scholarly journals Heavy overload forecasting of distribution transformers based on neural network

2020 ◽  
Vol 309 ◽  
pp. 05012
Author(s):  
Haining Xie ◽  
Yingjie Tian ◽  
Wei Zhu ◽  
Zhongyu Hu
Keyword(s):  
Neural Network ◽  
Meteorological Data ◽  
New Method ◽  
Electric Load ◽  
Distribution Transformer ◽  
Operation And Maintenance ◽  
Distribution Transformers ◽  
Load Rate ◽  
Network Operation ◽  
Prediction Efficiency

The overload management is significance component in distribution network operation and maintenance to improve electricity service. According to the periodic characteristics of the electric load, this paper designs a new method to identify and predict the heavy overload states and highlight the dates where the distribution transformer most likely heavy overload through the historical load rate and meteorological data. The Attention-GRU neural network is introduced to predict electric load rate of the highlight dates to improve the prediction efficiency. In comparison with the performances traditional LSTM in prediction of distribution transformers, results show that the new method has higher accuracy and efficiency in predicting highlight dates’ load rates.

scholarly journals A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3167
Author(s):  
Lei Li ◽  
Ying Xu ◽  
Lizi Yan ◽  
Shengli Wang ◽  
Guolin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Long Range ◽  
Meteorological Data ◽  
Weather Prediction ◽  
New Method ◽  
General Regression Neural Network ◽  
Inversion Method ◽  
Tropospheric Delay ◽  
Nwp Model ◽  
General Regression

Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System’s (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.

scholarly journals Configurations, Power Topologies and Applications of Hybrid Distribution Transformers

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1215
Author(s):  
Alvaro Carreno ◽  
Marcelo Perez ◽  
Carlos Baier ◽  
Alex Huang ◽  
Sanjay Rajendran ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Power Converter ◽  
Operating Conditions ◽  
Distribution Transformer ◽  
Nonlinear Loads ◽  
Electric Vehicle Charging ◽  
Distribution Transformers ◽  
Alternative Systems ◽  
Charging Stations ◽  
Hybrid Distribution

Distribution systems are under constant stress due to their highly variable operating conditions, which jeopardize distribution transformers and lines, degrading the end-user service. Due to transformer regulation, variable loads can generate voltage profiles out of the acceptable bands recommended by grid codes, affecting the quality of service. At the same time, nonlinear loads, such as diode bridge rectifiers without power factor correction systems, generate nonlinear currents that affect the distribution transformer operation, reducing its lifetime. Variable loads can be commonly found at domiciliary levels due to the random operation of home appliances, but recently also due to electric vehicle charging stations, where the distribution transformer can cyclically vary between no-load, rated and overrated load. Thus, the distribution transformer can not safely operate under highly-dynamic and stressful conditions, requiring the support of alternative systems. Among the existing solutions, hybrid transformers, which are composed of a conventional transformer and a power converter, are an interesting alternative to cope with several power quality problems. This article is a review of the available literature about hybrid distribution transformers.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

A New Method of Rolling Prediction for Gas Emission Based on Wavelet Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 1500-1506
Author(s):  
Yu Min Pan ◽  
Xiao Yu Zhang ◽  
Peng Qian Xue
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Wavelet Neural Network ◽  
New Method ◽  
Neutral Network ◽  
Gas Emission ◽  
Sample Data ◽  
Simulation Results

A new method of rolling prediction for gas emission based on wavelet neural network is proposed in this paper. In the method, part of the sample data is selected, which length is constant, and the data is reselected as the next prediction step. Then a wavelet neutral network is adopted to prediction which input data is rolling, the sequence model of rolling prediction is thus constructed. Simulation results have proved that the method is valid and feasible.

A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

2021 ◽  
pp. 1-16
Author(s):  
Hasmat Malik ◽  
Majed A. Alotaibi ◽  
Abdulaziz Almutairi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hybrid Model ◽  
Storage System ◽  
Load Forecasting ◽  
Model Performance ◽  
Energy Storage System ◽  
Electric Load ◽  
Real Time Analysis ◽  
Model Combining

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

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