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This paper proposes two second-order continuous-time algorithms to solve the economic power dispatch problem in smart grids. The collective aim is to minimize a sum of generation cost function subject to the power demand and individual generator constraints. First, in the framework of nonsmooth analysis and algebraic graph theory, one distributed second-order algorithm is developed and guaranteed to find an optimal solution. As a result, the power demand constraints can be kept all the time under appropriate initial condition. The second algorithm is under a centralized framework, and the optimal solution is robust in the sense that different initial power conditions do not change the convergence of the optimal solution. Finally, simulation results based on five-unit system, IEEE 30-bus system, and IEEE 300-bus system show the effectiveness and performance of the proposed continuous-time algorithms. The examples also show that the convergence rate of second-order algorithm is faster than that of first-order distributed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we devise a fast encoding algorithm for triply extended Reed–Solomon codes. The proposed approach requires approximately two XORs per bit, which improves the prior result of three XORs per bit established by certain maximum distance separable (MDS) array codes. We also prove that, for MDS codes with two and three parities, the scheduling algorithms require at least two XORs per bit. To the best of our knowledge, this is the first provable scheduling algorithm for the triple-parity MDS codes to approach the theoretical lower bounds. The implementation with SIMD instructions is provided. The simulations show that the proposed approach is competitive, as compared with other cutting edge implementations. [ABSTRACT FROM AUTHOR]

This paper applies a new technique for modal decomposition based solely on measurements to test systems and demonstrates the technique's capability for partitioning a power network, which determines the points of separation in an islanding strategy. The mathematical technique is called the Koopman mode analysis (KMA) and stems from a spectral analysis of the so-called Koopman operator. Here, KMA is numerically approximated by applying an Arnoldi-like algorithm recently first applied to power system dynamics. In this paper we propose a practical data-driven algorithm incorporating KMA for network partitioning. Comparisons are made with two techniques previously applied for the network partitioning: spectral graph theory which is based on the eigenstructure of the graph Laplacian, and slow-coherency which identifies coherent groups of generators for a specified number of low-frequency modes. The partitioning results share common features with results obtained with graph theory and slow-coherency-based techniques. The suggested partitioning method is evaluated with two test systems, and similarities between Koopman modes and Laplacian eigenvectors are showed numerically and elaborated theoretically. [ABSTRACT FROM PUBLISHER]

Distributed generators are integrated into microgrids or geographically distributed subsystems in active distribution networks and may belong to various entities. Centralized solutions cannot meet future requirements such as high resilience and privacy protection. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms exhibit linear or sublinear convergence. This paper introduces an efficient fully distributed dynamic ED solution for minimizing photovoltaic power curtailment, based on a parallel primal-dual interior-point algorithm with a quasi-Newton technique. In this method, each area optimizes its own problem with limited information exchanged with its neighbors, and no central coordinator is needed. Based on a peer-to-peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. Numerical tests demonstrated that the algorithm outperforms gradient-based methods such as ADMM and maintains stability under a partial communication failure. [ABSTRACT FROM AUTHOR]

Accurate event analysis in real time is of paramount importance for high-fidelity situational awareness such that proper actions can take place before any isolated faults escalate to cascading blackouts. Existing approaches are limited to detect only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale systems, the performance tends to degrade dramatically in large-scale systems. In this paper, we focus on multiple event detection, recognition, and temporal localization in large-scale power systems. We discover that there always exist “regions” where the reaction of all buses to certain event within each region demonstrates high degree similarity, and that the boundary of the “regions” generally remains the same regardless of the type of event(s). We further verify that, within each region, this reaction to multiple events can be approximated as a linear combination of reactions to each constituent event. Based on these findings, we propose a novel method, referred to as cluster-based sparse coding (CSC), to extract all the underlying single events involved in a multievent scenario. Multiple events of three typical disturbances (e.g., generator trip, line trip, and load shedding) can be detected and recognized. Specifically, the CSC algorithm can effectively distinguish line trip events from oscillation, which has been a very challenging task for event analysis. Experimental results based on simulated large-scale system model (i.e., NPCC) show that the proposed CSC algorithm presents high detection and recognition rate with low false alarms. [ABSTRACT FROM PUBLISHER]

The number and locations of transmission lines that are likely to get simultaneously congested are useful information for analysis of power market. In this paper, we present a study that demonstrates the number of simultaneously congested transmission lines is very limited, as well as an algorithm for identifying a small set of transmission lines that may become simultaneously congested based on the current states of the system and market. To this end, we first investigate the maximum number of simultaneously congested lines. Then, a novel algorithm is developed to identify a smaller set using two scientific criteria – one is concerned with the absolute impact of generation dispatch decisions, and the other, the relative sensitivities of line flows with respect to the changes of generation outputs. The effectiveness of the proposed algorithm is demonstrated through two simulation studies, one based on IEEE 39-bus for illustration of concepts, and the other based on a large-scale, real-world power system for showing the practical application and significance of results. [ABSTRACT FROM AUTHOR]

In this paper, an application of the theoretical algebraic notion of a separable ring extension in the realm of cyclic convolutional codes or, more generally, ideal codes, is investigated. It is worked under very mild conditions that cover all previously known as well as new non-trivial examples. It is proved that ideal codes are direct summands, as left ideals, of the underlying non-commutative algebra, in analogy with cyclic block codes. This implies, in particular, that they are generated by a non-commutative idempotent polynomial. Hence, by using a suitable separability element, an efficient algorithm for computing one of such idempotents is designed. We show that such an idempotent generator polynomial can be used to get information on the free distance of the convolutional code. [ABSTRACT FROM AUTHOR]

Driven by smart grid technologies, distributed energy resources (DERs) have been rapidly developing in recent years for improving reliability and efficiency of distribution systems. Emerging DERs require effective and efficient coordination in order to reap their potential benefits. In this paper, we consider an optimal DER coordination problem over multiple time periods subject to constraints at both system and device levels. Fully distributed algorithms are proposed to dynamically and automatically coordinate distributed generators with multiple/single storages. With the proposed algorithms, the coordination agent at each DER maintains only a set of variables and updates them through information exchange with a few neighbors. We show that the proposed algorithms with properly chosen parameters solve the DER coordination problem as long as the underlying communication network is connected. The simulation results are used to illustrate and validate the proposed method. [ABSTRACT FROM AUTHOR]

Grid code specifications for voltage ride-through (VRT) of power generating units are based on time-domain envelopes of permissible terminal voltage magnitudes. Since an infinite number of voltage trajectories can be inscribed within a given enclosure, verifying that a given apparatus will withstand any such disturbance can be a daunting task. This paper tackles the VRT verification problem for doubly fed induction generator (DFIG)-based wind turbines. To this end, reachability analysis is employed to calculate sets that enclose all possible trajectories of the dynamic states of a DFIG-based system when perturbed by unknown-but-bounded grid voltage disturbances. [ABSTRACT FROM PUBLISHER]

To isolate the negative impacts of pulsed power loads (PPL) in a shipboard power system (SPS), energy storage systems (ESS) are usually equipped for online ESS charging and offline ESS discharging for PPL deployment. In order to achieve fast and smooth ESS charging, the generation control and ESS charging control need to be well coordinated. In addition, control constraints, such as generation and charging current bounds and ramp rates, and changes of operating conditions during charging, should also be properly considered. To better solve this important but insufficiently investigated problem, two control algorithms are presented in this paper. The first PI-based control algorithm is designed based on analysis of the desired performance and rules for coordination. The second feedback linearization based control algorithm is designed based on a simplified SPS model and ramp rates difference of supply and demand. The algorithms are simple to implement and can effectively reduce the negative impacts during supercapacitor charging. The algorithms are tested with detailed single-generator and multiple-generator SPS models. Power hardware-in-the-loop and real-time digital simulation studies are performed to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper considers a linear quasi-cyclic product code of two given quasi-cyclic codes of relatively prime lengths over finite fields. We give the spectral analysis of a quasi-cyclic product code in terms of the spectral analysis of the row and column codes. Moreover, we provide a new lower bound on the minimum Hamming distance of a given quasi-cyclic code and present a new algebraic decoding algorithm. More specifically, we prove an explicit (unreduced) basis of an \ell A {\ell B} -quasi-cyclic product code in terms of the generator matrix in reduced Gröbner basis with respect to the position-over-term (RGB/POT) order form of the \ell A -quasi-cyclic row code and the \ell B -quasi-cyclic column code, respectively. This generalizes the work of Burton and Weldon for the generator polynomial of a cyclic product code (where \ell A= {\ell B}=1 ). Furthermore, we derive the generator matrix in Pre-RGB/POT form of an \ell A {\ell B} -quasi-cyclic product code for two special cases: i) for \ell A=2 and \ell B=1 and ii) if the row code is a one-level \ell A -quasi-cyclic code (for arbitrary \ell A ) and \ell B=1 . For arbitrary \ell A and \ell B , the Pre-RGB/POT form of the generator matrix of an \ell A {\ell B} -quasi-cyclic product code is conjectured. The spectral analysis is applied to the generator matrix of the product of an \ell -quasi-cyclic and a cyclic code, and we propose a new lower bound on the minimum Hamming distance of a given \ell -quasi-cyclic code. In addition, we develop an efficient syndrome-based decoding algorithm for \ell -phased burst errors with guaranteed decoding radius. [ABSTRACT FROM AUTHOR]

Fire codes are cyclic codes generated by the product of two polynomials: a binomial that characterizes the code’s guaranteed burst-correcting capability and an irreducible polynomial that characterizes the code length. However, the true burst-correcting capability of a Fire code may exceed its guaranteed burst-correcting capability, which can be thought of as the designed burst-correcting capability of the Fire code. The true burst-correcting capability of a Fire code depends on the irreducible polynomial used in code construction. In this paper, the maximum true burst-correcting capabilities of Fire codes are considered. Fire codes are classified based on three parameters: their designed burst-correcting capabilities, the least common multiple of the periods of the binomials, and the irreducible polynomials used in their constructions, and the ratios of the periods of primitive polynomials of the same degrees as the irreducible polynomials to the periods of the irreducible polynomials. It is shown that the maximum true burst-correcting capability of each class, which pertains to an infinite number of codes, can be determined by checking whether or not a finite number of incongruences have a solution. It is also shown that in each class, there is an infinite number of Fire codes, with increasing code lengths, that attain this maximum. The maximum true burst-correcting capabilities of several classes of Fire codes are determined. It is also shown that there are infinite sequences of irreducible polynomials that generate cyclic codes of rates approaching one and with burst-correcting capabilities that exceed any given number. [ABSTRACT FROM AUTHOR]

This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new way of providing cyclic structures to convolutional codes. We define the skew cyclic convolutional codes as left ideals of a quotient ring of a suitable non-commutative polynomial ring. In contrast to the previous approaches to cyclicity for convolutional codes, we use Ore polynomials with coefficients in a field (the rational function field over a finite field), so their arithmetic is very well known and we may proceed similarly to cyclic block codes. In particular, we show how to obtain easily skew cyclic convolutional codes of a given dimension, and we compute an idempotent generator of the code and its dual. [ABSTRACT FROM AUTHOR]

The fast growing of data scale encourages the wide employment of data disks with large storage capacity. However, a mass of data disks’ equipment will in turn increase the probability of data loss or damage, because of the appearance of various kinds of disk failures. To ensure the intactness of the hosted data, modern storage systems usually adopt erasure codes, which can recover the lost data by pre-storing a small amount of redundant information. As the most common case among all the recovery mechanisms, the single disk failure recovery has been receiving intensive attentions for the past few years. However, most of existing works still take the stripe-level recovery as their only consideration, and a considerable performance improvement on single failure disk reconstruction in the stack-level (i.e., a group of rotated stripes) is missed.   To seize this potential improvement, in this paper we systematically study the problem of single failure recovery in the stack-level. We first propose two recovery mechanism based on greedy algorithm to seek for the near-optimal solution (BP-Scheme and STP-Scheme) for any erasure array code in stack level, and further design a rotated recovery algorithm (RR-Algorithm) to eliminate the size of required memory. Through a rigorous statistic analysis and intensive evaluation on a real system, the results show that BP-Scheme gains 3.4 to 38.9 percent (the average is 21.2 percent) higher recovery speed than Khan's Scheme and 3.4 to 34.8 percent (the average is 19.1 percent) higher recovery speed than Luo's U-Scheme, while STP-Scheme owns 3.4 to 46.9 percent (the average is 25.15 percent) and 3.4 to 41.1 percent (the average is 22.3 percent) higher recovery speed than Khan's Scheme and Luo's U-Scheme, respectively. [ABSTRACT FROM PUBLISHER]

Although there are vast potential locations to install FACTS devices in a power system, the actual installation number is very limited due to economical consideration. Therefore the allocation strategy exhibits strong sparsity. This paper formulates FACTS device allocation problem as a general sparsity-constrained OPF problem and employs Lq(0< q\leq 1) norms to enforce sparsity on FACTS devices setting values to achieve solutions with desirable device numbers and sites. An algorithm based on alternating direction method of multipliers is proposed to solve the sparsity-constrained OPF problem. The algorithm exploits the separability structure and decomposes the original problem into an NLP subproblem, an Lq regularization subproblem, and a simple dual variable update step. The NLP subproblem is solved by the interior point method. The Lq regularization subproblem has a closed-form solution expressed by shrinkage-threholding operators. The convergence of the proposed method is theoretically analyzed and discussed. The proposed method is successfully tested on allocation of SVC, TCSC, and TCPS on IEEE 30-, 118-, and 300-bus systems. Case studies are presented and discussed for both single-type and multiple-type FACTS devices allocation problems, which demonstrates the effectiveness and efficiency of the proposed formulation and algorithm. [ABSTRACT FROM PUBLISHER]

Today, designers of network processors strive to keep the packet reception and transmission orders identical, and therefore avoid any possible out-of-order transmission. However, the development of new features in advanced network processors has resulted in increasingly parallel architectures and increasingly heterogeneous packet processing times, leading to large reordering delays. In this paper, we introduce novel scalable scheduling algorithms for preserving flow order in parallel multi-core network processors. We show how these algorithms can reduce reordering delay while adapting to any load-balancing algorithm and keeping a low implementation complexity overhead. To do so, we use the observation that all packets in a given flow have similar processing requirements and can be described with a constant number of logical processing phases. We further define three possible knowledge frameworks of the time when a network processor learns about these logical phases, and deduce appropriate algorithms for each of these frameworks. Finally, we model our proposed algorithms and simulate them under both synthetic traffic and real-life traces, and show that they significantly outperform past approaches. [ABSTRACT FROM PUBLISHER]

Gaussian networks are degree four symmetric networks and these are designed based on the concept of Gaussian integers. The Gaussian network can be described in terms of a generator $\alpha =a+bi$ , where a$ and b$ are integers and . When $\gcd (a,b)=1$ , how to find edge disjoint Hamiltonian cycles has been shown before. In this paper for any generator $\alpha =a+bi,$ even when $\gcd (a, b)=d > 1$ , how to obtain two edge disjoint Hamiltonian cycles in these networks is described. [ABSTRACT FROM AUTHOR]

Accurate performance characterization is critical in a design optimization environment. The electromagnetic (EM) design optimization of an ocean wave linear generator buoy or wave activated device (WAD) requires the use of large number of finite element-state space, design evaluations due to the stochastic behavior of the wave and the nonlinear nature of the device. This paper proposes the utilization of Taguchi orthogonal arrays method to reduce the EM, computational time needed to accurately characterize a WAD performance corresponding to a sea wave profile and for use in a design optimization environment. [ABSTRACT FROM PUBLISHER]

In traditional power system, economic dispatch and generation control are separately applied. Online generation adjustment is necessary to regulate generation reference for real-time control to realize economic operation of power systems. Since most economic dispatch solutions are centralized, they are usually expensive to implement, susceptible to single-point-failures, and inflexible. To address the above-mentioned problems, this paper proposed a multi-agent system based distributed control solution that can realize optimal generation control. The solution is designed based upon an improved distributed gradient algorithm, which can address both equality and inequality constraints. To improve the reliability of multi-agent system, the N-1 rule is introduced to design the communication network topology. Compared with centralized solutions, the distributed control solution not only can achieve comparable solutions but also can respond timely when the system experiences change of operating conditions. MAS based real-time simulation results demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM PUBLISHER]

In this paper, regarding the observer form of the well-known Youla parameterization, the controller design and optimization are exhibited with an integrated residual access. To better reveal this philosophy, the feedback control loop is interpreted on the basis of the observer-based residual generator. The next main attention is drawn to the generation of residuals, the design of a deadbeat controller for system stabilization both in the data-driven environment, and later the optimal adaptive realization of a dynamic system that translates residuals into compensatory control inputs to meet certain performance specifications. Towards these goals, numerical algorithms are summarized, and for the issues of controller optimization, the reinforcement learning algorithm is introduced using only measured input-output and residual signals. In addition, the effectiveness of developed schemes for industrial applications is also illustrated by experimental studies on a laboratory continuous stirred tank heater (CSTH) process. [ABSTRACT FROM PUBLISHER]