scholarly journals Aggregation Method of Distributed Load Resources Based on Non-intrusive Load Identification

2021 ◽  
Vol 2138 (1) ◽  
pp. 012004
Author(s):  
Wei Liu ◽  
Chaoliang Wang ◽  
Yilong Li
Keyword(s):  
Power System ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Power Grid ◽  
Electrical Equipment ◽  
Image Features ◽  
Load Identification ◽  
Aggregation Method ◽  
Efficient Operation ◽  
Deep Learning Algorithm

Abstract Because the power system contains a large number of user-side adjustable load resources, it can effectively enhance the operational flexibility of the power system and realize the safe, economical and efficient operation of the power grid by aggregating and modeling all kinds of resources and participating in the interactive response of the system as a whole. In this paper, a user-side adjustable load resource aggregation method based on non-intrusive load identification is proposed, which aims to obtain the load response potential of various users without intruding into the users, thus providing important support for power grid dispatching. Specifically, starting from the basic attributes of electrical equipment, considering the influence of numerical features such as current, harmonics, power, and V-I trajectory image features on load identification, the deep learning algorithm is used to deeply fuse the numerical features and image features in high-dimensional space, and then the fused advanced features are supervised by the Softmax classification algorithm, so as to effectively identify different types of electrical equipment. Finally, a bottom-up aggregation strategy is adopted to aggregate and model all kinds of load resources under the same station, so as to realize the accurate evaluation of the response ability of station resources. The simulation results of a numerical example verify the correctness and effectiveness of the proposed method.

scholarly journals Reinforcement based Multi-Model Deep Learning Algorithm for Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 1704-1710
Keyword(s):  
Deep Learning ◽  
Maximum Entropy ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Image Data ◽  
Space Policy ◽  
Model Classification ◽  
Deep Learning Algorithm ◽  
Robust Model ◽  
Neural Network Algorithm

Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent's action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

scholarly journals Classification of Magnetic Tile Surface Defects Based on Efficientnet Network with Attention

2021 ◽  
Vol 2137 (1) ◽  
pp. 012062
Author(s):  
Chengshuai Fan
Keyword(s):  
Classification Accuracy ◽  
Surface Defects ◽  
Learning Algorithm ◽  
Complex Surface ◽  
Detection Algorithm ◽  
Image Features ◽  
Low Contrast ◽  
Deep Learning Algorithm ◽  
Model Size

Abstract The magnetic tile image has the characteristics of uneven illumination, complex surface texture, and low contrast. Aiming at the problem that the traditional defect detection algorithm is difficult to accurately identify the defects, and the deep learning algorithm is difficult to balance the classification accuracy and the size of the speed model, a defect classification algorithm based on attention-based EfficientNet is proposed. The algorithm first enhances the network’s spatial and location information for image features by integrating the Convolutional Block Attention Module, and improves the network’s ability to identify defects. Then, on this basis, Criss-Cross Attention is added to the network, so that the network can better the context information of the horizontal and vertical cross of image features, so that each pixel can finally capture the full image dependency of all pixels. Experimental results show that the algorithm has higher classification accuracy than EfficientNet-B0, reached 99.11%, and has a better balance between accuracy, speed and model size than other classification models.

Analysis and Prevention of Grid Over-Voltage Ferromagnetic Resonance

2014 ◽  
Vol 960-961 ◽  
pp. 929-934
Author(s):  
Ming Jun Lv ◽  
Xin Zhao ◽  
Xiang Dong Zhao ◽  
Jian Guo Liu ◽  
Feng Zhen Liu ◽  
...  
Keyword(s):  
Power System ◽  
Ferromagnetic Resonance ◽  
Preventive Measures ◽  
Power Grid ◽  
Electrical Equipment ◽  
Normal Operation ◽  
Stable Operation ◽  
Power Outages ◽  
The Future ◽  
Great Harm

Over-voltage in the power system can be caused by a lot of reasons , including higher frequency ferromagnetic resonance overvoltage which occurs in normal operation and causes great harm. Overvoltage events often result in damages to electrical equipment or even power outages . In this paper,ferromagnetic resonance is analyzed to study harm , causes, conditions , and phenomena and to handle resonance and develop practical preventive measures. The related analysis is important to work for the future operation of the power grid to prevent and limit the ferromagnetic resonance over voltage which provides some help to further ensure the safety and stable operation of the power grid .

scholarly journals Fault diagnosis of motor bearing based on deep learning

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987562 ◽  
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Liang He ◽  
Yang Zhao ◽  
Xiao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Input Data ◽  
Learning Algorithm ◽  
Random Noise ◽  
Thermal Power ◽  
Efficient Operation ◽  
Deep Learning Algorithm ◽  
Motor Bearing

The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.

scholarly journals Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

2021 ◽  
Vol 680 (1) ◽  
pp. 553-567
Author(s):  
Pandia Rajan Jeyaraj ◽  
Aravind Chellachi Kathiresan ◽  
Siva Prakash Asokan ◽  
Edward Rajan Samuel Nadar ◽  
Hegazy Rezk ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Learning Algorithm ◽  
Wide Area ◽  
Deep Learning Algorithm ◽  
Wide Area Control

Deep learning and signal processing based algorithm for autorecognition of harmonic loads

2021 ◽  
pp. 1-14
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power System ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Nonlinear Loads ◽  
Nonlinear Load ◽  
Interconnected Power System ◽  
Time Frequency ◽  
Deep Learning Algorithm

A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%.

scholarly journals Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Qiang Wang ◽  
Dong Liu ◽  
Guangheng Liu
Keyword(s):  
Deep Learning ◽  
Pregnant Women ◽  
Uterine Artery ◽  
Learning Algorithm ◽  
Perinatal Outcomes ◽  
Image Features ◽  
Severe Preeclampsia ◽  
Control Group ◽  
Observation Group ◽  
Deep Learning Algorithm

This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F 1 -score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant ( P < 0.05 ). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid ( P < 0.05 ). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.

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