An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Heating load interval forecasting approach based on support vector regression and error estimation

As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching.

Adaptive Bit Loading Scheme with Semi-Blind Channel Estimation for OFDMSystems

An adaptive bit loading and power-allocation scheme is proposed in order to augment the performance of the system based on orthogonal frequency division multiplexing （OFDM）, which is based on the maximum power margin. Coinciding with the adaptive loading scheme, a semi-blind channel estimation algorithm using subspace decomposition method is proposed, which uses the information in the cyclic prefix. An initial channel state information is estimated by using the training sequences with the method of interpolation filtering. The proposed adaptive scheme is simulated on an OFDM wireless local area network（WLAN） system in a time-varying channel. The performance is compared to the constant loading scheme.

State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid.To improve the prediction accuracy of power systems with source-load twoterminal uncertainties,an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper.In the algorithm,the Q0 is used to offset the modeling error,and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.Verification of the proposed method is implemented on the IEEE 30 node system through simulation.The results show that,compared with the traditional methods,the improved adaptive cubature Kalman filter has higher prediction accuracy,which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.

Modeling and parameter estimation for hydraulic system of excavator's arm

A retrofitted electro-hydraulic proportional system for hydraulic excavator was introduced firstly. According to the principle and characteristic of load independent flow distribution(LUDV) system,taking boom hydraulic system as an example and ignoring the leakage of hydraulic cylinder and the mass of oil in it,a force equilibrium equation and a continuous equation of hydraulic cylinder were set up. Based on the flow equation of electro-hydraulic proportional valve,the pressure passing through the valve and the difference of pressure were tested and analyzed. The results show that the difference of pressure does not change with load,and it approximates to 2.0 MPa. And then,assume the flow across the valve is directly proportional to spool displacement and is not influenced by load,a simplified model of electro-hydraulic system was put forward. At the same time,by analyzing the structure and load-bearing of boom instrument,and combining moment equivalent equation of manipulator with rotating law,the estimation methods and equations for such parameters as equivalent mass and bearing force of hydraulic cylinder were set up. Finally,the step response of flow of boom cylinder was tested when the electro-hydraulic proportional valve was controlled by the step current. Based on the experiment curve,the flow gain coefficient of valve is identified as 2.825×10-4 m3/(s·A) and the model is verified.

Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia,China

Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.

Estimation of gases emitted by forest fires based on remote sensing data

Forest fire, an important agent for change in many forest ecosystems, plays an important role in atmo- spheric chemical cycles and the carbon cycle. The primary emissions from forest fire, CO2, CO, CH4, long-chained hydrocarbons and volatile organic oxides, however, have not been well quantified. Quantifying the carbonaceous gas emissions of forest fires is a critical part to better under- stand the significance of forest fire in calculating carbon balance and forecasting climate change. This study uses images from Enhanced Thematic Mapper Plus （ETM＋） on the Earth-observing satellite LANDSAT-7 for the year 2005 to estimate the total gases emitted by the 2006 Kanduhe forest fire in the Daxing＇an Mountains. Our results suggest that the fire emitted approximately 149,187.66 t CO2, 21,187.70 t CO, 1925.41 t CxHy, 470.76 t NO and 658.77 t SO2. In addition, the gases emitted from larch forests were significantly higher than from both broadleaf-needle leaf mixed forests and broadleaf mixed forests.

Alternative Method for Concrete Structures Compressive Strength Estimation with Small-Scale Destruction

To estimate a compressive strength from existing concrete structures by core drilling are usually gathered with a diameter specimen of 100 mm or three times of maximum coarse aggregate size and examined by uniaxial compressive strength （UCS）. It is relatively difficult to gather a large sized core, and a pit place will be limited by main members. To get an alternative solution with smaller specimen, point load test （PLT） has been sele,：ted which is a simple test and widely accepted in rock materials research, but relatively new in concrete. The reliability of PLT is evaluated by extracting a lot of core drilled specimen from ready mixed concrete blocks with maximum coarse aggregate size, G of 20 mm in representative of architectural structures and 40 mm in representative of civil structures on the range of concrete grade from 16 to 50. Compressive strengths were classified into general categories, conversion factors were determined, and scattering characteristics were also investigated. The relationship between point load index （Is） and compressive strength of concrete core specimen （fcc） can be written as linear approximation as fcc = k.Is- C.

Kriging metamodels for the dynamic response of high-rise buildings with outrigger systems and fragility estimates for seismic and wind loads

This paper proposes kriging metamodels for the dynamic response of high-rise buildings with outrigger systems subject to seismic and wind loads.Three types of outrigger systems are considered.Three-dimensional(3D)finite element models of high-rise buildings with outrigger systems are developed using ANSYS.Data generated from the finite element models are used to develop the proposed kriging metamodels.A sensitivity analysis is then carried out to determine the most sensitive input parameters in kriging metamodels to gain insights and suggest possible future developments.The proposed kriging metamodels are used to develop fragility estimates for high-rise buildings with three types of outrigger systems under seismic and wind loads.

A Hybrid Short Term Load Forecasting Model of an Indian Grid

This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

Dynamic Load Identification for Structures with Variable Stiffness Based on Extended Kalman Filter

We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.

Principal axes of M-DOF structures PartⅡ:Dynamic loading

This paper is the second in a two-part series that discusses the principal axes of M-DOF structures subjected to static and dynamic loads.The primary purpose of this series is to understand the magnitude of the dynamie response of structures to enable better design of structures and response modification devices/systems.Under idealized design condi- tions,the structural responses are obtained by using single directinn input ground motions in the direction of the intended response modification devices/systems,and by assuming that the responses of the structure is deconpleable in three mutual- ly perpendicular directions.This standard practice has been applied to both new and retrofitted structures using various seis- mic protective systems.Very limited information is available on the effects of neglecting the impact of directional couplings (cross effects of which torsion is a component)of the dynamic response of structures.In order to quantify such effects,it is necessary to examine the principal axes of structures under both static and dynamic loading.In this twn-part series,the first paper is concerned with static loading,which provides definitions and fundamental formulations,with the conclusion that cross effects of a statically loaded M-DOF structure resulting from the lack of principal axes are of insignificant magnitude. However,under dynamic or earthquake loading,a relatively small amount of energy transferred across perpendicular direc- tions is accumulated,which may result in significant enlargement of the structural response.This paper deals with a formu- lation to define the principal axes of M-DOF structures under dynamic loading and develops quantitative measures to identify cross effects resuhing from the non-existence of principal axes.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

Load Flow Analysis Framework for Active Distribution Networks Based on Smart Meter Reading System

With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.

A Novel Technique for Detection of Time Delay Switch Attack on Load Frequency Control

In this paper, we focus on the estimation of time delays caused by adversaries in the sensing loop (SL). Based on the literature review, time delay switch (TDS) attacks could make any control system, in particular a power control system, unstable. Therefore, future smart grids will have to use advanced methods to provide better situational awareness of power grid states keeping smart grids reliable and safe from TDS attacks. Here, we introduce a simple method for preventing time delay switch attack on networked control systems. The method relies on an estimator that will estimate and track time delays introduced by an adversary. Knowing the maximum tolerable time delay of the plant’s optimal controller for which the plant remains stable, a time-delay detector issues an alarm signal when the estimated time delay is larger than the minimum one and directs the system to alarm state. In an alarm state, the plant operates under the control of an emergency controller that is local to the plant and remains in this mode until the networked control system state is restored. This method is an inexpensive and simple way to guarantee that an industrial control system remains stable and secure.

CALCULATIONS TO ITS FATIGUE DAMAGE FRACTURE AND TOTAL LIFE UNDER MANY－STAGE LOADING FOR A CRANK SHAFT

Suggests some calculating formulas and methods with respect to the damage evolvingrate da / dN|i and the fatigue life and in varied history from uncrack to microcrackinitiation until fracture for a crankshaft, which are suitable to stress concentration positionsabout its journal fillets and oil holes on a crankshaft, that it is undergone to bending, twistingand shearing loading and subjected to unsymmetric cyclic many-stage loading. Last the total lifein whole process is estimated by

Peak Load Evaluation Based on the Accumulated Annual Energy

This paper presents a nonlinear approach to estimate the consumed energy in electric power distribution feeders. The proposed method uses the statistical solution algorithm to analyze the active energy monthly consumption, which enables one to estimate the energy consumption during any period of the year. The energy readings and the normalized accumulated energy profile are used to estimate the hourly consumed active power, which can be used for future planning and sizing the equipment of the electrical system. The effectiveness of the proposed method is demonstrated by comparing the simulated results with that of real measured data.

基于盲源分离的工业谐波源负荷分类识别方法

针对传统谐波源辨识方法无法实现工业用户谐波源具体类型的非侵入式识别的问题,该文提出一种基于盲源分离的工业谐波源负荷分类识别方法。该方法仅依据工业用户进线处电压、电流数据即可实现谐波源负荷具体类型的非侵入式识别。首先,从负荷等值阻抗模型入手,建立工业用户多负荷等值阻抗并联电路模型;其次,采用集合经验模态分解与奇异值分解相结合的方法确定构成用户进线处监测点综合等值阻抗信号的源阻抗信号数目;然后,采用快速独立分量分析实现将谐波源负荷等值阻抗信号从综合负荷等值阻抗信号中分离;最后,将分离出谐波源负荷等值阻抗信号频率特征与典型谐波源负荷进行匹配,进而实现分类识别。仿真与实测实验结果均表明,所提方法能够准确识别工业用户所含多种谐波源负荷的具体类型,具有较好的可行性和实用性。

地震荷载作用下土体变形试验研究

为研究不规则地震荷载对土体变形特性的影响,对砂土试样开展了系统的动三轴试验。试验中分别对3种不同密度砂土试样施加了4条具有不同峰值的不规则地震应力时程及其循环次数为20周的等幅正弦荷载,以便开展对比研究。结果表明:在地震荷载及其等幅循环荷载输入下,砂土试样的应变增长曲线差异显著,荷载类型及荷载波形是影响土单元应变时程发展特征的主要因素。考虑荷载不规则性得到的修正系数受荷载类型和砂土相对密实度的影响,不受输入荷载动应力幅值大小及砂土类型的影响。最后针对地震荷载下土单元变形的计算,给出了以常规的等幅三轴变形试验为依据的估算方法。