For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE's next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field. As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield.
DUBAI, UNITED ARAB EMIRATES - A Yemen rebel drone strike this week on a critical Saudi oil pipeline shows that the otherwise-peaceful sandy reaches of the Arabian Peninsula now are at risk of similar assault, including an under-construction nuclear power plant and Dubai International Airport, among the world's busiest. U.N. investigators said the Houthis' new UAV-X drone, found in recent months during the Saudi-led coalition's war in Yemen, likely has a range of up to 1,500 km (930 miles). That puts the far reaches of both Saudi Arabia and the United Arab Emirates, the two main opponents of the Iranian-allied Houthi rebels in Yemen, within reach of drones difficult to detect and track. Their relatively simple design, coupled with readily available information online, makes targeting even easier, analysts say. "These installations are easily findable, like on Google Earth," said Tim Michetti, an expert on illicit weapons technology with experience in Yemen.
U.S. stocks were slightly higher Wednesday morning as utility companies climbed. Energy companies were trading lower as the price of oil continued to slip. Stocks are at their lowest levels in two months after large losses in two of the last three days. The Dow Jones industrial average advanced 31 points, or 0.2%, to 18,097 as of 10:05 a.m. The Standard & Poor's 500 index rose 5 points, or 0.2%, to 2,132.
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.
Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its "frequency domain". Then we proposed a novel attention mechanism to select relevant time series, and use its "frequency domain" information for forecasting. We applied the proposed model on several real-world tasks and achieved the state-of-the-art performance in all of them with only one exception. We also show that to some degree the learned filters play the role of bases in discrete Fourier transform.