An optimized power conversion system concept of the integral, inherently-safe light water reactor

Memmott, Matthew; Wilding, Paul; Petrović, Bojan

doi:10.1016/j.anucene.2016.08.015

Cited by 9 publications

(1 citation statement)

References 7 publications

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“…Power systems has become a lifeline for the whole society [3][4]. With the development of the national economy, the demand for electricity in both urban and rural areas is increasing in China contiguously.…”

Section: Introductionmentioning

confidence: 99%

Power Load Forecasting Based on Wireless Sensor Networks

Liu

Li²

2017

Int. J. Onl. Eng.

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Abstract-At present, wireless sensor networks (WSN) technology is a field of much research interest in the area of information technology. The application of wireless sensor networks is expected to be very broad. With the development of communication protocol and their corresponding components, wireless sensor networks technology plays an increasingly important role in the power industry. The analysis and forecast of electric power data is essential to the construction and operation of the power network. Through the wireless sensor networks, we can obtain comprehensive power data. Then we can better forecast the power load through the data we obtain from the wireless sensor networks. In this paper, we propose an improved LSSVM method. We collect the power data by the wireless sensor networks and use the improved LSSVM method to forecast the power load. Experimental results demonstrate the effectiveness of the proposed method. Keywords-LSSVM; WSN; power data IntroductionWith the advent of wireless sensor networks, networking and intelligent information gathering technology replace the independent single model. Wireless sensor networks have become an important research focus in the IT field [1-2].Power energy is the primary energy supply form in modern society. Power systems has become a lifeline for the whole society [3][4]. With the development of the national economy, the demand for electricity in both urban and rural areas is increasing in China contiguously. By the end of the year 2016, Chinese annual electricity generation was 5 trillion and 920 billion kw/h, ranking first in power output globally.The production, transmission and use of electric energy constitute a complex, timevarying and stochastic dynamic process. As such, the enormous energy demands must be distributed and consumed by the facilities of power generation, transmission and distribution to numerous users [5]. 86http://www.i-joe.org Paper-Power Load Forecasting Based on Wireless Sensor NetworksAny fault in the power network may cause a chain reaction and may result in collapse. This will have disasterous results to the national economy and national security [6]. Therefore, the modern power network must use advanced monitoring, control and scheduling mechanism to maintain its stability and optimal operation. The expansion of the power network to accomidate the increasing demand will lead to the continuous improvement in the automation level of power network management and automatic level of operation [7][8].In the power system automation field, the existing research applies the wireless sensor networks to remote meter reading [9], substation automation [10], transmission line real-time monitoring [11] and early warning [12], etc. At the same time, the data collected by the wireless sensor networks can also be used to forecast the power load and provide a powerful guarantee for the electrical needs of both daily life of the society and industry.The least squares support vector machine replaces the inequality constraints of standard support vect...

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Section: Introductionmentioning

confidence: 99%