Operation Planning Method Using Convolutional Neural Network for Combined Heat and Power System

2021 ◽  
Author(s):  
Tetsushi Ono ◽  
Tsutomu Kawamura ◽  
Ryosuke Nakamura
Keyword(s):  
Neural Network ◽  
Power System ◽  
Convolutional Neural Network ◽  
Combined Heat And Power ◽  
Planning Method ◽  
Operation Planning

scholarly journals Representational learning approach for power system transient stability assessment based on convolutional neural network

2017 ◽  
Vol 2017 (13) ◽  
pp. 1847-1850 ◽  
Author(s):  
Bendong Tan ◽  
Jun Yang ◽  
Xueli Pan ◽  
Jun Li ◽  
Peiyuan Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Power System ◽  
Convolutional Neural Network ◽  
Transient Stability ◽  
Learning Approach ◽  
Stability Assessment

Week-ahead Forecasting of Household Energy Consumption Using CNN and Multivariate Data

2021 ◽  
Vol 20 ◽  
pp. 182-188
Author(s):  
Vanita Agrawal ◽  
Pradyut K. Goswami ◽  
Kandarpa K. Sarma
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power System ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Smart Grids ◽  
Smart Meters ◽  
Household Energy ◽  
Household Energy Consumption

Short-Term Load Forecasting for buildings has gained a lot of importance in recent times due to the ongoing penetration of renewable energy and the upgradation of power system networks to Smart Grids embedded with smart meters. Power System expansion is not able to keep pace with the energy consumption demands. In this scenario, accurate household energy forecasting is one of the key solutions to managing the demand side energy. Even a small percentage of improvement in forecasting error, translates to a lot of saving for both producers and consumers. In this paper, it was found out that Aggregated 1-Dimensional Convolutional Neural Networks can be effectively modeled to predict the household consumption with greater accuracy than a basic 1-Dimensional Convolutional Neural Network model or a classical Auto Regressive Integrated Moving Average model. The proposed Aggregated Convolutional Neural Network model was tested on a 4 year household energy consumption dataset and gave very promising Root Mean Square Error reduction

scholarly journals Partial Discharge Pattern Recognition of Transformers Based on MobileNets Convolutional Neural Network

2021 ◽  
Vol 11 (15) ◽  
pp. 6984
Author(s):  
Yuanyuan Sun ◽  
Shuo Ma ◽  
Shengya Sun ◽  
Ping Liu ◽  
Lina Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Power System ◽  
Convolutional Neural Network ◽  
Oil And Gas ◽  
Recognition Accuracy ◽  
Partial Discharge ◽  
Nonlinear Function ◽  
Classification Performance ◽  
Feature Expression

The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified ın a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.

Convolutional neural network-based power system transient stability assessment and instability mode prediction

2020 ◽  
Vol 263 ◽  
pp. 114586 ◽  
Author(s):  
Zhongtuo Shi ◽  
Wei Yao ◽  
Lingkang Zeng ◽  
Jianfeng Wen ◽  
Jiakun Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power System ◽  
Convolutional Neural Network ◽  
Transient Stability ◽  
Instability Mode ◽  
Stability Assessment

scholarly journals Power System Event Classification and Localization Using a Convolutional Neural Network

2020 ◽  
Vol 8 ◽  
Author(s):  
Huiying Ren ◽  
Z. Jason Hou ◽  
Bharat Vyakaranam ◽  
Heng Wang ◽  
Pavel Etingov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power System ◽  
Convolutional Neural Network ◽  
Situation Awareness ◽  
Training Data ◽  
Polar Coordinate System ◽  
Data Set ◽  
Simulation Based ◽  
Adequate Data

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

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