Optimization of Contact Structure among Main Transformers Considering Unit Power Supply Capacity Cost

2013 ◽  
Vol 805-806 ◽  
pp. 788-792
Author(s):  
Shao Yun Ge ◽  
Kai Zhang ◽  
Hong Liu
Keyword(s):  
Power Supply ◽  
Contact Structure ◽  
Rapid Development ◽  
Target Function ◽  
Branch Length ◽  
Unit Power ◽  
Load Demand ◽  
Proposed Model ◽  
Supply Capacity ◽  
Capacity Cost

In allusion to the problems of corridor scarcity and substation layout during the rapid development of urban economy, a model and method of distribution contact structure optimization was proposed based on power supply capacity. Aimed at satisfying the area load demand, simplifying contact channel, reducing construction cost, firstly, on the base of clearing the concepts of power supply capacity and distribution contact, the calculation method of power supply capacity is established. Furthermore, a model with a target function of unit power supply capacity cost minimum considering some specific bounds of contact branch length, area load demand, the number of main transformer contact channel is built. Finally, the modified genetic algorithm is used to solve the model. The validity and effectiveness of the proposed model and method is verified through the analysis of practical example.

scholarly journals Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Xudong He ◽  
Jian Wang ◽  
Jiqiang Liu ◽  
Enze Yuan ◽  
Kailun Wang ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Supply ◽  
Rapid Development ◽  
Good Effect ◽  
Smart Meter ◽  
Human Beings ◽  
Labor Costs ◽  
Time Consumption ◽  
Decentralized Management ◽  
Supply Capacity

The rapid development of the smart grid brings convenience to human beings. It enables users to know the real-time power supply capacity, the power quality, and the electricity price fluctuation of the grid. However, there are still some threats in the smart grid, which increase all kinds of expenses in the grid and cause great trouble to energy distribution. Among them, the man-made nontechnical loss (NTL) problem is particularly prominent. Recently, there are also some NTL detection programs. However, most of the schemes need huge amounts of supporting data and high labor costs. As a result, the NTL problem has not been well solved. In order to better avoid these risks, problems such as tampering of smart meter energy data, bypassing the smart meter directly connected to the grid, and imbalance between revenue and expenditure of the smart grid are tackled, and the threat scene of NTL is constructed. A hierarchical grid gateway blockchain is proposed and designed, and a new decentralized management MDMS system is constructed. The intelligent contract combined with the elliptic curve encryption technology is used to detect the storage and the acquisition of power data, and the detection of NTL problems is realized. At the same time, it has a certain ability to resist attacks such as replay, monitoring, and tampering. We tested the time consumption and throughput of this method on Hyperledger Fabric. At the same time, eight indexes of other methods proposed in the literature are compared. This method has a good effect.

scholarly journals Optimal Scheduling of Island Microgrid with Seawater-Pumped Storage Station and Renewable Energy

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 737
Author(s):  
Ning Liang ◽  
Pengcheng Li ◽  
Zhijian Liu ◽  
Qi Song ◽  
Linlin Luo
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Power Supply ◽  
Rapid Development ◽  
Operational Reliability ◽  
Optimal Scheduling ◽  
Diesel Generator ◽  
Storage Station ◽  
Proposed Model ◽  
Pumped Storage

The rapid development of renewable energy, represented by wind and photovoltaic, provides a new solution for island power supplies. However, due to the intermittent and random nature of renewable energy, a microgrid needs energy-storage components to stabilize its power supply when coupled with them. The emergence of seawater-pumped storage stations provides a new method to offset the shortage of island power supply. In this study, an optimal scheduling of island microgrid is proposed, which uses seawater-pumped storage station as the energy storage equipment to cooperate with wind, photovoltaic and diesel generator. First, a mathematic formulation of seawater-pumped storage station with renewable energy is presented. Then, to reach the goal of economic dispatch, an optimal scheduling model of island microgrid is established with the consideration of both respective operation constraints and island load requirements. Finally, the effectiveness of the proposed model is verified by an island microgrid over two typical seasons. The simulation results show that the proposed framework not only increases the usage of renewable energy, but also improves the operational reliability and economy of island microgrids.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

scholarly journals Economic Load Dispatch using Lambda Iteration, Particle Swarm Optimization & Genetic Algorithm

2021 ◽  
Vol 9 (8) ◽  
pp. 972-977
Author(s):  
Zaineb Nisar Jan
Keyword(s):  
Genetic Algorithm ◽  
Power Supply ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Load Demand ◽  
Reduced Costs ◽  
Reliable Power Supply ◽  
Fuel Costs

Abstract: In economic load dispatch problem scheduling of loads is done in order to achieve reliable power supply with reduced costs. With the increase in load demand with each passing year energy crisis is increasing hence an area of study where fuel costs can be reduced was proposed. This can be achieved using various methods among which the methods discussed in this paper are Lambda Iteration, Particle Swarm Operation and Genetic Algorithm. Based on numerical results the best optimization technique can be figured out among the discussed methods. Keywords: Lambda Iteration, PSO, GA, economic load dispatch, optimization solutions

scholarly journals DOWNHOLE REACTIVE POWER COMPENSATION

2015 ◽  
pp. 29-33
Author(s):  
V. A. Kopyrin ◽  
V. A. Iordan ◽  
O. V. Smirnov
Keyword(s):  
Centrifugal Pump ◽  
Power Supply ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Pump Unit ◽  
Unit Power ◽  
Transformer Substation ◽  
Electrical Centrifugal Pump

The authors provide a method for compensation of the reactive power inside a well. In the environment Matlab/ Simylink a model was developed of the site of the electrical centrifugal pump unit power supply from the transformer substation. A comparison is made of the proposed method of downhole reactive power compensation with the existing method.

scholarly journals A TWO-CHANNEL MODEL FOR REPRESENTATION LEARNING IN VIETNAMESE SENTIMENT CLASSIFICATION PROBLEM

2020 ◽  
Vol 36 (4) ◽  
pp. 305-323
Author(s):  
Quan Hoang Nguyen ◽  
Ly Vu ◽  
Quang Uy Nguyen
Keyword(s):  
Channel Model ◽  
Rapid Development ◽  
Representation Learning ◽  
Classification Problem ◽  
Sentiment Classification ◽  
Complex Sentences ◽  
Digital World ◽  
Part Of Speech ◽  
Proposed Model ◽  
Important Research Topic

Sentiment classification (SC) aims to determine whether a document conveys a positive or negative opinion. Due to the rapid development of the digital world, SC has become an important research topic that affects many aspects of our life. In SC based on machine learning, the representation of the document strongly influences on its accuracy. Word Embedding (WE)-based techniques, i.e., Word2vec techniques, are proved to be beneficial techniques to the SC problem. However, Word2vec is often not enough to represent the semantic of documents with complex sentences of Vietnamese. In this paper, we propose a new representation learning model called a \textbf{two-channel vector} to learn a higher-level feature of a document in SC. Our model uses two neural networks to learn the semantic feature, i.e., Word2vec and the syntactic feature, i.e., Part of Speech tag (POS). Two features are then combined and input to a \textit{Softmax} function to make the final classification. We carry out intensive experiments on $4$ recent Vietnamese sentiment datasets to evaluate the performance of the proposed architecture. The experimental results demonstrate that the proposed model can significantly enhance the accuracy of SC problems compared to two single models and a state-of-the-art ensemble method.

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