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Grid synchronization stability is an important research topic for grid-forming converters. To prevent instability and undesired oscillations, the small-signal modelling and stability analysis must be performed, which require the line impedance information. Nevertheless, the line impedance may be unknown in most cases. As a consequence, the designed control parameters may fail to satisfy the stability requirement. To fill in this research gap, this paper presents a geometrical approach to evaluate and improve the small-signal synchronization stability of grid-forming converters. The proposed analytical approach maps the line impedance variation on a two-dimension plane and identifies the stability boundary in a geometrical way. As a benefit, the controller of grid-forming converters can be explicitly designed to achieve the robust synchronization stability. Finally, the feasibility of the proposed technique is verified by simulation and hardware experimental results.

Distributed generations (DGs) have been increasingly employed in AC microgrids to provide carbon-free power supply. To coordinate multiple DGs, careful attention must be paid to the synchronization stability of their interfaced inverters. Otherwise, low-frequency oscillations and even loss of synchronism may occur and pose a great threat to the system. The conventional solutions towards this issue are to derive the microgrid state-space model and evaluate its stability based on the roots of the characteristic equation. However, tremendous efforts shall be spent on modeling as the number of DG increases. This paper provides an alternative way, which localizes the target of system synchronization stability into individual inverters. It is revealed and proven that microgrid synchronization stability can be guaranteed if all the inverters are designed to have passive synchronization behaviors. To fulfill this requirement and achieve a satisfactory phase margin, an auxiliary controller is designed to reshape frequency-power characteristics of inverters. Finally, case studies of a three-DG microgrid verify the theoretical findings as well as the effectiveness of the auxiliary controller.

In more-electronics power systems, grid-forming power converters, which operate as ac voltage sources, regulate the grid frequency and voltages in replacement of synchronous generators. Notably, grid impedances greatly influence the small signal and voltage stability of grid-forming converters. As such, prior knowledge of grid impedances can be very helpful for controller design. However, grid impedance estimation schemes are normally designed for current-controlled grid-following converters. Moreover, they are either very complicated or only yield grid inductances in a generally intrusive way. To fill this research gap, an impedance estimation method well suited to grid-forming converters is proposed. The method consists of four operating modes, which work well in voltage and power control cases. In the voltage control case, the voltage amplitude perturbation or phase angle information is exploited. Subsequently, the grid inductance and resistance are derived from power measurement. Alternatively, the active or reactive power information serves to estimate the grid impedance in the power control case. The proposed method features an easy implementation without any harmonic distortion, safety concern, or dependence on control parameters. Moreover, the method operates nonintrusively in most scenarios. Furthermore, a novel Kalman filtering scheme is proposed to provide added incentives. Finally, simulation and experimental results validate the effectiveness and simplicity of the proposed method.

It has been shown that it is impossible to achieve stringent timely delivery guarantees in a large network without having complete information of all future packet arrivals. In order to maintain desirable performance in the presence of uncertainty of future, a viable approach is to add redundancy by increasing link capacities. This paper studies the amount of capacity needed to provide stringent timely delivery guarantees. We propose a low-complexity online algorithm and prove that it only requires a small amount of redundancy to guarantee the timely delivery of most packets. Furthermore, we show that in large networks with very high timely delivery requirements, the redundancy needed by our policy is at most twice as large as the theoretical lower bound. For practical implementation, we propose a distributed protocol based on this centralized policy. Without adding redundancy, we further propose a low-complexity order-optimal online policy for the network. The simulation results show that our policies achieve much better performance than the other state-of-the-art policies.

As binary code is storage efficient and fast to compute, it has become a trend to compact real-valued data to binary codes for the nearest neighbors (NN) search in a large-scale database. However, the use of binary code for the NN search leads to low retrieval accuracy. To increase the discriminability of the binary codes of existing hash functions, in this paper, we propose a framework of double-bit quantization and index hashing for an effective NN search. The main contributions of our framework are: first, a novel double-bit quantization (DBQ) is designed to assign more bits to each dimension for higher retrieval accuracy; second, a double-bit index hashing (DBIH) is presented to efficiently index binary codes generated by DBQ; and third, a weighted distance measurement for DBQ binary codes is put forward to re-rank the search results from DBIH. The empirical results on three benchmark databases demonstrate the superiority of our framework over existing approaches in terms of both retrieval accuracy and query efficiency. Specifically, we observe an absolute improvement on precision of 10%–25% in most cases and the query speed increases over 30 times compared to traditional binary embedding methods and linear scan, respectively.

The problem of allocating jobs to appropriate servers in cloud computing is studied in this paper. We consider that the jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We study this problem for both systems with persistent jobs, such as video streaming, and systems with dynamic jobs, such as database queries. For both systems, we propose online job allocation policies with low complexity. For systems with persistent jobs, we prove that our policies can achieve a given hard allocation ratio requirement with the least capacity. For systems with dynamic jobs, the capacity needed for our policies to achieve the hard allocation ratio requirement is close to a theoretical lower bound. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two even, when the jobs arrive according to some random process.

In this paper, two different micro probes (D-probe and GSSG probe) are studied. A specially designed PCB was used to compare the electrical performances of DUTs with de-embedded fixtures for the micro probes and 2.9 mm connectors. Smart Fixture De-embedding (SFD) was used to de-embed the S-parameters of the DUT from the test fixtures. The results showed consistent results of DUTs using both micro probes and 2.92 mm connectors up to nearly 40GHz.

This paper studies the problem of using minimum power to provide satisfactory performance for real-time applications over unreliable and fading wireless channels. We demonstrate that this problem can be formulated as a linear programming problem. However, this formulation involves exponentially many constraints, and many parameters are either unavailable or difficult to compute, which makes it infeasible to employ standard techniques to solve the linear programming problem. Instead, we propose a simple online algorithm for this problem. We prove that our algorithm provides satisfactory performance to all real-time applications, and the total power consumption can be made arbitrarily close to the theoretical lower bound. Furthermore, our algorithm has very low complexity and does not require knowledge of many parameters in the linear programming problem, including the distributions of channel qualities. We further extend our algorithm to address systems where real-time applications and non-real-time ones coexist. We demonstrate that our algorithm achieves both low total power consumption and high utility for each non-real-time client while satisfying the performance requirements of real-time clients. Simulation results further provide some insights in setting important parameters of our algorithms, and demonstrate that our algorithm indeed achieves a significant reduction in power consumption.

WiFi offloading, where mobile users opportunistically obtain data through WiFi rather than cellular networks, is a promising technique for greatly improving spectrum efficiency and reduce cellular network congestion. We consider a system where the service provider deploys multiple WiFi hotspots to offload mobile traffic, and study the scheduling policy to maximize the amount of offloaded data. Since users’ movements are unpredictable, we focus on online scheduling policy, where APs have no knowledge of users’ mobility patterns. We study the performance of online policies by comparing them against the optimal offline policy. We prove that any work-conserving policy is able to offload at least half as much data as the offline policy, and then propose an online policy such that when the requested data by each user is very large, the policy can offload $(e-1)/e$ as much data as the offline policy, where $e$ is Euler’s constant. We further study the case where the service provider can increase the capacity of WiFi so as to provide some guarantees on the amount of offloaded data. We derive a lower-bound on the trade-off between capacity and the amount of offloaded data, and propose a simple online policy that achieves this lower bound. In addition, we show that our policy only needs half as much capacity as current mechanisms to provide the same performance guarantee.

The increasing penetration of renewable energy resources facilitates the carbon footprint reduction process yet reduces the power system inertia. As a result, the grid frequency and the rate of change of frequency (RoCoF) might probably go beyond the normal range, resulting in unexpected load shedding, generator tripping, and even frequency instability. To address this problem, grid-connected inverters are designed to participate in frequency regulation and provide the equivalent inertial support. Nevertheless, the inertia emulation effect is affected by the inverter synchronization dynamic and high RoCoF events may occur as the result of poor synchronization dynamics. In view of this limitation, a synthetic inertia control is developed in this article considering the synchronization dynamics. The synthetic inertia principles and control design guideline are explicitly provided. Finally, hardware experimental results of a scaled-down power system prototype are provided to validate the effectiveness of the proposed approach.