Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

Stochastic modelling of stratospheric temperature

<p>A stochastic model for daily-spatial mean stratospheric temperature over a given area is suggested. The model is a sum of a deterministic seasonality function and a Lévy driven vectorial Ornstein-Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the model is an order 4 continuous-time autoregressive (CAR(4)) process, derived from data analysis suggesting an order 4 autoregressive (AR(4)) process to model the deseasonalized stochastic temperature data empirically. In this analysis, temperature data as represented in ECMWF re-analysis model products are considered. The residuals of the AR(4) process turn out to be normal inverse Gaussian distributed random variables scaled with a time dependent volatility function. In general, it is possible to show that the discrete time AR(p) process is closely related to CAR(p) processes, its continuous counterpart. An equivalent effort is made in deriving a dual stochastic model for stratospheric temperature, in the sense that the year is divided into summer and winter seasons. However, this seems to further complicate the modelling, rather than obtaining a simplified analytic framework. A stochastic characterization of the stratospheric temperature representation in model products, such as the model proposed in this paper, can be used in geophysical analyses to improve our understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modelling and prediction of large-scale middle atmospheric events like sudden stratospheric warmings. Through stratosphere-troposphere coupling, this is important in the work towards an extended predictability of long-term tropospheric weather forecasting.</p>

A Stochastic Characterization of the Capture Zone in Pursuit-Evasion Games

Pursuit-evasion games are used to define guidance strategies for multi-agent planning problems. Although optimal strategies exist for deterministic scenarios, in the case when information about the opponent players is imperfect, it is important to evaluate the effect of uncertainties on the estimated variables. This paper proposes a method to characterize the game space of a pursuit-evasion game under a stochastic perspective. The Mahalanobis distance is used as a metric to determine the levels of confidence in the estimation of the Zero Effort Miss across the capture zone. This information can be used to gain an insight into the guidance strategy. A simulation is carried out to provide numerical results.

Modeling the Energy Harvested by an RF Energy Harvesting System Using Gamma Processes

In an Energy Harvesting system (EHS) the gamma process is used to model the electromagnetic energy received from radiofrequency (RF) radiation. The stochastic characterization of the harvested energy as a continuous-time stochastic process, namely, gamma process, is obtained from the Nakagami-m fading model, which describes the signal reception in a large amount of types of radiofrequency channels. Using the gamma process, some performance measures of the EHS system are obtained. Also, a transmission policy subject to different fading conditions is considered.