Oil immersed inverted current transformer is vulnerable to overvoltage in the transmission line during operation. The accumulative effect of switching impulse voltage can degrade the main insulation and lead to a reduction in insulating property. Frequency domain spectroscopy (FDS) can be used to reflect the insulating property of oil-paper insulation. In order to investigate the FDS characteristics of oil-paper insulation under repeated switching impulse voltage, samples of oil-paper insulation with different thickness were made. Afterwards, the FDS of samples was tested with different number of repeated switching impulse voltage and applied voltage. The results show that more impurities occur in oilpaper insulation under repeated switching impulse voltage, which has a greater impact on the FDS results. Therefore, the FDS characteristic curves can reflect the deterioration characteristics of oil-immersed inverted current transformers under repeated switching impulse voltage. The role of impurities concentration and motion characteristics in the degradation process of oil-paper insulation was analyzed.
For many vehicular safety applications, it is critical to timely and reliably deliver multimedia data from a traveling vehicle to a roadside access point (AP) in an error-prone vehicular ad hoc network (VANET), which is a typical uplink scenario for drive-thru Internet. To achieve this goal, we propose a cooperative retransmission scheme that exploits a common phenomenon in reality, in which consecutive vehicles can naturally form a platoon to reduce energy consumption. We develop a 4-D Markov chain to model the proposed scheme and analyze the uplink throughput of drive-thru Internet, which also reveals some fundamental relationships among traffic flow, platoon parameters, and system throughput. We conduct extensive simulation in OMNeT++ to validate our scheme and the analytical model. Numerical results show that the proposed platoon-based cooperative retransmission scheme significantly improves the uplink throughput of drive-thru Internet, considerably decreases the total transmission times for a given quantity of upload data, and, hence, achieves a greener mobile multimedia communication.
The concept that Smart grid is the future direction of power grid has been a broad consensus around the world.It is of great significance for building the smart power supply and distribution network in iron steel enterprise based on the research of smart grid.This paper describes the development of smart power network and its characteristics,and according to the specific characteristics and needs of steel companies on the smart grid,a preliminary discussion and analysis is provided.
In this paper, we propose a novel antenna architecture called movable antenna (MA) to improve the performance of wireless communication systems. Different from conventional fixed-position antennas (FPAs) that undergo random wireless channel variation, the MAs with the capability of flexible movement can be deployed at positions with more favorable channel conditions to achieve higher spatial diversity gains. To characterize the general multi-path channel in a given region or field where the MAs are deployed, a field-response model is developed by leveraging the amplitude, phase, and angle of arrival/angle of departure (AoA/AoD) information on each of the multiple channel paths under the far-field condition. Based on this model, we then analyze the maximum channel gain achieved by a single receive MA as compared to its FPA counterpart in both deterministic and stochastic channels. First, in the deterministic channel case, we show the periodic behavior of the multi-path channel gain in a given spatial field, which can be exploited for analyzing the maximum channel gain of the MA. Next, in the case of stochastic channels, the expected value of an upper bound on the maximum channel gain of the MA in an infinitely large receive region is derived for different numbers of channel paths. The approximate cumulative distribution function (CDF) for the maximum channel gain is also obtained in closed form, which is useful to evaluate the outage probability of the MA system. Moreover, our results reveal that higher performance gains by the MA over the FPA can be acquired when the number of channel paths increases due to more pronounced small-scale fading effects in the spatial domain. Numerical examples are presented which validate our analytical results and demonstrate that the MA system can reap considerable performance gains over the conventional FPA systems with/without antenna selection (AS), and even achieve comparable performance to the single-input multiple-output (SIMO) beamforming system.
The use of unmanned aerial vehicles (UAVs) as communication platforms is of great significance in future wireless networks, especially for on-demand deployment in temporary events and emergency situations. Although prior works have shown the performance improvement by exploiting the UAV's mobility, they mainly focus on delay-tolerant applications. As delay requirements fundamentally limit the UAV's mobility, it remains unknown whether the UAV is able to provide any performance gain in delay-constrained communication scenarios. Motivated by the above, we study, in this paper, an UAV-enabled orthogonal frequency-division multiple access (OFDMA) network where an UAV is dispatched as the mobile base station (BS) to serve a group of users on the ground. We consider a minimum-rate ratio (MRR) for each user, defined as the minimum instantaneous rate required over the average achievable throughput, to flexibly adjust the percentage of its delay-constrained data traffic. Under a given set of constraints on the users' MRRs, we aim to maximize the minimum average throughput of all users by jointly optimizing the UAV trajectory and OFDMA resource allocation. First, we show that the max-min throughput in general decreases as the users' MRRs become larger, which reveals a fundamental throughput-delay tradeoff in UAV-enabled communications. Next, we propose an iterative parameter-assisted block coordinate descent method to optimize the UAV trajectory and OFDMA resource allocation alternately, by applying the successive convex optimization and the Lagrange duality, respectively. Furthermore, an efficient and systematic UAV trajectory initialization scheme is proposed based on the simple circular trajectory. Finally, simulation results are provided to verify our theoretical findings and demonstrate the effectiveness of our proposed designs.
In this paper, we propose a model for energy cooperation between cellular base stations (BSs) with individual hybrid power supplies (including both the conventional grid and renewable energy sources), limited energy storages, and connected by resistive power lines for energy sharing. When the renewable energy profile and energy demand profile at all BSs are deterministic or known ahead of time, we show that the optimal energy cooperation policy for the BSs can be found by solving a linear program. We show the benefits of energy cooperation in this regime. When the renewable energy and demand profiles are stochastic and only causally known at the BSs, we propose an online energy cooperation algorithm and show the optimality properties of this algorithm under certain conditions. Furthermore, the energy-saving performances of the developed offline and online algorithms are compared by simulations, and the effect of the availability of energy state information (ESI) on the performance gains of the BSs' energy cooperation is investigated. Finally, we propose a hybrid algorithm that can incorporate offline information about the energy profiles, but operates in an online manner.
No abstract is provided for this article.
Wireless networks can be self-sustaining by harvesting energy from radio-frequency (RF) signals. Building on classic cognitive radio networks, we propose a novel method for network coexisting where mobiles from a secondary network, called secondary transmitters (STs), either harvest energy from transmissions by nearby transmitters from a primary network, called primary transmitters (PTs), or transmit information if PTs are sufficiently far away; STs store harvested energy in rechargeable batteries with finite capacity and use all available energy for subsequent transmission when batteries are fully charged. In this model, each PT is centered at a guard zone and a harvesting zone that are disks with given radiuses; a ST harvests energy if it lies in some harvesting zone, transmits fixed-power signals if it is outside all guard zones or else idles. Based on this model, the spatial throughput of the secondary network is maximized using a stochastic-geometry model where PTs and STs are modeled as independent homogeneous Poisson point processes (HPPPs), under the outage constraints for coexisting networks and obtained in a simple closed-form. It is observed from the result that the maximum secondary throughput decreases linearly with the growing PT density, and the optimal ST density is inversely proportional to the derived transmission probability for STs.
Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network's capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS's antenna radome to directly reconfigure the signal radiation to/from the BS's antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS's antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS's reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS's reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.
This paper studies the simultaneous wireless information and power transfer (SWIPT) in a multiuser wireless system, in which distributed transmitters send independent messages to their respective receivers, and at the same time cooperatively transmit wireless power to the receivers via energy beamforming. Accordingly, from the wireless information transmission (WIT) perspective, the system of interest can be modeled as the classic interference channel, while it also can be regarded as a distributed multiple-input multiple-output (MIMO) system for collaborative wireless energy transmission (WET). To enable both information decoding (ID) and energy harvesting (EH) in SWIPT, we adopt the low-complexity time switching operation at each receiver to switch between the ID and EH modes over scheduled time. For the hybrid system, we aim to characterize the achievable rate-energy (R-E) trade-offs by various transmitter-side collaboration schemes. Specifically, to facilitate the collaborative energy beamforming, we propose a new signal splitting scheme at the transmitters, where each transmit signal is generally split into an information signal and an energy signal for WIT and WET, respectively. With this new scheme, first, we study the two-user SWIPT system over the fading channel and derive the optimal mode switching rule at the receivers as well as the corresponding transmit signal optimization to achieve various R-E trade-offs. We also compare the R-E performance of our proposed scheme with transmit energy beamforming and signal splitting against two existing schemes with partial or no cooperation of the transmitters. Next, the general case of SWIPT systems with more than two users is studied, for which we propose a practical transmit collaboration scheme by extending the result for the two-user case: we group users into different pairs and apply the cooperation schemes obtained in the two-user case to each paired group. Furthermore, we present a benchmarking scheme based on joint cooperation of all the transmitters inspired by the principle of interference alignment, against which the performance of the proposed scheme is compared.
Abstract Drilling parameter optimization constitutes a pivotal technique for expediting the intricate processes involved in oil and gas drilling operations. Presently, optimization strategies predominantly hinge on statistical data analysis or data-driven models and optimization algorithms. However, these methods often overlook the real-time changes occurring in the downhole rock-breaking environment. Additionally, achieving high precision with data-driven rate of penetration (ROP) models proves challenging, as they may struggle to accurately reflect the intricate mechanisms involved in rock-breaking. Moreover, the inherent randomness and uncertainty associated with optimization algorithms pose difficulties in ensuring the consistent acceleration of the drilling process. This study introduces areal-time targets-based intelligent optimization and decision-making system tailored specifically for field drilling operations.The system workflow comprises data preprocessing, predictive optimization, and interpretive decision-making stages. In the crucial modeling and prediction phase, we developed a highly accurate ROP prediction model using advanced machine learning techniques and mechanistic empirical constraints. By employing three model updating mechanisms alongside a 3D heat map visualization technique, the most robust current ROP model can be selected, thereby enhancing the adaptability of the model to dynamic downhole environment. This process establishes a solid foundation for achieving specific ROP targets. In the optimization decision stage, this work proposes the optimal path decision process with specific target parameter optimization and small parameter fluctuation. The process begins by determining the ROP enhancement target for the next stage based on the average ROP of the current formation. Subsequently, the eligible combinations of drilling parameters undergo a preliminary screening. Afterwards, the optimal path decision-making process is informed by the consideration of parameter fluctuation variance and mechanical specific energy as a combined evaluation indicator. This approach ensures that drilling parameters are adjusted with utmost efficiency, all while meeting the predetermined ROP enhancement target. Moreover, it facilitates stable speed enhancement, minimizes drilling parameter fluctuations, and effectively manages complex working conditions. The application of the system in the Huizhouoilfield in China demonstrates the stability and effectiveness of the method. The optimized drilling parameters exhibit low fluctuations, with a maximum reductionin parameter variance of 63.8%compared to the original tuned parameters, ensuring robust real-time operability. Notably, the method achieves asmooth speed-up of 15% to 25%, while minimizing mechanical specific energy by a maximum of 19% and reducing bit energy loss. This study contributes novel insights into real-time drilling parameter optimization, with a dynamic adaptation mechanism poised to incorporate emerging drilling data streams.
In this paper, we propose a new form of polarization reconfigurable antennas (PRAs) that can form linear, circular, and general elliptical polarizations assisted by phase shifters (PSs). With PRAs, polarforming is achieved, which enables the antenna to shape its polarization into a desired state for aligning with that of the received electromagnetic (EM) wave or reconfiguring that of the transmit EM wave. To demonstrate the benefits of polarforming, we investigate a PRA-aided single-input single-output (SISO) communication system equipped with tunable PSs for polarization adaptation. We characterize the achievable signal-to-noise ratio (SNR) at the receiver as a function of the phase shifts of PS-based PRAs. Moreover, we develop an alternating optimization approach to maximize the SNR by optimizing the phase shifts at both the transmitter and receiver. Finally, comprehensive simulation results are presented, which not only validate the effectiveness of polarforming in mitigating the channel depolarization effects, but also demonstrate its substantial performance improvement over conventional systems.
Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV's trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.
The necessity,advantages and resistance of offshore wind power in Zhejiang province are analyzed combined with its current situation of economic development and geographical and climatic characteristics.The key technologies in offshore wind power construction are analyzed.Meanwhile,the offshore wind power type selection,offshore substation access principles,access voltage grade selection and cable laying considerations for the actual situation in Zhejiang are provided.
