Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Study on wave energy resource assessing method based on altimeter data——A case study in Northwest Pacific

Wave energy resource is a very important ocean renewable energy. A reliable assessment of wave energy resources must be performed before they can be exploited. Compared with wave model, altimeter can provide more accurate in situ observations for ocean wave which can be as a novel method for wave energy assessment.The advantage of altimeter data is to provide accurate significant wave height observations for wave. In order to develop characteristic and advantage of altimeter data and apply altimeter data to wave energy assessment, in this study, we established an assessing method for wave energy in local sea area which is dedicated to altimeter data.This method includes three parts including data selection and processing, establishment of evaluation indexes system and criterion of regional division. Then a case study of Northwest Pacific was performed to discuss specific application for this method. The results show that assessing method in this paper can assess reserves and temporal and spatial distribution effectively and provide scientific references for the siting of wave power plants and the design of wave energy convertors.

Power Big Data Fusion Prediction

This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.

Characterizing big data analytics workloads on POWER8 SMT processors

Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.

A hardware-based algorithm for virtual machine provisioning in a private cloud

Cloud computing is becoming a key factor in the market day by day. Therefore, many companies are investing or going to invest in this sector for development of large data centers. These data centers not only consume more energy but also produce greenhouse gases. Because of large amount of power consumption, data center providers go for different types of power generator to increase the profit margin which indirectly affects the environment. Several studies are carried out to reduce the power consumption of a data center. One of the techniques to reduce power consumption is virtualization. After several studies, it is stated that hardware plays a very important role. As the load increases, the power consumption of the CPU is also increased. Therefore, by extending the study of virtualization to reduce the power consumption, a hardware-based algorithm for virtual machine provisioning in a private cloud can significantly improve the performance by considering hardware as one of the important factors.

Calorimetric power measurements in the EAST ECRH system

In this paper, the measurement method of calorimetric power for an electron cyclotron resonance heating（ECRH） system for EAST is presented. This method requires measurements of the water flow through the cooling circuits and the input and output water temperatures in each cooling circuit. Usually, the inlet water temperature stability is controlled to obtain more accurate results.The influence of the inlet water temperature change on the measurement results is analyzed for the first time in this paper. Also, a novel temperature calibration method is proposed. This kind of calibration method is accurate and effective, and can be easily implemented.

Research on Real-time Monitoring of Abnormal Seismic Noise

The noise data in vertical component records of 85 seismic stations in Fujian Province during 2012 is used as the research object in this paper. The noise data is divided into fiveminute segments to calculate the power spectra. The high reference line and low reference line of station are then identified by drawing a probability density function graph( PDF)using the power spectral probability density function. Moreover, according to the anomalies of PDF graphs in 85 seismic stations,the abnormal noise is divided into four categories: dropped packet, low noise, high noise, and median noise anomalies.Afterwards,four selection methods are found by the high or low noise reference line of the stations,and the system of real-time monitoring of seismic noise is formed by combining the four selection methods. Noise records of 85 seismic stations in Fujian Province in July2013 are selected for verification,and the results show that the anomalous noise-recognition system could reach a 90% success rate at most stations and the effect of selection are very good. Therefore,it could be applied to the seismic noise real-time monitoring in stations.

Knowledge Model for Electric Power Big Data Based on Ontology and Semantic Web

It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.

Data Center Power System Stability-Part II: System Modeling and Analysis

This is the second part of a two-part paper on stability study of data center power systems by impedance-based methods.As the basis for this application,Part I[1]developed new impedance models for power supplies that are the most dominant loads in data centers.This second part presents system modeling and analysis methods that can support practical data center power system design to ensure stability.The proposed methods comprise:1)building distribution network modeling by impedance scaling;2)system modeling and model reduction based on equivalent source impedance;3)system stability analysis in the sequence domain to include zero-sequence dynamics;and 4)expansion of system models and analyses to account for network asymmetry and uneven loading.These methods are used to characterize practical resonance problems observed in data centers,explain their root causes,and develop solutions.For systems using Y-connected power supply units(PSUs),the zero sequence is identified as the weakest link and the first to become unstable.The expanded system model and analysis reveal a new,differential-mode instability that is responsible for high frequency resonances.To guarantee system stability,new impedance-based product and system design specifications are developed based on sufficient conditions derived from the Nyquist stability criterion.Laboratory and field measurements are presented to substantiate the proposed methods and conclusions.

Data Center Power System Stability-Part I: Power Supply Impedance Modeling

This two-part paper presents methods to predict,characterize and ensure the stability of data center power systems based on impedance analysis.The work was motivated by recent power system resonance incidents in new data centers.Part I presents new input impedance models for single-phase power supply units(PSUs)to enable this application.Existing impedance models of single-phase PSU cannot meet the requirements of this application because they exclude DC voltage control that affects system stability at low frequency,or are in a dq reference frame that cannot handle the complexity of data center power systems.The developed new models include DC bus dynamics and are directly in the phase domain to simplify system stability analysis,avoiding the need for multiple-input-multiple-output(MIMO)system models and the generalized Nyquist criterion that are difficult to apply but necessary with dq-frame models.Both the converter and system level models also include the coupled current response that is characteristic of AC-DC converters and important for system stability at low frequency.The simple form of the models and system stability analysis directly in the phase domain also make it possible to develop new PSU design methods and performance specifications that together will ensure the stability of new data center power systems.The developed models are validated by laboratory measurements and are used in Part II of the work to study data center power system stability.

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

Data Completion for Power Load Analysis Considering the Low-rank Property

With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.

Attribute-based keyword search encryption for power data protection

To protect the privacy of power data,we usually encrypt data before outsourcing it to the cloud servers.However,it is challenging to search over the encrypted data.In addition,we need to ensure that only authorized users can retrieve the power data.The attribute-based searchable encryption is an advanced technology to solve these problems.However,many existing schemes do not support large universe,expressive access policies,and hidden access policies.In this paper,we propose an attributebased keyword search encryption scheme for power data protection.Firstly,our proposed scheme can support encrypted data retrieval and achieve fine-grained access control.Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data.Secondly,to satisfy the requirement in the power grid environment,the proposed scheme can support large attribute universe and hidden access policies.The access policy in this scheme does not leak private information about users.Thirdly,the security analysis and performance analysis indicate that our scheme is efficient and practical.Furthermore,the comparisons with other schemes demonstrate the advantages of our proposed scheme.

Green challenges to system software in data centers

With the increasing demand and the wide application of high performance commodity multi-core processors, both the quantity and scale of data centers grow dramatically and they bring heavy energy consumption. Researchers and engineers have applied much effort to reducing hardware energy consumption, but software is the true consumer of power and another key in making better use of energy. System software is critical to better energy utilization, because it is not only the manager of hardware but also the bridge and platform between applications and hardware. In this paper, we summarize some trends that can affect the efficiency of data centers. Meanwhile, we investigate the causes of software inefficiency. Based on these studies, major technical challenges and corresponding possible solutions to attain green system software in programmability, scalability, efficiency and software architecture are discussed. Finally, some of our research progress on trusted energy efficient system software is briefly introduced.

Prediction model for cost data of a power transmission and transformation project based on Pearson correlation coefficient-IPSO-ELM

In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.

L_(p) Quasi Norm State Estimator for Power Systems

This paper proposes an L_(p)(0<p<1)quasi norm state estimator for power system static state estimation.Compared with the existing L1 and L2 norm estimators,the proposed estimator can suppress the bad data more effectively.The robustness of the proposed estimator is discussed,and an analysis shows that its ability to suppress bad data increases as p decreases.Moreover,an algorithm is suggested to solve the nonconvex state estimation problem.By introducing a relaxation factor in the mathematical model of the proposed estimator,the algorithm can prevent the solution from converging to a local optimum as much as possible.Finally,simulations on a 3-bus DC system,the IEEE 14-bus and IEEE 300-bus systems as well as a 1204-bus provincial system verify the high computation efficiency and robustness of the proposed estimator.

6T SRAM cell analysis for DRV and read stability

The present paper analyzes the hold and read stability with temperature and aspect ratio variations. To reduce the power dissipation, one of the effective techniques is the supply voltage reduction. At this reduced supply voltage the data must be stable. So, the minimum voltage should be discovered which can also retain the data. This voltage is the data retention voltage（DRV）. The DRV for 6T SRAM cell is estimated and analyzed in this paper.The sensitivity analysis is performed for the DRV variation with the variation in the temperature and aspect ratio of the pull up and pull down transistors. Cadence Virtuoso is used for DRV analysis using 45 nm GPDK technology files. After this, the read stability analysis of 6T SRAM cell in terms of SRRV（supply read retention voltage） and WRRV（wordline read retention voltage） is carried out. Read stability in terms of RSNM can be discovered by accessing the internal storage nodes. But in the case of dense SRAM arrays instead of using internal storage nodes,the stability can be discovered by using direct bit line measurements with the help of SRRV and WRRV. SRRV is used to find the minimum supply voltage for which data can be retained during a read operation. Similarly, WRRV is used to find the boosted value of wordline voltage, for which data can be retained during read operation. The SRRV and WRRV values are then analyzed for different Cell Ratios. The results of SRRV and WRRV are then compared with the reported data for the validation of the accuracy of the results.