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نتیجه جستجو - تخلیه جزئی

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 Electrical discharges in ferrofluids based on mineral oil and novel gas-to-liquid oil
تخلیه الکتریکی در فروسیال های مبتنی بر روغن معدنی و نفت جدید گاز به مایع-2021
Ferrofluids consisting of stabilized iron oxide nanoparticles and insulating oils have emerged as a promising substitute for liquid dielectrics in electrical engineering applications. Recent enhancements of electrical insulating liquids rely on preparation of ferrofluids on emerging insulating oils available on the market. The present work reports on a comparative experimental study of insulating properties of a conventional mineral oil (MO)- based ferrofluid and a ferrofluid based on novel insulating oil produced by a gas-to-liquid (GTL) technology. The ferrofluids prepared on the two oils are subjected to rigorous experimental investigation of dielectric breakdown and partial discharges. The experiments are conducted on 4 MO-and 4 GTL-based ferrofluids with equal concentrations of magnetite nanoparticles. Measurements of partial discharges according to IEC 60270 are complemented with high frame–rate photography with positive streamer analysis. Based on the statistical analysis, it is found that MO-based ferrofluids exhibit superior breakdown performance to GTL-based ferrofluids, even though the pure GTL oil exhibits slightly higher mean breakdown voltage than the pure MO. A deeper analysis revealed a significantly greater number of partial discharges in GTL ferrofluids. The positive streamer size is lowered with increasing nanoparticle concentration only in MO-based nanofluids. Differences in the physical properties of the two oils, such as density, viscosity and permittivity, are considered in the interpretation of the different dielectric performance of the two ferrofluids. The experimental comparison leads to better understanding of the breakdown mechanism, and lay the foundations for proper selection of physical properties of a base oil for high performance insulating ferrofluids.
Keywords: Magnetic nanoparticles | Ferrofluids | Non-polar liquid | Dielectric breakdown | Partial discharge | Insulating material
مقاله انگلیسی
2 Electrical and acoustic investigation of partial discharges in two types of nanofluids
بررسی الکتریکی و صوتی تخلیه جزئی در دو نوع نانوسیال-2021
Measurements of partial discharges in liquid dielectrics are important for prediction of a serious failure of electrical equipment. Recent research findings show that application of nanoparticles in dielectric liquids may increase their resistance to partial discharges. However, physical properties of a host liquid are expected to play a decisive role in the effective suppression of partial discharges by nanoparticles. In this paper, a transformer oil prepared by a gas-to-liquid technology and a standard naphthenic transformer oil are doped with an equal amount of iron oxide nanoparticles. Determination of basic physical properties of the oils and the nanofluids is followed by an experimental investigation of partial discharges under various alternating voltage levels. The detection of partial discharges is approached by two independent methods, electrically and acoustically. Both methods revealed occurrence of partial discharges in the gasto-liquid oil at higher voltages, as compared with the naphthenic oil. Acoustic emission energy is found one order of magnitude greater in the gas-to-liquid oil than in the naphthenic oil. The acoustic wave propagation dependence on the physical properties of the liquid is considered in the qualitative explanation of the observed phenomenon. The presence of nanoparticles can suppress partial discharges in the naphthenic oil, but not in the gas-to-liquid oil.
Keywords: Liquid dielectric | Partial discharge | Ferrofluids | Transformer oil | Acoustic emission | Magnetic nanoparticles
مقاله انگلیسی
3 Truck scheduling in a multi-door cross-docking center with partial unloading : Reinforcement learning-based simulated annealing approaches
زمانبندی کامیون در یک مرکز متصل متقابل چند درب با تخلیه جزئی: رویکردهای بازپخت شبیه سازی شده مبتنی بر یادگیری تقویتی -2020
In this paper, a truck scheduling problem at a cross-docking center is investigated where inbound trucks are also used as outbound. Moreover, inbound trucks do not need to unload and reload the demand of allocated destination, i.e. they can be partially unloaded. The problem is modeled as a mixed integer program to find the optimal dock-door and destination assignments as well as the scheduling of trucks to minimize makespan. Due to model complexity, a hybrid heuristic-simulated annealing is developed. A number of generic and tailor-made neighborhood search structures are also developed to efficiently search solution space. Moreover, some reinforcement learning methods are applied to intellectually learn more suitable neighborhood search structures in different situations. Finally, the numerical study shows that partial unloading of compound trucks has a crucial impact on makespan reduction.
Keywords: Logistics | Cross docking | Truck scheduling | Simulated annealing | Reinforcement learning
مقاله انگلیسی
4 Multiple partial discharge source discrimination with multiclass support vector machines
تبعیض منبع تخلیه جزئی چنگانه با پشتیبانی ماشین بردار چند کلاسی-2016
The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses. The PRPD is a two dimensional representation of pulses that enables visual inspection but lacks discriminative power in common scenarios found in industrial environments, such as many simultaneous PD sources and low magnitude events that can be hidden below noise. The literature shows several works that complement PRPD with machine learning detectors (neural networks and support vector machines) and with more sophisticated signal representations, like statistics captured in several modalities, wavelets and other transforms, etc. These methods improve the classification accuracy but obscure the interpretation of the results. In this paper, the use of a support vector machine (SVM) operating on the power spectrum density of signals is proposed to identify different pulses what could be used in an online tool in the maintenance decision-making of the utility. Particularly, the approach is based on an SVM endowed with a special kernel that operates in the frequency domain. The SVM is previously trained with pulses of different PD types (internal, surface and corona) and noise that are obtained with several test objects in the laboratory. The experimental results demonstrate that this technique is highly effective in identifying PD for cases where several sources are active or when the noise level is high. Thus, the early identification of critical events with this approach during normal operation of the equipment will help in the decision of decommissioning the asset with reduced costs and low impact to the grid reliability.
Keywords: Support vector machine | Partial discharges | Electric maintenance | Machine learning | Condition monitoring | Risk assessment
مقاله انگلیسی
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