Reliability analysis of single-phase photovoltaic inverters with reactive power support

Reactive power support is expected to be an emerging ancillary requirement for single-phase photovoltaic (PV) inverters. This work assesses related reliability issues and focuses on the second stage or inversion process in PV inverters. Three PV inverter topologies are analyzed and their reliability is determined on a component-by-component level. Limiting operating points are considered for each of these topologies. The capacitor in the dc link, the MOSFETs in the inverting bridge, and the output filter are the components affected. Studies show that varying power-factor operation with a constant real power output increases the energy storage requirement as well as the capacitance required in the dc link in order to produce the double-frequency power ripple. The overall current rating of the MOSFETs and output filter must also be sized to accommodate the current for the apparent power output. Modeling of the inverter verifies the conditions for each of the components under varying reactive power support commands. It is shown that the production of reactive power can significantly increase the capacitance requirement, but the limiting reliability issue comes from the increased output current rating of the MOSFETs

Wigner distribution transformations in high-order systems

By combining the definition of the Wigner distribution function (WDF) and the matrix method of optical system modeling, we can evaluate the transformation of the former in centered systems with great complexity. The effect of stops and lens diameter are also considered and are shown to be responsible for non-linear clipping of the resulting WDF in the case of coherent illumination and non-linear modulation of the WDF when the illumination is incoherent. As an example, the study of a single lens imaging systems illustrates the applicability of the method.Comment: 16 pages, 7 figures. To appear in J. of Comp. and Appl. Mat

Frequency-Selective PAPR Reduction for OFDM

We study the peak-to-average power ratio (PAPR) problem in orthogonal frequency-division multiplexing (OFDM) systems. In conventional clipping and filtering based PAPR reduction techniques, clipping noise is allowed to spread over the whole active passband, thus degrading the transmit signal quality similarly at all active subcarriers. However, since modern radio networks support frequency-multiplexing of users and services with highly different quality-of-service expectations, clipping noise from PAPR reduction should be distributed unequally over the corresponding physical resource blocks (PRBs). To facilitate this, we present an efficient PAPR reduction technique, where clipping noise can be flexibly controlled and filtered inside the transmitter passband, allowing to control the transmitted signal quality per PRB. Numerical results are provided in 5G New Radio (NR) mobile network context, demonstrating the flexibility and efficiency of the proposed method.Comment: Accepted for publication as a Correspondence in the IEEE Transactions on Vehicular Technology in March 2019. This is the revised version of original manuscript, and it is in press at the momen

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Large-scale inhomogeneities of the intracluster medium: improving mass estimates using the observed azimuthal scatter

Using a set of hydrodynamical simulations of 62 galaxy clusters and groups we study the ICM of inhomogeneities, focusing on the ones on the large scale that, unlike clumps, are the most difficult to identify. To this purpose we introduce the concept of residual clumpiness, C_R, that quantifies the large-scale inhomogeneity of the ICM. After showing that this quantity can be robustly defined for relaxed systems, we characterize how it varies with radius, mass and dynamical state of the halo. Most importantly, we observe that it introduces an overestimate in the determination of the density profile from the X-ray emission, which translates into a systematic overestimate of 6 (12)% in the measurement of M_gas at R_200 for our relaxed (perturbed) cluster sample. At the same time, the increase of C_R with radius introduces also a ~2% systematic underestimate in the measurement of the hydrostatic-equilibrium mass (M_he), which adds to the previous one generating a systematic ~8.5% overestimate in f_gas in our relaxed sample. Since the residual clumpiness of the ICM is not directly observable, we study its correlation with the azimuthal scatter in the X-ray surface brightness of the halo and in the y-parameter profiles. We find that their correlation is highly significant (r_S = 0.6-0.7), allowing to define the azimuthal scatter measured in the X-ray surface brightness profile and in the y-parameter as robust proxies of C_R. After providing a function that connects the two quantities, we obtain that correcting the observed gas density profiles using the azimuthal scatter eliminates the bias in the measurement of M_gas for relaxed objects, which becomes (0+/-2)% up to 2R_200, and reduces it by a factor of 3 for perturbed ones. This method allows also to eliminate the systematics on the measurements of M_he and f_gas, although a significant halo to halo scatter remains. (abridged)Comment: 18 pages, 17 figures, 3 tables. Submitted to MNRAS, revised after referee's comment