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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 |