Strong correlations between CO and NOx indicated that vehicular emission was the major contributor to the notable CO plumes observed at the sampling site. Both local meteorology and backward trajectory analyses suggest that CO plumes were associated with anthropogenically polluted air masses transferred by an advection to the site from densely populated city sites. Furthermore, a good linear correlation of R2 = 0.54 between CO and O3 (∆O3/∆CO=0.560±0.016 ppbv/ppbv) was observed, in good agreement with previous observations
This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.
This paper presents a portable exhaled breath analyser, developed to detect selected diseases. The set-up employs resistive gas sensors: commercial MEMS sensors and prototype gas sensors made of WO3 gas sensing layers doped with various metal ingredients. The set-up can modulate the gas sensors by applying UV light to induce physical changes of the gas sensing layers. The sensors are placed in a tiny gas chamber of a volume of about 22 ml. Breath samples can be either injected or blown into the gas chamber when an additional pump is used to select the last breath phase. DC resistance and resistance fluctuations of selected sensors using separate channels are recorded by an external data acquisition board. Low-noise amplifiers with a selected gain were used together with a necessary bias circuit. The set-up monitors other atmospheric parameters interacting with the responses of resistive gas sensors (humidity, temperature, atmospheric pressure). The recorded data may be further analysed to determine optimal detection methods.
This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.
Semiconductive - resistive sensors of toxic and explosive gases were fabricated from nanograins of SnO2 using thick-.lm technology. Sensitivity, selectivityand stabilityof sensors working in di.erent temperature depend on the way the tin dioxide and additives were prepared. A construction also plays an important role. The paper presents an attitude towards the evaluation of transport of electrical charges in semiconductive grain layer of SnO2, when dangerous gases appear in the surrounding atmosphere.
A layered sensor structure of metal-free phthalocyanine H2Pc (~160 nm) with a very thin film of palladium (Pd ~20 nm) on the top, has been studied for hydrogen gas-sensing application at relatively low temperatures of about 30°C and about 40°C. The layered structure was obtained by vacuum deposition (first the phthalocyanine Pc and than the Pd film) onto a LiNbO3Y- cut Z-propagating substrate, making use of the Surface Acoustic Wave method, and additionally (in this same technological processes) onto a glass substrate with a planar microelectrode array for simultaneous monitoring of the planar resistance of the layered structure. In such a layered structure we can detect hydrogen in a medium concentration range (from 0.5 to 3% in air) even at about 30°C. At elevated temperature up to about 40°C the differential frequency increases proportionally (almost linearly) to the hydrogen concentration and the response reaches its steady state very quickly. The response times are about 18 s at the lowest 0.5% hydrogen concentration to about 42 s at 4% (defined as reaching 100% of the steady state). In the case of the investigated layered structure a very good correlation has been observed between the two utilized methods - the frequency changes in the SAW method correlate quite well with the decreases of the layered structure resistance.
In recent years organic semiconductors have been given attention in the field of active materials for gas sensor applications. In the paper the investigations of the optoelectronic sensor structure of ammonia were presented. The sensor head consists of polyaniline and Nafion layers deposited on the face of the telecommunication optical fiber. The elaborated sensor structure in the form of Fabry-Perot interferometer is of the extremely small dimension its thickness is of the order of 1 um. Many sensor structures of diffierent combinations of the polyaniline and Nafion layers were constructed and investigated. The optimal solution seems to be the structures with small number of polianiline layers (up to three).
The paper presents the results of an analysis of gaseous sensors based on a surface acoustic wave (SAW) by means of the equivalent model theory. The applied theory analyzes the response of the SAW sensor in the steady state affected by carbon monoxide (CO) in air. A thin layer of WO3 has been used as a sensor layer. The acoustical replacing impedance of the sensor layer was used, which takes into account the profile of the concentration of gas molecules in the layer. Thanks to implementing the Ingebrigtsen equation, the authors determined analytical expressions for the relative changes of the velocity of the surface acoustic wave in the steady state. The results of the analysis have shown that there is an optimum thickness of the layer of CO sensor at which the acoustoelectric effect (manifested here as a change in the acoustic wave velocity) is at its highest. The theoretical results were verified and confirmed experimentally