The long memory HEAVY process: modeling and forecasting financial volatility

This paper studies the bivariate HEAVY system of volatility regression equations and its various extensions that are directly applicable to the day-to-day business treasury operations of trading in foreign exchange and commodities, investing in bond and stock markets, hedging out market risk, and capital budgeting. We enrich the HEAVY framework with powers, asymmetries, and long memory that improve its forecasting accuracy significantly. Other findings are as follows. First, hyperbolic memory fits the realized measure better, whereas fractional integration is more suitable for the powered returns. Second, the structural breaks applied to the bivariate system capture the time-varying behavior of the parameters, in particular during and after the global financial crisis of 2007/2008.

Voltage Stability Assessment in Radial Distribution Power System

The voltage stability problem of distribution networks is associated with a rapid voltage drop because of heavy system load. To operate the distribution system under such critical conditions the integration of distributed resource improves the reliability of supplying power by improving voltage stability and reducing the power losses. This paper presents voltage stability analysis of radial distribution networks in the presence of distributed generation. The analysis is accomplished using different methods which can be evaluated at each node of the distribution system. In this paper study the all types of methods which is used to improve the Voltage Stability Assessment in radial distribution power system.

An Automatic Decision-Making Mechanism for Virtual Machine Live Migration in Private Clouds

Due to the increasing number of computer hosts deployed in an enterprise, automatic management of electronic applications is inevitable. To provide diverse services, there will be increases in procurement, maintenance, and electricity costs. Virtualization technology is getting popular in cloud computing environment, which enables the efficient use of computing resources and reduces the operating cost. In this paper, we present an automatic mechanism to consolidate virtual servers and shut down the idle physical machines during the off-peak hours, while activating more machines at peak times. Through the monitoring of system resources, heavy system loads can be evenly distributed over physical machines to achieve load balancing. By integrating the feature of load balancing with virtual machine live migration, we successfully develop an automatic private cloud management system. Experimental results demonstrate that, during the off-peak hours, we can save power consumption of about 69 W by consolidating the idle virtual servers. And the load balancing implementation has shown that two machines with 80% and 40% CPU loads can be uniformly balanced to 60% each. And, through the use of preallocated virtual machine images, the proposed mechanism can be easily applied to a large amount of physical machines.

Static Var Compensators to Stabilize Voltages in Weak Power Systems

Large horsepower motors on weak power systems cause problems associated with excessive voltage drops. The voltage drops may be steady state caused by heavy system loading or they may be transient caused by starting a large motor. One way to solve the problems is to use a Static Var Compensator (SVC) on the power system, to compensate for the reactive loads and stabilize the voltage on the utility and in the station. SVCs have been used for years by electric utilities and are now being used for some industrial applications. This paper gives an overview of the technology and describes a pipeline application.