Earticle

This study shows an application method of Micro Grid integrating Solar Energy Seasonal Storage (SESS) based on the analysis of time-variant load characteristics of electric railway. Micro Grid includes various renewable energy generation plants as like wind farm, photovoltaic, small hydro, fuel cell, etc. and some energy storage systems to meet the balance between generation production and demand. The essential concept of this paper is the connection and integration between electric railway and Micro Grid including SESS which is able to control power system frequency. This new concept for power system stabilization would be tested in an independent Micro Grid site and some detail application methods would be evaluated for the commercialization.

대부분의 데이터 센터는 평균적으로 총 자원의 20% ~ 30%만이 사용되고 있고 50% 미만의 전력 효율을 갖고 있 다. 유연한 전력 관리 정책을 적용하면 데이터 센터의 전력 효율을 높일 수 있고 이를 위해서는 동적 부하에 따른 정확한 전력 소비량을 예측 가능해야 한다. 하지만 부하에 따른 서버의 전력 소비량 예측은 시스템 구축비용 및 컴 퓨팅 비용이 매우 높은 편이다. 본 논문에서는 기존의 시스템 정보를 이용한 서버 전력 측정 방법을 소개하고 간편 한 구축과 저렴한 컴퓨팅 비용으로 시스템 부하에 따른 전력 소비량을 예측할 수 있는 방법을 제시한다. 시스템 부 하를 분류하고 부하 별 전력 모델을 도출하여 적용함으로써 보다 정확한 예측 방법을 제안하였다. 또한, 실제 서버 환경을 구축하여 평가를 진행하였고 제안한 방법의 타당성을 증명하였다.

Data centers are mostly under-utilized as only 20 ~30% of total computing resources are consumed in average, leading power efficiency to less than 50%. In order to increase power efficiency of data centers, a flexible power management policy must be adopted. It requires precise estimation of power consumption in real-time; however, it is very difficult and expensive as the power consumption highly fluctuates with the utilization of computing resources. Hence, in this paper, we introduce an efficient power prediction method that has low computation and minimum installation. By classifying system loads and modelling power analysis model for each of them, we could increase accuracy of the power prediction method.

스펙트럼 모드해석에 기반한 풍방향 동적해석법은 1960년대에 정립된 이후, 이론적인 식에 의해서 응답을 평가할 수 있는 장점 때문에 세계 많은 나라에서 풍하중 기준으로 사용되고 있다. 이론식은 변동풍속 스펙트럼으로부터 모달풍하중 스펙트럼을 유도하고 스펙트럼해석에 의한 변동응답을 이용하여 구조물의 내풍성능을 평가할 수 있었다. 모달 풍하중 스펙트럼은 이론적 전개를 용이하게 하기 위하여 풍력계수, 난류강도, 코-코히어런스 등과 같은 바람의 성상을 단순화하여 유도된 것으로 그에 대한 타당성을 검토할 필요가 있다. 본 연구에서는 대기 경계층 풍동에서 공탄성 실험을 수행하고 하중 추정법을 이용하여 풍방향 동적하중을 구하였으며, 이를 이론적인 모달 풍하중 스펙트럼과 비교하였다. 공탄성 실험으로부터 계측된 가속도 응답에 상태공간 모드분해법을 적용하여 모달응답을 구하고 그로부터 풍방향 모달하중을 역추정하고 이론적 모달풍하중과 비교하여 그 특성을 분석하였다. 특히 풍속스펙트럼으로부터 모달 풍하중스펙트럼으로의 변환과정에서 요구되는 공력어드미턴스의 특성을 이론적 어드미턴스와 비교분석하였다. 분석결과 추정된 풍방향 동적하중 스펙트럼은 최상층 풍속에 따라 이론적 풍하중 스펙트럼의 크기가 다소 다르게 반영되어 나타났으나 진동수 특성은 매우 유사한 것을 확인할 수 있었다. 본 연구기법과 과정을 자연풍을 받는 구조물의 실계측 응답에 적용하면 자연환경상태의 풍하중을 분석하고 그에 기반하여 풍하중 모델을 정립하는데 활용 가능할 것으로 사료된다.

The dynamic along-wind response based on spectral modal analysis has been adopted as a wind load standard in many countries over the world due to the advantage of evaluating the dynamic response by the simple theoretical formula since it has been established in the 1960s. In theoretical evaluation process, the modal wind load spectrum was derived from the spectral density of longitudinal velocity fluctuation, and the spectral modal analysis is performed for the wind-resistant performance. Since the modal wind load spectrum is derived by simplifying wind characteristics such as drag coefficient, turbulence intensity, co-coherence, etc. to facilitate the theoretical development, it is necessary to examine the validity of the simplification. In this study, the aero-elastic model test was performed in the atmospheric boundary layer wind tunnel, and the modal response was separated from the acceleration responses measured from the model test by applying the state space mode decomposition technique. And then modal load was estimated in order to compare with the theoretical modal wind load. Especially, the characteristics of aerodynamic admittance used for the conversion from spectral density of wind velocity to modal wind load spectrum are mainly analyzed and compared with theoretical aerodynamic admittance. From the results, it is shown that the spectrum of estimated dynamic wind load is slightly different with the theoretical wind load spectrum according to the top wind velocity, but the overall spectral characteristics is similar in the frequency signature. Applying the process in this study to the measured response of a structure under natural wind, it can be used for estimating the wind load in the natural environment condition and establishing a wind load model based on in-situ data.

본 연구에서는 소형풍력발전용 블레이드의 축소모델을 대상으로 단순 하중 계산 및 구조 시험을 수행하였 다. 먼저, 연구 대상인 블레이드의 초기 모델의 0.2 비율만큼 축소하여 설계 및 제작하였다. 그리고 소형 풍력발전 국제 규격인 IEC 61400-2에 의거한 단순 하중 계산식을 이용하여 모멘트를 획득하였다. 또한, 추를 이용한 구조 시험을 수행하여 최대 모멘트를 획득하였다. 이를 통하여 계산 및 시험에 따른 최대 모멘트를 비교하였다.

This study deals with simplified load calculation and structural testing for scale down model of small wind turbine blade. First, the blade was designed and produced scale down to 0.2 ratio of initial blade. And moments were acquired by simplified load calculation equations according to IEC 61400-2 standard. Also, structural test using weight was conducted to obtain the maximum moment. Therefore maximum moments were compared at calculation and test.

This paper considers automated storage and retrieval systems with double shuttle. We develop the travel time model based on the first come first service rule. We evaluate the performance of the double shuttle system working on the four command cycle.

스마트그리드에서 정확한 단기 부하 예측을 통한 자원의 이용 계획은 에너지 시스템 운영의 불확실성을 줄이고 운영 효율을 높이는데 있어서 매우 중요하다. 단기 부하 예측에 얕은 신경회로망을 포함한 다수의 머신 러닝 기법이 적용되어왔지만 예측 정확도의 개선이 요구되고 있다. 최근에는 컴퓨터 비전이나 음성인식 분야에서 심층 신경회로망의 뛰어난 연구 결과로 인해 심층 신경회로망을 단기 전력수요 예측에 적용해 예측 정확도를 개선하려는 시도가 주목 받고 있다. 본 논문에서는 일별 전력 부하 첨두치를 예측하기 위한 다층신경회로망 구조의 심층 신경회로망 모델을 제안한다. 제안된 심층 신경회로망은 층별 학습이 선행된 후 전체 모델의 학습이 이루어진다. 한국전력거래소에서 얻은 4년 동안의 일별 전력 수요 데이터를 사용, 하루 및 이틀 앞선 전력수요 첨두치를 예측하는 심층 신경회로망 모델을 구축하고 예측 정확도를 비교, 평가한다.

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

In this paper, a new model, core vector regression (CVR) optimized by memetic algorithm (MA), is presented to predict electric daily load. Support vector regression (SVR) has obtained wide focus in recent years to solve nonlinear regression problems in many fields. However, it is limited on large scale dataset problem because of its high time and space complexity. Hence, CVR is proposed to improve the SVR on solving large scale dataset problem. Proper parameters selection of CVR model determines the complexity and accuracy of the model. In this paper, MA is proposed to optimize the parameters of CVR, which is called MA-CVR. Electric load is the time-dependent data which shows recurrent pattern weekly, seasonally and yearly. In this paper, we adopt MA optimization method and choose adaptive parameters dynamically based on time recurrent character of electric load data. Experimental results show that MA-CVR outperforms the existing model optimized by genetic algorithm which is called GA-CVR.

In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive particle swarm optimization (APSO) based on fitness function was put forward to optimize the kernel parameter σ and regularization parameter γ of LS-SVM. Based on the optimized forecasting model, non-parametric error correction model is also presented by iterative method. The error forecasted by non-parametric model was used to update the forecasted load so as to improve the forecasting accuracy. Load data selected from some area in South China as training and forecasting data is used to analyze. Case study illustrates that the proposed forecasting model (NP-APSO-SVM) has more generalized performance and better forecasting accuracy compared with the method of standard SVM.

This paper designs and implements a high availability clusters and incorporated with load balance infrastructure of web servers. The paper described system can provide full facilities to the website hosting provider and large business organizations. This system can provide continuous service though any system components fail uncertainly with the help of Linux Virtual Server (LVS) load-balancing cluster technology and combined with virtualization as well as shared storage technology to achieve the three-tier architecture of Web server clusters. This technology not only improves availability, but also affects the security and performance of the application services being requested. Benefits of the system include node failover overcome; network failover overcome; storage limitation overcome and load distribution.

In order to overcome the problems which are difficult to be accurately predicted, such as voilent vibration, large amplitude, and pseudoperiod, we put forward a load-classification method and a model-selection method following a multi-model merit. The multi-model merit can be realized by the result of Network training, so we can forecast the load of iron and steel enterprises respectively. In this way, we can avoid the limitations of traditional load forecasting methods which simply depend on sample datas. In the framework of this model, to minimize the load forecast error is the target. On the one hand, it can be convenient to add new models into the framework, so as to improve the accuracy of the prediction, find more characteristics of the load, and better the model. On the other hand, based on the load data, we can also adaptively change the way the model is formed, so as to expand the applicability of prediction methods. By simulating different load management forecasting systems, we confirm that the effectiveness of the proposed method is verified. A software package based on the methods presented in this paper for power systems scheduling is also completed. Some native steel generation corporations have already used the system.

Considering the condition of target selected under different circumstances, this paper proposed a new multi-objective model of transmission congestion management with interruptible load based on brand circuit overload match with interrupt capacity. The objective function of the mathematical model presented in this paper are respectively that, if interruptible load matching with overload capacity ,the least number of interruptible load node at least, the lowest total interrupt amount of interruptible load. The multi-object model put forward three goals, brand circuit overload match with interruptible load, the minimum number of interruptible load nodes and the minimum total interruption of interruptible load. Against other optimization methods cannot prioritize to multiple targets and it can easily lead to convergence in the process of solving problems, the paper presented construct evaluation function based on the linear weighted sum to optimize multi-objective linear problem. This method can be sorted prior to multi-objective optimization model. And it has better convergence than other optimization methods in the solution process. Finally, it tested and verified the correctness of method through the IEEE 30 bus power system. And it successfully applied to grid congestion management in oilfield.

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in order to realize the mutual supplement with each other's advantages in this paper. Firstly, the PSO algorithm is used to optimize the parameters of the SVM in order to obtain the optimal parameters of the SVM. Then a short-term load forecasting method based on combining the PSO and SVM according to the characteristics and influencing factors of short-term load forecasting is proposed. An actual power system in one region is applied to test and verify the short-term load forecasting method. The results show that the short-term load forecasting method takes on the good convergence and higher prediction precision.

Current grey clustering analysis methods have some defects. So, this paper proposes a prediction model based on improved grey clustering analysis. Firstly, it constructs the grey classical domain and the grey sector domain based on prediction subjects and data and according to relevant theory about grey clustering analysis. Secondly, it categorizes samples according to features of prediction subjects and confirms the analysis categories corresponding to the classical domain. Thirdly, based on the grey system theory, it constructs the grey correlation coefficient model and grey correlation degree model so as to obtain the weighed grey correlation degree. Thus, prediction subjects can be divided into proper category. Finally, power load forecasting in the power industry is taken as a case to prove that the model is reliable and has efficacy.

This article proposes a design and implementation of a low cost two-tier architecture model for high availability cluster combined with load-balancing and shared storage technology to achieve desired scale of three-tier architecture for application load balancing e.g. web servers. The research work proposes a design that physically omits Network File System (NFS) server nodes and implements NFS server functionalities within the cluster nodes, through Red Hat Cluster Suite (RHCS) with High Availability (HA) proxy load balancing technologies. In order to achieve a low-cost implementation in terms of investment in hardware and computing solutions, the proposed architecture will be beneficial. This system intends to provide steady service despite any system components fails due to uncertainly such as network system, storage and applications.

The medium and long term load forecasting is the basis of power planning, investment, production, scheduling and trade, which plays an important role in electric power safety and economic operation. In China, it has the increasing uncertainty and the uncertainty of random variation to forecast the medium and long term load. Thus we can regard it as a typical grey system. However, the traditional grey prediction method cannot be adapt to the needs of the load forecasting gradually. It need to be rich and perfect with the continuous improvement of power system complexity and power marketization degree. This paper studied the modelling mechanism of grey prediction model. Then we analyzed the problems existing in the model, including the boundary value problem, the background value structure problem and the least squares parameter identification problem. This paper put forward an optimization method to directly identify the boundary value x(0)(1), the developing coefficient a and grey coefficient b using ant colony algorithm according to the time response expression of GM(1,1) model, so that it established an optimized GM(1,1) prediction model based on ant colony algorithm. This model can fix the impact of boundary value, and also avoid the errors brought by the background value construction and the least squares parameter estimation. It can verify the effectiveness of the proposed optimization model through the load data simulation. And it can improve the prediction accuracy effectively.

This paper introduces a new load balancing algorithm and a method of estimating the performance of the load balancer in a network composed of a multi-server sharing the entire load such as cloud computing. A load balancer collects newly arrived traffic in batches and distributes the batches sequentially. A discrete-time Geo/G/1 queueing model having a heterogeneous two-phase service mode with a fixed-size batch is applied for designing the optimal load balancing rule. The stationary queue length and regeneration cycle length are derived so that the long-run average cost function of a load balancer could be analyzed. A numerical example also illustrates the process of finding the optimal threshold value that minimizes the work load of a load balancer.

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

In this paper, we propose a dynamic load model considering change of flux inside induction motor for transient stability analysis and we present a new method for estimating unknown parameters of the dynamic load model. The model is a parallel composite of a constant impedance load and an induction motor load behind a series constant reactance. An adequate dynamic load model is essential for evaluating power system stability and this model can represent the behavior of actual load by using appropriate parameters. However, the problem of this model has a large amount of parameters and it is not easy to estimate a lot of unknown parameters. Therefore we propose an estimating method based on Particle Swarm Optimization (PSO), which is a non-linear optimization method, by using measured instantaneous voltage sag data. We estimated parameters of the actual load by PSO to minimize error between calculated and measured power of load after a load drop due to voltage sag by the accident. It was confirmed that the proposed method was successful and PSO is effective in parameter estimation.

Electric arc furnaces (EAFs) use bulk electrical energy to create heat in metal refining industries. The electric arc process is a main cause of the degradation of the electric power quality such as voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the aperiodic physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this parer, a chaotic approach to such modeling is described and justified. At the same time, a DLL(Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.