Search

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. [ABSTRACT FROM AUTHOR]

An $(r, \delta)$ -locally repairable code ($(r, \delta)$ -LRC for short) was introduced by Prakash et al. for tolerating multiple failed nodes in distributed storage systems, which was a generalization of the concept of $r$ -LRCs produced by Gopalan et al.. An $(r, \delta)$ -LRC is said to be optimal if it achieves the Singleton-like bound. Recently, Chen et al. generalized the construction of cyclic $r$ -LRCs proposed by Tamo et al. , and constructed several classes of optimal $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively in terms of a union of the set of zeros controlling the minimum distance and the set of zeros ensuring the locality. Following the work of , , this paper first characterizes $(r, \delta)$ -locality of a cyclic code via its zeros. Then we construct several classes of optimal cyclic $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively from the product of two sets of zeros. Our constructions include all optimal cyclic $(r,\delta)$ -LRCs proposed in , , and our method seems more convenient to obtain optimal cyclic $(r, \delta)$ -LRCs with flexible parameters. Moreover, many optimal cyclic $(r,\delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively with $(r+\delta -1)\nmid n$ can be obtained from our method. [ABSTRACT FROM AUTHOR]

Ground faults in field/rotor and generator stator circuits are serious events that can lead to damage, costly repair, extended outages, and loss of revenue. This paper explores advances in field/rotor circuit ground fault and stator ground fault protection. These advanced protection strategies employ ac injection, ground differential protection, and the use of hybrid grounding to reduce both internal generator ground fault levels and facility ground levels in utility-paralleled operating modes. [ABSTRACT FROM AUTHOR]

Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts – trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30 percent in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems. [ABSTRACT FROM AUTHOR]

The vision recognition module is one of the very important modules in the normal operation of the intelligent plant cultivation robot. In the original vision module, by improving the original YOLOV3(The algorithm proposed in YOLOv3: An Incremental Improvement), a more excellent YOLOV3 was obtained to recognize plants under normal conditions. However, the improved YOLOV3 does not perform well in the visual recognition process when there is a lot of noise interference. To make the visual recognition module continue to work properly, in this paper, the better Cycle GAN (generative adversarial networks) model is proposed to deal with noise based on three common noises, and finally, through a lot of experiments, it is proved that the improvement has some significance for the vision recognition module, which extends the use of the vision recognition module and makes the intelligent plant cultivation robot avoid the interference of noise. [ABSTRACT FROM AUTHOR]

Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]

This paper provides the electrical equivalent circuits of high-resistance grounded (HRG) power systems other than the delta–wye-connected utility power supply transformer included in Part I of this paper. This paper also provides the HRG of a medium-voltage generator for connection to an industrial power system and to a utility power system, and this paper discusses a line–ground fault with fault resistance. Three-line diagrams of ungrounded power systems are also included to illustrate the HRG application and its effect on the ground fault current during the line–ground fault. The damage at the fault location is based on the assumption that the fault remains a line–ground fault until it is cleared by appropriate ground fault protection relays. The fault resistance and the fault current that have an effect on the damage at the fault location are included. This paper provides guidance to update the current edition of the IEEE STD. 142, with respect to HRG systems. [ABSTRACT FROM AUTHOR]

A new method is presented for the design of kinematotropic linkages based on 2-DOF kinematic chains that generate more than one surface. As an example of the proposed method, a kinematotropic linkage is obtained by studying a special case of the Bohemian dome which has two different parametrizations constructed by translation of circles and, therefore, two different hybrid kinematic chains can be designed to generate the same Bohemian dome. Each of these hybrid kinematic chains can generate two different surfaces and, thus, can be used in the proposed method. Parametrizations for the secondary surfaces are then obtained and studied. A total of 27 motion branches are found in the configuration space of this kinematotropic linkage. The singularities in the configuration space are further determined using the properties of the surfaces. The resultant linkage offers an explanation of Wholhart's queer-square linkage other than its original paper folding. As part of the analysis of this example, the relationship between the properties of self-intersections in generated surfaces and the configuration space of the generator linkage is studied for the first time, leading to a description of motion branches related to self-intersections of generated surfaces. [ABSTRACT FROM AUTHOR]

Long period digital random sequence plays an important role in reliable communications and high security scenarios. This paper improved the method of generating long period digital random sequences based on the Residue Number System (RNS) and the Chinese Remainder Theorem (CRT), and a sequence mapping method after CRT extension. This paper proves that the period of sequence after mapping will not degenerate if the modulus used in the mapping stage is coprime with the period of the original sequence. By using the parallelism of RNS, the proposed method can generate sequences at high speed with fewer hardware resources. The NIST test results show that the pass rate of each test item is above 98.40%, which meets the NIST test confidence requirements, confirming the randomness of the generated sequences. An image encryption test is given as one of the example applications of the generated sequences. On the theoretical basis, by jointly optimizing the sequence mapping and iteration procedure, a hardware implementation architecture is also presented in this paper. The implementation is based on Xilinx XC7Z020CLG484-3 FPGA and compared with the implementations of classical chaotic maps. The results show that the proposed architecture has longer sequence period with less hardware resource consumption and higher generation speed and is more general. Meanwhile, the proposed architecture has fast phase switching ability, which is about 10 clock periods. This is one of the key attributes when the sequence is used in communication systems. [ABSTRACT FROM AUTHOR]

Multiple strokes are the main factors affecting the safe and stable operation of power equipment. As the core lightning protection equipment, arresters are also frequently thermally collapsed due to multiple strokes. This paper investigates thermal effects of polymer-housed metal-oxide surge arrester (PMOSA) under multiple strokes. The comparative experiments of PMOSA lightning impulse are carried out through multiple strokes of experiment platform, including continuous lightning stroke and multiple strokes. Furthermore, the temperature distribution of the housing surface is obtained by infrared imager for thermal effect analysis. Subsequently, some typical DC parameters are tested to explore the association of impulse aging and thermal effects of the arrester under multiple strokes. Finally, a thermal-electric coupling model (TECM) is constructed to investigate the influence of some factors on the temperature of PMOSA from the perspective of materials and structure. The research in this paper can provide a theory reference and brief data support for improving the heat dissipation performance of arresters under multiple strokes based on structural optimization design and material modification. [ABSTRACT FROM AUTHOR]

Power system inertia is declining and is increasingly variable and uncertain in regions where the penetration of non-synchronous generation and interconnectors is growing. This presents a challenge to power system operators who must take appropriate actions to ensure the stability and security of power systems relying on short-term forecasts of the system’s inertial response. Existing models to forecast inertia fail to quantify uncertainty, which may prevent their utilization given the risk aversion of the system operators when handling stability issues. This paper is the first to develop a model to produce calibrated, data-driven probabilistic forecasts of the inertia contribution of transmission-connected synchronous generators. The model provides a necessary tool for system operators to quantify forecast uncertainty, allowing them to manage the risk of frequency instability cost-effectively. The paper demonstrates that the assumption of a Gaussian distribution of uncertainty applied in existing models is not acceptable to accurately forecast the inertial response and provides a satisfactory forecast model by combining non-parametric density forecasting with parametric tail distributions. Moreover, the paper shows that satisfactory predictive performance can only be achieved by adopting a rolling horizon forecast approach to deal with the rapidly changing characteristics of the inertial response in power systems. [ABSTRACT FROM AUTHOR]

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system. [ABSTRACT FROM AUTHOR]

Power system frequency disturbances are caused by various generation and transmission events including generator trips, load disconnections, line trips, etc. Accurate detections of the events are crucial to bulk power system situation awareness and event investigation. This paper utilizes the recent advances of deep learning to build a convolutional neural network model to detect events in an accurate yet straightforward manner. In this paper, the rate of change of frequency and the relative angle shift are converted to images as the inputs of the proposed model. Finally, this paper uses two convolutional neural networks and classifier fusion to achieve the detection result. Compared with the conventional event detection algorithm and the frequency only deep learning model, the proposed model improves the detection accuracy by over 48%. As a promising tool for bulk power system situation awareness, the proposed model requires a short decision time, which is suitable for practical scenarios. [ABSTRACT FROM AUTHOR]

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to privacy and security as they can reveal sensitive commercial information and expose system vulnerabilities. This paper investigates how to release the data for power networks where the parameters of transmission lines and transformers are obfuscated. It does so by using the framework of Differential Privacy (DP), that provides strong privacy guarantees and has attracted significant attention in recent years. Unfortunately, simple DP mechanisms often result in AC-infeasible networks. To address these concerns, this paper presents a novel differentially private mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated power network. Experimental results also show that the obfuscation significantly reduces the potential damage of an attack carried by exploiting the released dataset. [ABSTRACT FROM AUTHOR]

Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture information, making it difficult to annotate with a simple single label. Moreover, wallpaper designers spend significant time to create different styles of wallpaper. For this purpose, this paper proposes to describe wallpaper texture images by using multi-label semantics. Based on these labels and generative adversarial networks, we present a framework for perception driven wallpaper texture generation and style transfer. In this framework, a perceptual model is trained to recognize whether the wallpapers produced by the generator network are sufficiently realistic and have the attribute designated by given perceptual description; these multi-label semantic attributes are treated as condition variables to generate wallpaper images. The generated wallpaper images can be converted to those with well-known artist styles using CycleGAN. Finally, using the aesthetic evaluation method, the generated wallpaper images are quantitatively measured. The experimental results demonstrate that the proposed method can generate wallpaper textures conforming to human aesthetics and have artistic characteristics. [ABSTRACT FROM AUTHOR]

Phase angle measurements from synchrophasors show discontinuities when they roll over from +180 to -180 degrees or vice versa. This paper proposes efficient strategies for unwrapping of the angles in real-time data streams. Examples presented in this paper first show that the phase angle differences across bus voltage phasors can go over 360 degrees in a synchronized power system under stressed conditions. Unwrapped phase angles are shown to be useful in fast reliable islanding detection methodology, and for phase angle arithmetic such as for averaging of phase angles and for phase angle difference calculations. Strategies for handling missing phase angle data in real-time data streams are also discussed. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for synthesizing a novel street-view panorama given a satellite image, as if captured from the geographical location at the center of the satellite image. Existing works approach this as an image generation problem, adopting generative adversarial networks to implicitly learn the cross-view transformations, but ignore the geometric constraints. In this paper, we make the geometric correspondences between the satellite and street-view images explicit so as to facilitate the transfer of information between domains. Specifically, we observe that when a 3D point is visible in both views, and the height of the point relative to the camera is known, there is a deterministic mapping between the projected points in the images. Motivated by this, we develop a novel satellite to street-view projection (S2SP) module which learns the height map and projects the satellite image to the ground-level viewpoint, explicitly connecting corresponding pixels. With these projected satellite images as input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results on two cross-view benchmark datasets demonstrate that our method generates more accurate and consistent images than existing approaches. [ABSTRACT FROM AUTHOR]

Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of ${\mathbb {F}}_{q^{2}}^{*}$. To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection. [ABSTRACT FROM AUTHOR]

The second part of this two-paper series analyzes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for the isolated system of La Graciosa, Canary Islands, Spain. Based on the stochastic programming model developed in its companion paper, the impact of DR and ESS on the location and size of new generation and storage units and the distribution assets to be installed, reinforced, or replaced are defined. Numerical results illustrate the effective performance of the proposed approach. Additionally, a set of metrics showing the welfare achieved by the different stakeholders has been implemented. [ABSTRACT FROM PUBLISHER]

This paper presents a distributed optimal power supply–demand management method by using negawatt trading in a real-time electricity market. In the negawatt trading, power consumers reduce their own power demand and receive monetary reward from the market operator. This paper discusses a power adjustment method via negawatt trading by including charge or discharge power from energy storage systems. In order to derive appropriate electricity incentive prices for consumers and to use energy storage systems in power grids efficiently, we deal with a power adjustment problem by using some future power supply information from generators. We then show a novel market algorithm to solve this problem in a distributed manner through market trading. Finally, numerical simulation results illustrate the effectiveness of our proposed method. [ABSTRACT FROM PUBLISHER]

One important element in the progression of aircraft environmental impact reduction is to reduce their overall weight (without impacting other consumption-oriented performance index, such as drag). In addition to the numerous work conducted in material and structural engineering, from a control viewpoint, this challenge is strongly connected to the need of the development and assessment of dedicated load control strategies in response to gust disturbances. Indeed, the load factors due to gust are considered as sizing criteria during the aircraft conception steps and require specific verification according to the certification process. To this end, a dedicated experimental research program based on wind tunnel (WT) campaigns has been carried out. More specifically, this paper contributions are twofold: 1) to identify the gust load effect using two different versatile frequency-domain techniques, namely, the Loewner interpolation and a modified subspace approach and 2) to design and implement an active closed-loop control to alleviate the gust main effect. The entire procedure is validated in a WT setup, involving a gust generator device and a 2-D aeroelastic airfoil, for varying configuration traveling from sub to transonic airflow and varying angles of attack, emphasizing the effectiveness and robustness of the overall approach. [ABSTRACT FROM AUTHOR]

This paper proposed a distributed uniform controlstrategy for parallel bidirectional interlinking converters(BICs) to realize resilient operation of hybrid ac/dc microgrid (HMG). The proposed control strategy can coordinate the control objectives of BICs in different HMG operation modes with a uniform control structure, thus, to eliminate any mode transition detections. In normal condition, the power coordination between ac and dc subgrids can be achieved, while ac or dc voltage regulation can be maintained when main voltage source(MVS) fails in ac or dc subgrid. The coordination between parallel BICs relies on low-bandwidth neighbourhood communication. With the communication planned, accurate power sharing can be achieved. Communication fault ride-through capability is enabled with less power sharing accuracy. The system reliability and resilience of HMG under various fault conditions can be improved by the proposed control strategy. To verify the effectiveness of the proposed control strategy, the controller hardware-in-the-loop experiment results have been provide in this paper. [ABSTRACT FROM AUTHOR]

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: 1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints and 2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on: 1) 3-bus; 2) IEEE 14-bus system; and 3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers. [ABSTRACT FROM AUTHOR]

This paper introduces a new consensus-based control scheme for distributed power systems to solve the distributed economic dispatch (DED) problem. With the development of renewable energy, smart grid, and microgrid, more and more distributed generators will be connected to power systems. Thus the distributed control schemes become more and more important for power systems to control and manage the distributed equipment. Therefore, some consensus-based methods are published previously to solve the DED problem. But the challenge to apply the consensus-based method is to meet the power balance constraint without the centralized facility, since the value of power mismatch is hard to be obtained for a distributed control system. To solve the problem, this paper proposes the method to combine the frequency control methods with the consensus protocol from graph theory to develop the DED control method, so that the real power between the load and the generation can be balanced during the economic dispatch. With this method, a totally distributed optimal control scheme is obtained. The PI frequency controller and neural network frequency controller are employed to develop the DED control method. Then the communication failure problem is considered in this paper. The robust DED control method is proposed to improve the robustness of this distributed control method, allowing the system to stay at the optimal dispatching even if the communication is failed. In addition, the model of transmission line losses is incorporated in the DED problem to reduce the line losses. Finally, several case studies are given in this paper to verify the proposed method. [ABSTRACT FROM AUTHOR]

A new modular pulsed-power source with fast rise time and step-wise arbitrary output waveform generation is currently under development to drive the Gepulste Elektronenstrahlanlage device investigated at the Institute for Pulsed Power and Microwave Technology (IHM). Acknowledging the complex design procedures necessary to set up a modular pulsed-power source for voltages of up to 120 kV, this paper focuses on the circuit design for a single stage and its validation prior to the generator assembly. Using a semiconductor-based Marx generator topology, the stages are designed to have an output voltage of 1 kV with a pulse current of up to 600 A. When connected to an ohmic load, the measured current rise times are in the order of 46 ns resulting in the current rise rates of up to 10 kA/ $\mu \text{s}$ using the commercial devices. The step-wise arbitrary output waveform is created by generating the switching commands on the stage using a microprocessor and a fast optical synchronization unit. Effective stage shielding is verified by operating the stage in a 100-kV, 2.5-kA electromagnetic interference test bed. This paper presents the design considerations and the corresponding measurements. [ABSTRACT FROM AUTHOR]

In an interesting paper, Prof. C. Ding provided three constructions of cyclic codes of length being a product of two primes. Numerical data shows that many codes from these constructions are best cyclic codes of the same length and dimension over the same finite field. However, not much is known about these codes. In this paper, we explain some of the numerical data by developing a general method on cyclic codes of composite length and on estimating the minimum distance. We also provide a general construction of cyclic codes of composite length which are related to Ding’s constructions. Numerical data shows that it produces many best cyclic codes as well. Finally, we point out how these cyclic codes can be used to construct convolutional codes with large free distance. [ABSTRACT FROM AUTHOR]

Human beings tend to learn unknown knowledge in a gradual process, from the basic to the complex. Based on this point, we propose a progressive learning method for producing service strategies according to requests, with a hierarchical priori knowledge and reinforcement learning. Service strategy aims to guide how to perform home services and takes into consideration the relationship between actions and objects in home environment. In this paper, strategy generation is regarded as a text generation problem in question answering (QA). Firstly, a hierarchical priori knowledge with service-object correlation at the bottom and action-object correlation at the top is constructed to assist the understanding on the relationship of objects and actions in service strategies. Service-object correlation guides how to select proper objects with the correct order, while action-object correlation associates actions in strategies according to selected objects. Based on the hierarchical priori knowledge, a progressive learning method is proposed to make the model produce effective strategies with a sequential cognition, from service-object correlation (objects) to action-object correlation (actions). After that, reinforcement learning is employed to enhance the progressive guidance, by designing rewards in terms of the hierarchical priori knowledge. Finally, the proposed method is tested with both comparative experiments and ablation studies, and the experimental results demonstrate the superiority in producing comprehensive and logical strategies, indicating that the progressive learning method in our paper can further improve the QA performance. [ABSTRACT FROM AUTHOR]

After any major blackout, recreating the exact scene is one of the crucial but foundation steps in postmortem analysis. This helps in identifying and understanding the exact causes and sequence of events to avoid such failures in the future. Rather, power system restoration demands critical skills which involves deployment of appropriate strategies based on information about various factors, notably the extent and duration of the blackout, location of black-started units, interconnections with neighbouring systems, generator capabilities, and selecting appropriate restoration paths. The actual execution of the restoration plan consists of many surprises of unique nature and a lot of learning’s to avoid future occurrences of such incidents. The restoration efforts and failure of Mumbai grid failure on October 12, 2020, are highlighted in this paper. The paper first proposes formulating the dynamic models to get a better insight of the restoration process in the event of critical issues during this incident. The various challenges faced in systematic recovery of such a large system and lessons learnt from it forms focus of the paper. The paper also discusses factors not covered in existing literature, such as weather, fuel availability, and water mass oscillations which plays an important role in the restoration. [ABSTRACT FROM AUTHOR]

A functional $k$ -batch code of dimension $s$ consists of $n$ servers storing linear combinations of $s$ linearly independent information bits. Any multiset request of size $k$ of linear combinations (or requests) of the information bits can be recovered by $k$ disjoint subsets of the servers. The goal under this paradigm is to find the minimum number of servers for given values of $s$ and $k$. A recent conjecture states that for any $k=2^{s-1}$ requests the optimal solution requires $2^{s}-1$ servers. This conjecture is verified for $s \leqslant 5$ but previous work could only show that codes with $n=2^{s}-1$ servers can support a solution for $k=2^{s-2} + 2^{s-4} + \left \lfloor{ \frac { 2^{s/2}}{\sqrt {24}} }\right \rfloor $ requests. This paper reduces this gap and shows the existence of codes for $k=\lfloor \frac {5}{6}2^{s-1} \rfloor - s$ requests with the same number of servers. Another construction in the paper provides a code with $n=2^{s+1}-2$ servers and $k=2^{s}$ requests, which is an optimal result. These constructions are mainly based on extended Simplex codes and equivalently provide constructions for parallel Random I/O (RIO) codes. [ABSTRACT FROM AUTHOR]

The energy transition is aimed to take advantage of the operational flexibility of hydropower to extend the integration of intermittent renewable sources. Consequently, the hydrogenerators will have to operate in regimes far away from their designed best-point operation. In order to accurately assess the impact, this paper presents a useful approach to determine the overall operating efficiency of synchronous generators under intermittent operation. An accumulated average efficiency (AAE) model is proposed and compared against an alternative approach; the weighted average efficiency (WAE) model. It is found that the WAE approach produces unrealistic low efficiencies when the generator operates in synchronous condenser mode (SCM) for long periods. In general, the AAE supersedes the WAE for all the different load distributions that were investigated. This was further illustrated by a worked example and by constructing more complex load distributions. A load distribution dominated by SCM yields a difference as high as $33.18 \,\%$ , while an even distribution deviates $1.43 \,\%$ in their respective efficiencies. Finally, a yearly on-site measurement of our studied $103 \,\mathrm{MVA}$ generator’s concentrated load distribution revealed a discrepancy of $0.67 \,\%$ , which is a significant deviation considering what the operating regime would mean in terms of economic implications. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, information has become an increasingly important asset for the operation and planning of power systems. However, the existing studies and practices pay little attention to information value evaluation, i.e., the potential for information to be converted into actual economic benefits. To this end, this paper designs an information market framework and proposes a generalized information valuation model to help price data in smart grids efficiently. Here we analyze the information value of photovoltaic (PV)-related data in a power system operation problem. Specifically, we examine how additional meteorological and PV power data help to improve day-ahead forecasting accuracy, thus enhancing unit commitment (UC). In this paper, information quality is captured by two indices of a set of PV-related data, i.e., Shannon entropy and non-noise ratio. Then a neural network-based engine is employed to predict day-ahead hourly solar power on the premise of input datasets with different information quality. Here we define forecasting accuracy as information utility, and discover an exponential relationship between such utility and information quality. Finally, a two-stage stochastic UC model is formulated to quantify the contributions of different PV-related datasets, in which real-time solar power deviation is penalized. In this instance, the economic value of PV-related data is measured as the operational cost reduction induced by forecasting accuracy improvement, which we find can be estimated by information quality. Case studies based on the IEEE 30- and 118-bus systems validate the effectiveness of the proposed paradigm and method. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. Results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration. [ABSTRACT FROM AUTHOR]

This paper proposes a novel inertia-enhanced distributed control method to complement the inertia of microgrids. The rate of change of frequency (RoCoF) and the rate of change of voltage (RoCoV) are employed in this paper to quantify the frequency inertia and voltage inertia, respectively. Then, a fully distributed algorithm with constrained changing rates is proposed. By bounding the changing rates of frequency and voltage during the consensus control, the algorithm can address the consensus problem while enhancing the inertia of microgrids. Compared with most inertia control methods, the proposed method can utilize the reserve power of scattered DGs to supply inertia. Besides, it performs better under disturbances and delays than conventional distributed control methods. The effectiveness of the proposed method is validated by several cases in MATLAB/Simulation and a hardware experiment. [ABSTRACT FROM AUTHOR]

This paper presents an elegant method for determining the simplest model of a power system electrical/mechanical load that will suffice for dynamic frequency power system studies and closed-loop simulation work. The strategy behind this technique is to supply the simplest load model possible that gives sufficiently accurate results for the goals of each unique modeling effort. This paper identifies the frequency characteristics of several different load types. It also identifies the level of load model detail required for testing typical power management systems, contingency-based load-shedding systems, frequency-based load-shedding systems, governor control systems, island/grid/unit autosynchronization systems, and exciter control systems. This paper describes how to lump loads without loss of fidelity, when an induction motor needs to be modeled as a single-cage or double-cage motor model, what sort of mechanical load model is appropriate, when we can assume zero inertia for a direct-on-line type of load, and how to verify the turbine/generator inertia, and load inertia from field tests. This paper concludes with a simple reference that engineers can use to specify the level of detail required when modeling industrial power system loads. [ABSTRACT FROM PUBLISHER]

This paper presents the theoretical keystone for a decentralization of model-based diagnosis by proving the equivalence between decentralized and centralized residual generation. The proof is based on structural analysis and graph-theoretical concepts. The second contribution of this paper is the design of a decentralized fault-focused residual generation scheme advantageously implementing a strategy of fault isolation on request. Algorithms are tested on the attitude determination and control system of a low Earth orbit satellite. [ABSTRACT FROM PUBLISHER]

Intelligent false data injection on load measurements can trigger false relay operation (FRO) of frequency-based protection relays, affecting the power system frequency and thus threatening the security of power systems. In this paper, we propose an optimization-based formal model to find the optimal false data injection attack (OFDIA) with the minimum required time leading to an FRO. The proposed model considers the dynamic behavior of the power system in an optimization framework to find the optimal size of attacks over multiple generators’ dispatching cycles to minimize the attack launch time. Using the proposed formal modeling, we study the impact of power system parameters, including inertia, governor’s droop and time constant, and the attacker’s accessibility to loads on the attack success and launch time. The results demonstrate that systems with low inertia are more vulnerable to FDIAs while systems with higher inertia are more secure as fewer generator protection relays are impacted by FRO. In addition, we show that securing more load meters can increase the time for launching an attack in the system. Moreover, our studies show that a combination of large values of the governor’s time constants and small values of the governor’s droops can raise the time of successful attacks, making the system more secure against FDIAs. [ABSTRACT FROM AUTHOR]

It has become a great challenge to mitigate the unstable subsynchronous control interaction (SSCI) in practical series compensated wind power systems, mainly because of the system-wide influential factors, such as the constantly changing wind speed, number of in-service wind turbines, and operating conditions. In an effort to address the challenge, this paper proposes a simple yet practical grid-side subsynchronous damping controller (GSDC), which consists of a subsynchronous damping controller (SDC) supplemented to a subsynchronous current generator (SCG). The GSDC utilizes bus voltages and line currents as feedback signals and injects currents at the subsynchronous frequency into the system to provide active damping. The parameters of the SDC and SCG can be optimized for any system facing SSCI issues. In this paper, a GSDC is developed and optimized over an EMT model of a realistic system. The proposed scheme has demonstrated its effectiveness and robustness through controller-hardware-in-the-loop tests for mitigating SSCI over a wide range of operating conditions. This paper has paved the way for the commissioning of a practical GSDC in a real-world system that has experienced unstable SSCI incidents. [ABSTRACT FROM AUTHOR]

Tidal power is an emerging technology with great potential to provide a sustainable means of renewable energy in many areas worldwide. However, the nature of the underwater environment provides challenges. Submerged machinery cannot be easily accessed for inspections, and turbines must be brought to the surface for maintenance. This is an expensive process and results in prolonged periods of downtime where no power can be supplied to the grid. Condition monitoring systems, capable of accurately and remotely assessing the health state of machinery while in operation, can therefore be of great value to this industry. This paper presents an approach for condition monitoring of a tidal turbine’s gearbox from monitoring data with low sample rates. Models of normal behavior were trained using weighted least squares regression, where prediction errors are used to identify changes in response. This paper then examines how prediction errors from a number of different cases (including changes in control scheme and simulated gearbox faults) can be interpreted by operators to classify anomalous behavior. [ABSTRACT FROM AUTHOR]

This paper provides a pragmatic solution to the challenge of testing fault current interruption of high-voltage direct current (HVDC) circuit breakers (CBs). The critical parameters in the design of a test circuit capable of supplying the necessary stresses: current, energy, and voltage (both during and after interruption) are discussed. In addition, a practical implementation of a test circuit based on ac short-circuit generators operated at low power frequency, which is capable of testing the current interruption performance of the proposed technologies of HVDC CBs, is discussed. Tests validating the proposed method and circuit have been conducted on a prototype of an HVDC CB and the test results are presented. Since the performance of some technologies of HVDC CBs can depend on the magnitude of the interrupted current, four test duties are defined and demonstrated in the paper. Moreover, testing of HVDC CBs using ac short-circuit generators poses new challenges, such as the application of dielectric dc stress after current interruption and the protection of both the test-object as well as the test-circuit components when the HVDC CB fails to interrupt. Methods to overcome these challenges are developed and practically demonstrated in a test laboratory. Finally, taking into account the available resources of the author's test laboratory, the capability to test multiple series-connected modules of different technologies of HVDC CBs is verified and example cases are demonstrated. Six short-circuit generators (13 500 MVA @ 50 Hz) and up to ten step-up transformers (up to 550 kV) were actually used. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem for which lengths a maximum distance separable (MDS) Euclidean self-dual code over $\mathbb {F}_{q}$ exists. This problem is completely solved for the case where $q$ is even. For $q$ is odd, some $q$ -ary MDS Euclidean self-dual codes were obtained in the literature. In this paper, we construct six new classes of $q$ -ary MDS Euclidean self-dual codes by using generalized Reed–Solomon (GRS for short) codes and extended GRS codes. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed sliding mode (SM) control strategy for optimal load frequency control (OLFC) in power networks, where besides frequency regulation, minimization of generation costs is also achieved (economic dispatch). We study a nonlinear power network of interconnected (equivalent) generators, including voltage and second-order turbine-governor dynamics. The turbine-governor dynamics suggest the design of a sliding manifold such that the turbine-governor system enjoys a suitable passivity property, once the sliding manifold is attained. This paper offers a new perspective on OLFC by means of SM control, and in comparison with the existing literature, we relax required dissipation conditions on the generation side and assumptions on the system parameters. [ABSTRACT FROM AUTHOR]

This paper is concerned with distributed energy management and control issues of both generators and loads. It aims to maximize the total social welfare that balances generation-side expanses, user-side payments, and transmission line costs. A distributed control strategy with continuous information exchange among neighbors is first proposed. It is shown that this distributed algorithm achieves the global optimal power outputs on generators and the optimal electricity usage on loads asymptotically. To reduce communication resource consumptions, the distributed optimization algorithm is further expanded to incorporate event-triggered communication and control mechanism. In this new algorithm, an event-triggering condition for each generator and each load is employed to determine when its related state information should be sampled and transmitted to its neighbors. Compared with the standard periodic sampling and communication schemes, this new distributed and event-triggered algorithm can significantly reduce communication data flows while achieving the nearly identical control performance to that under continuous data communications. The theoretical results of this paper are validated by using a simulation case study with distributed generators and multiple loads on an IEEE 9-bus system. [ABSTRACT FROM AUTHOR]

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators. [ABSTRACT FROM AUTHOR]

With the expansion of system scale and data size in power systems, data-driven methods are gradually becoming widely used. However, compared with traditional methods, data-driven methods face more threats in data security and algorithm security. This paper proposes a black box data injection attack method against data-driven strategies in power systems. To obtain a stealthy attack scheme with the greatest impact, an attack vector generator based on deep convolutional neural networks (DCNN) is designed. The generator includes a node selector, a filter, an encoder, and a decoder. The node selector is used to select the most likely successful attack scheme for the filter, the encoder is used to extract sample features, and the decoder is used to generate disturbances based on the features. The proposed generator is trained with an improved generative adversarial network (GAN) and can generate minimal disturbances in real time based on measurement data from the power grid. The effectiveness of the proposed method is demonstrated with an attack experiment on an online transient stability application. [ABSTRACT FROM AUTHOR]

Over the last decade, deep neural networks (DNNs) are regarded as black-box methods, and their decisions are criticized for the lack of explainability. Existing attempts based on local explanations offer each input a visual saliency map, where the supporting features that contribute to the decision are emphasized with high relevance scores. In this paper, we improve the saliency map based on differentiated explanations, of which the saliency map not only distinguishes the supporting features from backgrounds but also shows the different degrees of importance of the various parts within the supporting features. To do this, we propose to learn a differentiated relevance estimator called DRE, where a carefully-designed distribution controller is introduced to guide the relevance scores towards right-skewed distributions. DRE can be directly optimized under pure classification losses, enabling higher faithfulness of explanations and avoiding non-trivial hyper-parameter tuning. The experimental results on three real-world datasets demonstrate that our differentiated explanations significantly improve the faithfulness with high explainability. Our code and trained models are available at https://github.com/fuweijie/DRE. [ABSTRACT FROM AUTHOR]

The linear induction launcher consists of two parts: the barrel section and the projectile section. The barrel contains of a long coaxial sectionalized group of coils that are energized by a capacitor bank or generator sets. This paper deals with the design, implementation, and experimental test of a coreless axial-flux permanent magnet (AFPM) pulsed alternator with a flywheel system as an energy source for linear induction coil launchers. The proposed coreless AFPM generator has a low internal inductance and resistance of stator windings without compensation windings when compared with the conventional iron core machines. This paper describes the coreless AFPM generator configuration and shows the simulation findings. [ABSTRACT FROM AUTHOR]