scholarly journals Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 657
Author(s):  
Sherif S. M. Ghoneim ◽  
Sobhy S. Dessouky ◽  
Ahmed Boubakeur ◽  
Adel A. Elfaraskoury ◽  
Ahmed B. Abou Sharaf ◽  
...  
Keyword(s):  
Power Systems ◽  
Prediction Model ◽  
Breakdown Voltage ◽  
Box Behnken Design ◽  
Barrier Materials ◽  
Insulating Oil ◽  
Barrier Effects ◽  
Hole Radius ◽  
Barrier Parameter ◽  
Inclined Angle

In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.

scholarly journals Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Sherif S. M. Ghoneim
Keyword(s):  
Prediction Model ◽  
Power Flow ◽  
Degree Of Polymerization ◽  
Classification Techniques ◽  
Aging Rate ◽  
Insulating Oil ◽  
Insulating Paper ◽  
Key Indicator ◽  
Carbon Dioxide Co2

The continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy.

scholarly journals Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

2019 ◽  
Vol 9 (20) ◽  
pp. 4417 ◽  
Author(s):  
Sana Mujeeb ◽  
Turki Ali Alghamdi ◽  
Sameeh Ullah ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Accurate Estimation ◽  
Wind Power Forecasting ◽  
Power Forecasting

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

scholarly journals Fabrication of Fe3O4@SiO2 Nanofluids with High Breakdown Voltage and Low Dielectric Loss

Coatings ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 716
Author(s):  
Bin Du ◽  
Yu Shi ◽  
Qian Liu
Keyword(s):  
Dielectric Loss ◽  
Breakdown Voltage ◽  
Shell Thickness ◽  
Electrical Breakdown ◽  
Sio2 Nanoparticles ◽  
Electrical Equipment ◽  
Insulating Oil ◽  
Low Dielectric ◽  
Sio2 Shell ◽  
High Dielectric

Insulating oil modified by nanoparticle (often called nanofluids) has recently drawn considerable attention, especially concerning the improvement of electrical breakdown and thermal conductivity of the nanofluids. However, traditional insulating nanofluid often tends to high dielectric loss, which accelerates the ageing of nanofluids and limits its application in electrical equipment. In this paper, three core-shell Fe3O4@SiO2 nanoparticles with different SiO2 shell thickness were prepared and subsequently dispersed into insulating oil to achieve nanofluids. The dispersion stability, breakdown voltages and dielectric properties of these nanofluids were comparatively investigated. Experimental results show the alternating current (AC) and positive lightning breakdown voltage of nanofluids increased by 30.5% and 61%, respectively. Moreover, the SiO2 shell thickness of Fe3O4@SiO2 nanoparticle had significant effects on the dielectric loss of nanofluids.

Based on Time Series Prediction of Photovoltaic Power Plant Output

2011 ◽  
Vol 383-390 ◽  
pp. 5142-5147
Author(s):  
Wei Guo Li ◽  
Zhi Min Liao ◽  
Xue Lin Sun
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Prediction Model ◽  
Time Series Prediction ◽  
System Capacity ◽  
Stable Operation ◽  
Power Prediction ◽  
Pv System ◽  
Test And Evaluation ◽  
System Output

With the PV power system capacity continues to expand, PV power generation forecasting techniques can reduce the PV system output power of randomness, it has great impact on power systems. This paper presents a method based on ARMA time series power prediction model. With historical electricity data and meteorological factors, the model gets test and evaluation by Eviews software. Results indicated that the prediction model has high accuracy, it can solve the shortcomings of PV randomness and also can improve the ability of the stable operation of the system.

scholarly journals Effect of repeated electrical breakdowns on mineral and natural ester insulating oils

2021 ◽  
Vol 10 (6) ◽  
pp. 2989-2996
Author(s):  
Sharin Ab Ghani ◽  
Mohd Shahril Ahmad Khiar ◽  
Imran Sutan Chairul ◽  
Muhammad Imran Zamir
Keyword(s):  
Breakdown Voltage ◽  
Electrical Discharge ◽  
Electrical Breakdown ◽  
Standard Test ◽  
Test Method ◽  
Power Transformers ◽  
Insulating Oil ◽  
Different Types ◽  
Increasing Demand ◽  
Natural Ester

Transformer insulating oils are exposed to repeated electrical discharge or breakdowns inside power transformers. Durability tests are conducted to analyze the ability of oil to resist decomposition due to such high electrical stresses. With the increasing demand for alternative insulating oils for oil-immersed transformers, it is worthy to compare the performance of different types of insulating oils (conventional mineral-based insulating oil and natural ester-based insulating oil) under repeated electrical breakdown. In this paper, the AC breakdown voltage of different mineral-based and natural ester-based insulating oils is reported. Durability tests were conducted based on the AC breakdown voltage behavior of insulating oils after 50 electrical breakdown shots. The AC breakdown voltage of each insulating oil sample was assessed according to the ASTM D1816 standard test method. Based on the results, it can be concluded that the dissimilarity in chemical composition of the insulating oils has a significant effect on the AC breakdown voltage behavior of these oils under repeated electrical breakdowns.

scholarly journals Influence of Copper Particles on Breakdown Voltage and Frequency-Dependent Dielectric Property of Vegetable Insulating Oil

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 938 ◽  
Author(s):  
Jing Zhang ◽  
Feipeng Wang ◽  
Jian Li ◽  
Hehuan Ran ◽  
Dali Huang
Keyword(s):  
Dielectric Property ◽  
Breakdown Voltage ◽  
Frequency Dependent ◽  
Copper Particles ◽  
Insulating Oil

scholarly journals A Promising Nano-Insulating-Oil for Industrial Application: Electrical Properties and Modification Mechanism

Nanomaterials ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 788 ◽  
Author(s):  
Jiaqi Chen ◽  
Potao Sun ◽  
Wenxia Sima ◽  
Qianqiu Shao ◽  
Lian Ye ◽  
...  
Keyword(s):  
Breakdown Voltage ◽  
Industrial Application ◽  
Breakdown Strength ◽  
Absorption Capacity ◽  
Photon Absorption ◽  
Electrical Performance ◽  
Test Results ◽  
Strong Electron ◽  
Insulating Oil ◽  
Lightning Impulse

Despite being discovered more than 20 years ago, nanofluids still cannot be used in the power industry. The fundamental reason is that nano-insulating oil has poor stability, and its electrical performance decreases under negative impulse voltage. We found that C60 nanoparticles can maintain long-term stability in insulating oil without surface modification. C60 has strong electronegativity and photon absorption ability, which can comprehensively improve the electrical performance of insulating oil. This finding has great significance for the industrial application of nano-insulating oil. In this study, six concentrations of nano-C60 modified insulating oil (CMIO) were prepared, and their breakdown strength and dielectric properties were tested. The thermally stimulated current (TSC) curves of fresh oil (FO) and CMIO were experimentally determined. The test results indicate that C60 nanoparticles can simultaneously improve the positive and negative lightning impulse and power frequency breakdown voltage of insulating oil, while hardly increasing dielectric loss. At 150 mg/L, the positive and negative lightning impulse breakdown voltages of CMIO increased by 7.51% and 8.33%, respectively, compared with those of FO. The AC average breakdown voltage reached its peak (18.0% higher compared with FO) at a CMIO concentration of 200 mg/L. Based on the test results and the special properties of C60, we believe that changes in the trap parameters, the strong electron capture ability of C60, and the absorption capacity of C60 for photons enhanced the breakdown performance of insulating oil by C60 nanoparticles.

Tension Prediction Model of Ice-Coated Transmission Line Based on YULE-WALK Auto-Regressive Model and Support Vector Machine

2013 ◽  
Vol 437 ◽  
pp. 331-334
Author(s):  
Lei Yang ◽  
Da Da Wang ◽  
Xin Wu ◽  
Lin Li ◽  
Xiao Ming Rui ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Prediction Model ◽  
Transmission Line ◽  
Break Line ◽  
Support Vector ◽  
Absolute Maximum ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model

Large tension of ice-coated transmission line will cause line overload and conductor galloping, accidents such as break line and tower collapse will be caused, it bring great threat to safety and stability of power systems. Therefore, there is an important physical meaning for preventing above accidents to in-depth study tension prediction model of ice-coated transmission line.In this paper,we establishes a tension prediction model of ice-coated transmission line based on the Yule-Wake auto-regressive model and support vector machine, the model contains the micrometeorological and tension historical data, etc. Through studying the tension prediction of Gan-Zhen 155# transmission line in Zhaotong area of Yunnan province,it shows the prediction obtained by this model in the next eight hours is in accord with the actual monitoring data pretty well, the absolute maximum error is less than 5.86%, and the maximum absolute mean error is less than 2.74%.So, the feasibility and accuracy of this model is verified.

Dielectric Strength, Breakdown Voltage and Acidity Test with Respect to Transformer Insulating Oil

2009 ◽  
Vol 62-64 ◽  
pp. 120-125 ◽  
Author(s):  
I.A. Adejumobi
Keyword(s):  
Breakdown Voltage ◽  
Economic Impacts ◽  
Dielectric Strength ◽  
Qualitative Assessment ◽  
Oil Degradation ◽  
British Standard ◽  
Insulating Oil ◽  
Distribution Transformers ◽  
Experimental Values ◽  
Insulation Oil

This paper presented the qualitative assessment of transformer insulating oil. The breakdown voltage, dielectric and acidity tests were electrically and chemically carried out on sixteen samples of transformer insulating oil collected from various serving distribution transformers in Ilorin Metropolis in Nigeria, through the supply authority. The adequacy of the obtained results was determined by comparing experimental values with America Society for Testing and British Standard (BTA4705) pre-requisites. About seventy five percent (75%) of the tested samples failed at least one of the tests, indicating inadequacy in the routine checks. Economic impacts of the obtained results and major causes and prevention of insulation oil degradation were also presented.

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