Jooyoung Jeon

Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this article, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate vector autoregressive moving average-generalized autoregressive conditional heteroscedastic (VARMA-GARCH) model, with a Student t error distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms.

A new "series-type" Doherty amplifier for miniaturization

A new topology of Doherty amplifier is proposed to reduce the circuit size by eliminating the bulky 3 dB 90/spl deg/ hybrid coupler. Unlike the classical Doherty amplifier, the carrier and peak amplifiers are connected in series together with an impedance-inverting network connecting the outputs of both amplifiers. A simple matching circuit between the peak and carrier amplifier replaces the input hybrid coupler. In this way, the need for an input hybrid coupler is eliminated, facilitating integration and miniaturization. The fabricated 2.45 GHz amplifier module using the proposed topology demonstrates enhanced efficiencies in the low- to mid-power ranges and shows good gain flatness. This approach is promising for highly-integrated Doherty-type amplifiers for handset applications.

A Linearity-Enhanced Compact Series-Type Doherty Amplifier Suitable for CDMA Handset Applications

A high-linearity series-type Doherty amplifier that does not use bulky 3 dB quadrature hybrid coupler is developed for mobile handset applications. A predistorter is employed at the input to improve the ACPR performance at the high-power region, which effectively extends maximum linear power range. For size and stability considerations, the phasing circuits between the carrier and peak amplifiers have been realized using high-pass T-networks. The series-type Doherty amplifier of this work shows PAEs of 18% and 42.8% at 16 dBm and 28 dBm, while meeting the IS-95A ACPR requirement of -42 dBc

Probabilistic forecasting of wave height for offshore wind turbine maintenance

Wind power continues to be the fastest growing source of renewable energy. This paper is concerned with the timing of offshore turbine maintenance for a turbine that is no longer functioning. Service vehicle access is limited by the weather, with wave height being the important factor in deciding whether access can be achieved safely. If the vehicle is mobilized, but the wave height then exceeds the safe limit, the journey is wasted. Conversely, if the vehicle is not mobilized, and the wave height then does not exceed the limit, the opportunity to repair the turbine has been wasted. Previous work has based the decision as to whether to mobilize a service vessel on point forecasts for wave height. In this paper, we incorporate probabilistic forecasting to enable rational decision making by the maintenance engineers, and to improve situational awareness regarding risk. We show that, in terms of minimizing expected cost, the decision as to whether to send the service vessel depends on the value of the probability of wave height falling below the safe limit. We produce forecasts of this probability using time series methods specifically designed for generating wave height density forecasts, including ARMA-GARCH models. We evaluate the methods in terms of statistical probability forecast accuracy, as well as monetary impact, and we examine the sensitivity of the results to different values of the costs.

A physics-based GaAs PHEMT noise model for low drain bias operation using characteristic potential method

A new physics-based noise model of a GaAs PHEMT is developed using the characteristic potential method (CPM). The model calculates the intrinsic noise current sources using CPM. Combined with the extrinsic noise parameters extracted from the measured S-parameters, the model reproduces four noise parameters of the device accurately under low drain bias voltages without using any fitting parameters. The model is verified with a 0.2-/spl mu/m GaAs PHEMT and shows excellent agreement with the measurements for all the noise parameters up to a drain voltage of 1 V Also, the proposed method allows the simulation of the microscopic noise distribution and thus allows one to obtain a physical understanding of noise mechanisms inside the device.