SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

A Slice Analysis-Based Bayesian Inference Dynamic Power Model for CMOS Combinational Circuits

To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.

Power Allocation and Performance Analysis of the Collaborative NOMA Assisted Relaying Systems in 5G

Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.

Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

Hydrodynamic Analysis of a Semi-submersible Wind-Tidal Combined Power Generation Device

Energy shortages and environmental pollution are becoming increasingly severe globally. The exploitation and utilization of renewable energy have become an effective way to alleviate these problems. To improve power production capacity, power output quality, and cost effectiveness, comprehensive marine energy utilization has become an inevitable trend in marine energy development. Based on a semi-submersible wind-tidal combined power generation device,a three-dimensional frequency domain potential flow theory is used to study the hydrodynamic performance of such a device. For this study, the RAOs and hydrodynamic coefficients of the floating carrier platform to the regular wave were obtained. The influence of the tidal turbine on the platform in terms of frequency domain was considered as added mass and damping. The direct load of the tidal turbine was obtained by CFX.FORTRAN software was used for the second development of adaptive query workload aware software, which can include the external force. The motion response of the platform to the irregular wave and the tension of the mooring line were calculated under the limiting condition(one mooring line breakage). The results showed that the motion response of the carrier to the surge and sway direction is more intense, but the swing amplitude is within the acceptable range. Even in the worst case scenario, the balance position of the platform was still in the positioning range, which met the requirements of the working sea area. The safety factor of the mooring line tension also complied with the requirements of the design specification. Therefore, it was found that the hydrodynamic performance and motion responses of a semi-submersible wind-tidal combined power generation device can meet the power generation requirements under all design conditions, and the device presents a reliable power generation system.

POWER FLOW ANALYSIS AND APPLICATION SIMULATION OF ELECTROMAGNETIC CONTINUOUSLY VARIABLE TRANSMISSION

With the structure of two air gaps and two rotors, the electromagnetic continuously variable transmission（EMCVT） is a novel power-split continuously variable transmission（CVT）. There are two kinds of power flowing through the EMCVT, one is mechanical power and the other is electric power. In the mean time, there are three power ports in the EMCVT, one is the outer rotor named mechanical power port and the other two are the inner rotor and the stator named electric power ports. The mechanical power port is connected to the driving wheels through the final gear and the electric ports are connected to the batteries through the transducers. The two kinds of power are coupled on the outer rotor of the EMCVT. The EMCVT can be equipped on the conventional vehicle being regarded as the CVT and it also can be equipped on the hybrid electric vehicle（HEV） as the multi-energy sources assembly. The power flows of these two kinds of applications are analysed. The back electromotive force（EMF） equations are illatively studied and so the dynamic mathematic model is theorized. In order to certify the feasibility of the above theories, three simulations are carried out in allusion to the above two kinds of mentioned applications of the EMCVT and a five speed automatic transmission（AT） vehicle. The simulation results illustrate that the efficiency of the EMCVT vehicles is higher than that of the AT vehicle owed to the optimized operation area of the engine. Hence the fuel consumption of the EMCVT vehicles is knock-down.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Review on failure analysis of interconnections in power devices

Interconnections in microelectronic packaging are not only the physical carrier to realize the function of electronic circuits,but also the weak spots in reliability tests.Most of failures in power devices are caused by the malfunction of interconnections,including failure of bonding wire as well as cracks of solder layer.In fact,the interconnection failure of power devices is the result of a combination of factors such as electricity,temperature,and force.It is significant to investigate the failure mechanisms of various factors for the failure analysis of interconnections in power devices.This paper reviews the main failure modes of bonding wire and solder layer in the interconnection structure of power devices,and its failure mechanism.Then the reliability test method and failure analysis techniques of interconnection in power device are introduced.These methods are of great significance to the reliability analysis and life prediction of power devices.

Dynamic character analysis for the arc welding power source based on fuzzy logic

A lot of experimental methods have been brought forth to assess the dynamic character of the arc welding power source, but up to now, this issue has not been solved very well. In this paper, based on the fuzzy logic reasoning method, a dynamic character assessing model for the arc welding power source was established and used to analyze the dynamic character of the welding power source. Three different types of welding machine have been tested, and the characteristic information of the electrical signals such as re-striking arc voltage, low welding current and so on of the welding process were extracted accurately by using a self-developed welding dynamic arc wavelet analyzer. The experimental results indicate that this model can be used as a new assessing method for the dynamic character of the arc welding power source.

Post hoc power analysis:is it an informative and meaningful analysis?

Power analysis is a key component for planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences ask for power analysis for data already collected and analysed before accepting manuscripts for publication. In this report, post hoc power analysis for retrospective studies is examined and the informativeness of understanding the power for detecting significant effects of the results analysed, using the same data on which the power analysis is based, is scrutinised. Monte Carlo simulation is used to investigate the performance of posthoc power analysis.

Architecture design and demand analysis on application layer of standard system for ubiquitous power Internet of Things

The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power system and makes full use of the mobile internet,artificial intelligence,and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system.This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things(IoT)-related industry standards system.It briefly describes the generic standard classification methods,layered architecture,conceptual model,and system tables in the UPIoT application layer.Based on the principles of inheritance,innovation,and practicability,this study divides the application layer into customer service,power grid operation,integrated energy,and enterprise operation,emerging business and analyzes the standard requirements of these five fields.This study also proposes a standard plan.Finally,it summarizes the research report and provides suggestions for a follow-up work.

Test Analysis of Traction Battery Peak Power

The test process of electric vehicles (EVs) traction battery peak power is analyzed in detail. Aimed at a special “traction” design of versatile battery—HORIZON~ C~2M Battery, the features are introduced. According to the peak power test schedule, the test parameters of HORIZON~ C~2M Battery are calculated and the charging and discharging experiments are carried out. The sustained (30 s) discharge power capability of battery at 2/3 of its open circuit voltage at each of various depths of discharge is determined. The dynamic internal resistance under peak power test is established. Considering the temperature impact during discharging, the peak power capability at each of various depths of discharge is corrected. The correctness of peak power test is validated by combining theory analysis with test results.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

Transient Stability Analysis of Power System Based on an Improved Neural Network

A new type of ANN (Artificial Neural Network) structure is introduced, and a nonlinear transformation of the original features is proposed so as to improve the learning covergence of the neural network. This kind of improved ANN is then used to analyse the transient stability of two real power systems. The results show that this method possesses better effectiveness and high convergence speed.

Aseismic safety analysis of a prestressed concrete containment vessel for CPR1000 nuclear power plant

Abstract： The containment vessel of a nuclear power plant is the last barrier to prevent nuclear reactor radiation. Aseismic safety analysis is the key to appropriate containment vessel design. A prestressed concrete containment vessel （PCCV） model with a semi-infinite elastic foundation and practical arrangement of tendons has been established to analyze the aseismic ability of the CPR1000 PCCV structure under seismic loads and internal pressure. A method to model the prestressing tendon and its interaction with concrete was proposed and the axial force of the prestressing tendons showed that the simulation was reasonable and accurate. The numerical results show that for the concrete structure, the location of the cylinder wall bottom around the equipment hatch and near the ring beam are critical locations with large principal stress. The concrete cracks occurred at the bottom of the PCCV cylinder wall under the peak earthquake motion of 0.50 g, however the PCCV was still basically in an elastic state. Furthermore, the concrete cracks occurred around the equipment hatch under the design internal pressure of 0.4MPa, but the steel liner was still in the elastic stage and its leak-proof function soundness was verified. The results provide the basis for analysis and design of containment vessels.

A Discrete State Event Driven Simulation based Losses Analysis for Multi-terminal Megawatt Power Electronic Transformer

At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

Analysis of the trend of global power sources based on comment emotion mining

In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.