Prediction and Analysis on Short-term Load of Power System Based on LSTM

Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions,make outstanding contributions to delaying global climate warming,and is conducive to global environmental protection and sustainable development.On the short-term load forecasting of power system,a variant model of RNN-LSTM is tested in this paper.It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN.On the basis of this model,optimization experiments are carried out under different super parameters to achieve better prediction results.The experimental results show that the accuracy of test set reaches 99.8%,which proves that the method proposed in this paper has certain reference value.

Sentiment Analysis of Short Texts Based on Parallel DenseNet

Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.

Research on Electronic Document Management System Based on Cloud Computing

With the development of information technology,cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion,popularization and vigorous development of e-government and e-commerce,the number of documents in electronic form is getting larger and larger.Electronic document is an indispensable main tool and real record of e-government and business activities.How to scientifically and effectively manage electronic documents?This is an important issue faced by governments and enterprises in improving management efficiency,protecting state secrets or business secrets,and reducing management costs.This paper discusses the application of cloud computing technology in the construction of electronic file management system,proposes an architecture of electronic file management system based on cloud computing,and makes a more detailed discussion on key technologies and implementation.The electronic file management system is built on the cloud architecture to enable users to upload,download,share,set security roles,audit,and retrieve files based on multiple modes.An electronic file management system based on cloud computing can make full use of cloud storage,cloud security,and cloud computing technologies to achieve unified,reliable,and secure management of electronic files.

The equivalent model of controller in synchronous frame to stationary frame

The system controlled in synchronous frame is commonly used. However, it is a problem how to transform the controller in synchronous frame to stationary frame. This paper deduces the stationary frame equivalent model of arbitrarily controller in synchronous frame. The equivalent model can reflect the control performance of the input signal at different frequency accurately. The unified frequency-domain model of the overall system can be established using the equivalent model, and the guidance for frequency analysis and stability analysis can be provided. Theoretical derivation and simulation results verify the correctness and generality of the equivalent model.

Real-time transient stability assessment in power system based on improved SVM

Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.

A Missing Power Data Filling Method Based on Improved Random Forest Algorithm

Missing data filling is a key step in power big data preprocessing,which helps to improve the quality and the utilization of electric power data.Due to the limitations of the traditional methods of filling missing data,an improved random forest filling algorithm is proposed.As a result of the horizontal and vertical directions of the electric power data are based on the characteristics of time series.Therefore,the method of improved random forest filling missing data combines the methods of linear interpolation,matrix combination and matrix transposition to solve the problem of filling large amount of electric power missing data.The filling results show that the improved random forest filling algorithm is applicable to filling electric power data in various missing forms.What’s more,the accuracy of the filling results is high and the stability of the model is strong,which is beneficial in improving the quality of electric power data.

Inhibition roles of molybdate and borate on Q235 steel corrosion in resistance reducing agent

The effects of Na_(2)MoO_(4) and Na_(2)B_(4)O_(7) on corrosion behavior of Q235 steel in resistance reducing agent(RRA)containing sodium bentonite were studied by mass loss,scanning electron microscopy and electrochemical measurement.The results showed that both the independent and mixed additions of Na_(2)MoO_(4) and/or Na_(2)B_(4)O_(7),can reduce the corrosion rate of Q235 steel in RRA containing sodium bentonite.And the inhibition effect of Na_(2)MoO_(4) and/or Na_(2)B_(4)O_(7)increased with their dosage increase.With the same dosage,the inhibition efficiency of the mixed addition of Na_(2)MoO_(4) and Na_(2)B_(4)O_(7),was higher than that of their independent addition.The passivation effect of Q235 steel was easy to obtain in the RRA with the mixed addition of Na_(2)MoO_(4) and Na_(2)B_(4)O_(7).The optimized inhibitor for the RRA containing sodium bentonite was the mixture of Na_(2)B_(4)O_(7) and Na_(2)MoO_(4) with a total concentration of 1.5 wt.%.Furthermore,the increase in corrosion potential E_(corr) and the decrease in corrosion current density i_(cor) in carbon steel were one of the important criteria for the formation of passivation film.

DC Current Order Optimization Based Strategy for Recovery Performance Improvement of LCC-HVDC Transmission Systems

For the safe and fast recovery of line commutated converter based high-voltage direct current(LCC-HVDC)transmission systems after faults,a DC current order optimization based strategy is proposed.Considering the constraint of electric and control quantities,the DC current order with the maximum active power transfer is calculated by Thevenin equivalent parameters(TEPs)and quasi-state equations of LCC-HVDC transmission systems.Meanwhile,to mitigate the subsequent commutation failures(SCFs)that may come with the fault recovery process,the maximum DC current order that avoids SCFs is calculated through imaginary commutation process.Finally,the minimum value of the two DC current orders is sent to the control system.Simulation results based on PSCAD/EMTDC show that the proposed strategy mitigates SCFs effectively and exhibits good performance in recovery.

A comprehensive review of Energy Internet: basic concept,operation and planning methods, and research prospects

With the intensifying energy crisis and environmental pollution, the Energy Internet and corresponding patterns of energy use have been attracting more and more attention. In this paper, the basic concept and characteristics of the Energy Internet are summarized, and its basic structural framework is analyzed in detail. On this basis,couplings between the electric power system and other systems such as the cooling and heating system, the natural gas system, and the traffic system are analyzed, and the operation and planning of integrated energy systems in both deterministic and uncertain environments are comprehensively reviewed. Finally, the research prospects and main technical challenges of the Energy Internet are discussed.

MHGCN:Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment

Knowledge graphs(KGs)provide a wealth of prior knowledge for the research on social networks.Crosslingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies.Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs.However,these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs.In this paper,we propose a new embedding-based framework named multiview highway graph convolutional network(MHGCN),which considers the entity alignment from the views of entity semantic,relation semantic,and entity attribute.To learn the structural features of an entity,the MHGCN employs a highway graph convolutional network(GCN)for entity embedding in each view.In addition,the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding.The alignment entities are identified based on the similarity of entity embeddings.The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods.The research also will benefit knowledge fusion through cross-lingual KG entity alignment.

A Comprehensive Review of Security-constrained Unit Commitment

Security-constrained unit commitment(SCUC)has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market.With the development of emerging power grids,fruitful research results on SCUC have been obtained.Therefore,it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems.In this paper,the basic mathematical model of the standard SCUC is summarized,and the characteristics and application scopes of common solution algorithms are presented.Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed.Finally,research trends in the field are prospected based on a summary of the state-of-the-art and latest studies.It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future.