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As research on wind energy has progressed, it has broadened from a focus on the wind turbine to include the entire wind farm. In particular, methods to mitigate the negative effects of upstream wakes on downstream turbines have received significant attention. One such mitigation method is axial induction control (AIC) in which upstream turbines are derated to reduce the momentum deficits in their wakes, leaving higher speed flow for downstream turbines. If performed correctly, it is theorized that the power production gains in downstream turbines can compensate for the power sacrificed by derating upstream turbines. Previous work has indicated that the “excess” energy left in the wake of the derated turbine is along the edges of the wake such that a turbine placed directly downstream will see little to no increase in power. To address this hypothesis, we performed a control and treatment experiment with model-scale turbines in a wide flume. Five turbines were arranged in three successive streamwise rows, with the first two rows consisting of two aligned turbines, while three turbines with small transverse spacing were placed in the third row, the central of which was also streamwise-aligned with the upstream two turbines. This arrangement was used to evaluate the difference in power production primarily among the turbines in the third row when the upstream turbines were derated. Particle image velocimetry (PIV) was used to measure the wake in the streamwise-vertical planes along the centerline of the array and along the rotor tips of the centerline turbines between all rows, and high accuracy power measurements were recorded from each turbine. The results show that the total power of the array was decreased while implementing AIC but that individual turbine performance differed from predictions. PIV results show that mean kinetic energy (MKE) is redistributed to the edges of the wakes as has been previously hypothesized. We provide an analysis of the results that connects both the power and flow measurements and that highlights several of the aspects of wind turbine wake flows that make them so complex and challenging to study.

In this paper, remote error monitoring techniques for electricity meters are overviewed suggesting their utilization for in-service surveillance assistance. It is discussed that in-service error observation could provide valuable input, contributing to the timely detection of batches of meters reaching nonconformance status. The payback period analysis of the deployment of a remote error monitoring solution is considered. However, it is pointed out that such an analysis lacks input information describing the relationship between the remote monitoring system’s performance and its ability to detect nonconformance of the batch. It is also noticed that there is no published methodology for grading the status of an entire batch of meters referring to error estimates of a subset of the meters, when the uncertainty of estimation is rather high.

This article presents a comparative experimental evaluation of some power calculation methods used for three-phase and single-phase systems. In order to make the comparison, methods based on three reference frames, the natural one, αβ and dq, are considered. This paper addresses the experimental comparison for single-phase systems facing sinusoidal and non-sinusoidal waveforms in both linear and non-linear loads. The results are obtained from an experimental platform consisting of a controlled voltage inverter and an acquisition system based on a TMS320F28335, where the power calculation algorithms are implemented. The experimental comparison of the different methods for active and reactive power calculation is made in terms of accuracy and processing time in digital implementation.

This paper concerns the optimal design and comparative analysis of resonant and non-resonant high-frequency GaN-based rotating transformers. A multi-physical design approach is employed, in which magnetic, electrical, and thermal models are coupled. The results are verified by experiments. Two different optimization objectives are considered; firstly, the efficiency of two standard core geometries is maximized for a required output power level. Secondly, a geometrical optimization is performed, such that the core inertia is minimized for the desired output power level. The results of both design optimizations have shown large improvements in terms of output power and core inertia as a result of applying series−series resonant compensation.

Reducing or optimizing electrical power consumption is one of the most fundamental goals within the current frameworks of smart energy management. This approach would be spread from the industrial plants, characterized by high consumptions often well distributed throughout the day, down to the domestic utilities, typically discontinuous and with limited consumption at the single user level. More specifically, it is desirable for the latter case to be able to control in a simple and effective way the power consumption of typical household appliances by means of technologies that are already used and spread (such as tablets and smartphones) to become aware of their actual impact, both economic and environmental. To this aim, the authors present the proof-of-principle of user-friendly monitoring system for power consumption awareness based on the recent technologies of Internet of Things (IoT) and Augmented Reality (AR). In particular, common devices such as smartphones associated along with appropriate measurement nodes and a suitable app, developed to the purpose, allow consumers to view in AR environment electrical consumption of their domestic electrical loads to consciously decide whether to switch them off. Performance of both sensor nodes and AR environment were preliminarily assessed in either laboratory experiments or actual household context, highlighting the promising effectiveness of the proposed approach.