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This study was carried out to determine inhibitory effect of crude extracts and essential oils of aromatic plants against Sclerotinia sclerotiorum in potato fields in Bahar and Lalehjin, Iran during cropping seasons of 2015 and 2016. The results showed that crude extracts of cinnamon and ginger at concentrations 5 g/l as well as essential oils of coriander, lavender thyme, savory and eucalyptus at concentration 50 μl EO/ml had potential to significantly reduce incidence of potato white mold. This is the first study about inhibitory effect of crude extracts and essential oils of aromatic plants against potato white mold under field condition. [ABSTRACT FROM AUTHOR]

A distributed control method is proposed to simultaneously optimize the power sharing among sources of islanded dc microgrids, while regulating the distribution bus voltage. During the optimization process, an economic dispatch problem is solved to minimize the total generation cost by setting the output powers of the dispatchable sources. To this end, the voltage set points of individual dc–dc converters are adjusted using a voltage regulator and an optimizer, which regulates the average voltage of the sources to establish the generation–consumption equality constraint and matches the incremental costs, respectively. Afterward, the proposed optimizer is modified to exclude the sources from the incremental cost consensus protocol upon reaching their generation limits, enforcing inequality constraints. This coregulation and cooptimization paradigm is developed in a fully distributed fashion. The dynamical model of the proposed controller is established. The steady-state analysis verifies the fulfillment of the control objectives, i.e., voltage regulation and cost minimization. Experimental results verify the controller performance and validate its resiliency against cyber and physical faults. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed finite-time restoration of inverter voltage and frequency terms in an islanded ac microgrid. Formulating networked inverters of ac microgrids as a cooperative multiagent system, the voltage and frequency restoration can be cast as synchronization problems, while the active power sharing can be viewed as a consensus problem. One popular distributed control approach is the neighbor-based linear consensus protocol, where the consensus at the frequency and voltage set points is achieved over an infinite-time horizon with an exponential convergence. To achieve accelerated convergence and better disturbance rejection properties, a distributed finite-time control protocol, based on feedback linearization approach, is proposed for voltage restoration, which synchronizes the voltage term at each inverter to the reference value in finite time period. Then, a finite-time control protocol for both frequency restoration and active power sharing problems is proposed to synchronize the microgrid frequency to the nominal value, and share the active power among inverters based on their ratings in a finite time. Rigorous Lyapunov proofs are provided to establish the upper bounds on the convergence times. Numerical simulation studies verify the effectiveness of the proposed control protocols. [ABSTRACT FROM PUBLISHER]

A distributed control method is proposed to handle power sharing among a cluster of dc microgrids. The hierarchical control structure of microgrids includes primary, secondary, and tertiary levels. While the load sharing among the sources within a dc microgrid is managed through primary and secondary controllers, a tertiary control level is required to provide the higher level load sharing among microgrids within a cluster. Power transfer between microgrids enables maximum utilization of renewable sources and suppresses stress and aging of the components, which improves its reliability and availability, reduces the maintenance costs, and expands the overall lifespan of the network. The proposed control mechanism uses a cooperative approach to adjust voltage set points for individual microgrids and, accordingly, navigate the power flow among them. Loading mismatch among neighbor microgrids is used in an updating policy to adjust voltage set point and mitigate such mismatches. While the voltage adjustment policy handles the load sharing among the microgrids within each cluster, at a lower level, each microgrid carries a communication network that is in contact with the secondary control system. It is this lower level network that propagates voltage set points across all sources within a microgrid. Load sharing and set point propagation are analytically studied for the higher and lower level controllers, respectively. Experimental studies on two cluster setups demonstrate excellent controller performance and validate its resiliency against converter failures and communication losses. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editor discusses various reports within the special section " Advanced Distributed Control of Energy Conversion Devices and Systems" of the issue on topics including energy storage systems, photovoltaics and renewable energy sources.

This paper proposes an adaptive and distributed secondary voltage control for microgrids with inverter-based distributed generators (DG). The proposed control is fully adaptive and does not require the information of DG parameters. Neural networks are used to compensate for the uncertainties caused by the unknown dynamics of DGs. The controller structure is fully distributed such that each DG only requires its own information and the information of its neighbors on the communication network. Therefore, this secondary control is associated with a sparse communication network. The effectiveness of the proposed methodology is verified for different loading, outage, and islanding scenarios, as well as variable communication structures in a microgrid setup. [ABSTRACT FROM AUTHOR]

A distributed-adaptive droop mechanism is proposed for secondary/primary control of dc microgrids. The conventional secondary control that adjusts the voltage set point for the local droop mechanism is replaced by a voltage regulator. A current regulator is also added to fine-tune the droop coefficient for different loading conditions. The voltage regulator uses an observer that processes neighbors’ data to estimate the average voltage across the microgrid. This estimation is further used to generate a voltage correction term to adjust the local voltage set point. The current regulator compares the local per-unit current of each converter with the neighbors’ on a communication graph and, accordingly, provides an impedance correction term. This term is then used to update the droop coefficient and synchronize per-unit currents or, equivalently, provide proportional load sharing. The proposed controller precisely accounts for the transmission/distribution line impedances. The controller on each converter exchanges data with only its neighbor converters on a sparse communication graph spanned across the microgrid. Global dynamic model of the microgrid is derived with the proposed controller engaged. A low-voltage dc microgrid prototype is used to verify the controller performance, link-failure resiliency, and the plug-and-play capability. [ABSTRACT FROM AUTHOR]

Modular dc-dc converters are popular in dc-power systems due to their advantageous characteristics such as fault tolerance, ease of thermal management, reducing voltage/current stress of the components, and modularity. In this paper, the concept of cooperative control in multiagent systems is introduced for modular dc-dc converters. Each constituent converter is represented by a node in a directed communication graph that models the information flow among converters. The proposed cooperative control scheme enjoys structural modularity, plug-and-play capability, fault tolerance against random failures in the converters and/or communication links, and satisfactory dynamic performance. This paper provides a general analytical framework to study modular dc-dc converters with an arbitrary communication graph. Hence, the designer has the freedom to choose among the various types of graphs based on the available communication resources and the desired level of reliability and fault tolerance. The dynamic model of the cooperative multiconverter system is developed and analyzed. Hardware measurements are presented to verify the plug-and-play capability, fault tolerance in both cyber and physical domains, and dynamic performance of the proposed cooperative control scheme. [ABSTRACT FROM AUTHOR]

Transport and deposition of nanoparticles (NPs) have drawn great attention in different fields of engineering in recent years because of their potential risk to the environment. In this study, mobility of aluminum oxide NP, one of the most popular metal oxide NPs, was studied in synthetic saturated porous media. The impacts of ionic strength using monovalent (NaCl) and divalent (MgCl2) salt solution, pH, and NPs concentrations on nano-alumina mobility were investigated. The experimental results showed that the transport of nano-alumina was influenced by ionic strength; the highest mobility was observed at the 0.01 M solution and decreased by increasing ionic strength. Moreover, the suspension containing alumina NPs presented the best mobility behavior in the acidic solution (pH = 2) in comparison with neutral and basic solutions. Consequently, transport of NPs with particle size distribution lower than 100 nm through porous media was facilitated at the acidic, low ionic strength condition. In addition, faster elution occurred when the influent concentration was increased from 0.1 wt% to 0.3 wt%. Finally, it is expected that ionic strength, pH of solution, and NPs concentration will be key parameters to control the nano-alumina mobility. [ABSTRACT FROM AUTHOR]

A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems. [ABSTRACT FROM PUBLISHER]

A single-stage multiple-input multiple-output dc–dc converter topology is proposed. The proposed converter can employ arbitrary number of energy sources and can supply arbitrary number of loads. The ability of the converter to regulate the input powers coming from different energy sources, in addition to regulating the output voltages, stands out. A specific switching scheme is proposed, and the dynamic model of the converter is developed based on the designated switching scheme. An appropriate control algorithm is presented, based on which the input powers and output voltages are regulated. Several hardware measurements and numerical simulations are presented to verify dynamic characterizations and to demonstrate the converter's performance. [ABSTRACT FROM PUBLISHER]

Parallel and standby configurations can be applied to semiconductor switches to improve the reliability of power electronic converters in mission-critical applications. In this paper, the reliability models of both configurations are developed based on the Markov process. The mean time to failure (MTTF) of each configuration is derived in terms of the underlying parameters. It is demonstrated that there is a boundary condition in which both configurations have the same MTTF. This boundary condition is expressed in terms of the junction temperature of the semiconductor switch in the steady state. The temperature range in which the parallel configuration is more reliable is formulated for different types of power semiconductor switches including MOSFETs, bipolar junction transistors, SCRs, triacs, regular diodes, and Schottky diodes. Case studies are presented to determine the more reliable configuration for a laboratory-scale buck converter. [ABSTRACT FROM PUBLISHER]

Switched reluctance machines (SRMs) suffer from ultra-low-frequency torque ripple in single pulse mode. This ripple is the result of an imbalance in phase currents, when multiphase excitation occurs. This paper investigates the origin of this imbalance and identifies the imbalanced phase. Excitation shifting strategy, which delays excitation of the imbalanced phase, is then proposed. The delay angle adjusts current amplitude in the imbalanced phase to attain a perfect balance between all phases. The excitation shifting strategy is a general solution, which is applicable to both three-phase and four-phase machines. In addition, the proposed method can be implemented by a much simpler drive structure, when compared to the existing balancing methods. Hardware measurements are provided to verify superior performance of the proposed current balancing technique. [ABSTRACT FROM PUBLISHER]

This study proposes a secondary voltage and frequency control scheme based on the distributed cooperative control of multi‐agent systems. The proposed secondary control is implemented through a communication network with one‐way communication links. The required communication network is modelled by a directed graph (digraph). The proposed secondary control is fully distributed such that each distributed generator only requires its own information and the information of its neighbours on the communication digraph. Thus, the requirements for a central controller and complex communication network are obviated, and the system reliability is improved. The simulation results verify the effectiveness of the proposed secondary control for a microgrid test system. [ABSTRACT FROM AUTHOR]

This paper proposes a secondary voltage control of microgrids based on the distributed cooperative control of multi-agent systems. The proposed secondary control is fully distributed; each distributed generator only requires its own information and the information of some neighbors. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability. Input–output feedback linearization is used to convert the secondary voltage control to a linear second-order tracker synchronization problem. The control parameters can be tuned to obtain a desired response speed. The effectiveness of the proposed control methodology is verified by the simulation of a microgrid test system. [ABSTRACT FROM PUBLISHER]

A multiple-input multiple-output dc–dc converter topology is proposed. This converter is able to accommodate an arbitrary number of sources and loads. The steady-state and dynamic characteristics of the proposed converter are analyzed. A controller scheme is proposed that enables budgeting the input powers coming from different energy sources, in addition to regulating the output voltages. Loss and efficiency modeling and sensitivity analysis to the underlying parameters are performed. Several case studies are presented to verify the analytical models and evaluate the performance of the proposed converter. [ABSTRACT FROM PUBLISHER]

A systematic, automatable, unified methodology is presented for the modeling of non-linear large-signal dynamics and eigenvalue analysis of microgrid systems. Unlike nodal-based simulators and typical state-variable methods that require Kirchhoff's current law analysis, a state-space model is systematically derived from the netlist of the equivalent qd microgrid circuit model. The model may be used to conduct time-domain simulations and analyze system response to large transients. Additionally, system eigenvalues may be analyzed with respect to inverter control gains and system parameters to assess small-signal stability and sensitivity. The dynamic model is verified against existing experimental results, and small-signal stability results are presented. [ABSTRACT FROM AUTHOR]

Magnetic components, such as inductors and transformers, enable short-term energy storage and transfer and are essential for many power electronic converters. Physics-based models of magnetic systems, such as finite element-based models and/or high-fidelity magnetic equivalent circuit (HFMEC) models, accurately represent the magnetic device. However, these models are computationally intensive and hard to formulate. In this paper, an HFMEC approach for laminated and solid magnetic cores is set forth that avoids conventional geometrical simplifications and assumptions of uniform flux density. The proposed dynamic HFMEC model accurately captures the effects of magnetic saturation, high frequency eddy currents, corner effects, and 3-D effects. The resulting full-order HFMEC models introduce a large set of state variables. Automated linear and nonlinear order-reduction techniques are introduced to extract the desired essential system dynamics, thus preserving both model accuracy and computational efficiency. The resulting reduced-order models are validated with hardware measurements and the original full-order HFMECs in both time and frequency domains. [ABSTRACT FROM AUTHOR]

Analytical average-value modeling of pulsewidth modulation dc-dc converters is usually based on the piece-wise linear waveforms of the circuit variables and neglects the parasitics that give rise to waveform nonlinearities and complicate model derivation. This letter presents a straightforward but powerful methodology that considers the true averaging of nonlinear waveforms and produces a fairly accurate large-signal transient model that is functional in both operational modes. Based on the proposed modeling approach, a fast procedure for extracting the small-signal frequency-domain characteristics is set forth. The key model parameters are established numerically using the detailed simulation, which streamlines the analysis. The resulting average-value model is validated with a hardware prototype, a detailed simulation, and several previously established models in time and frequency domains. [ABSTRACT FROM AUTHOR]

State-space average-value modeling of pulsewidth modulation converters in continuous and discontinuous modes has received significant attention in the literature, and various models have been developed. This paper presents a new approach for generating the state-space average-value model. In the proposed methodology, the so-called duty-ratio constraint and the correction term are extracted numerically using the detailed simulation and are expressed as nonlinear functions of the duty cycle and average-value of the fast state variable. The parasitic effects of circuit elements are readily included. The resulting average-value model is compared to a hardware prototype, a detailed simulation, and several previously published models. The proposed model is shown to be very accurate in predicting the large-signal time-domain transients as well as the small-signal frequency-domain characteristics. [ABSTRACT FROM AUTHOR]

The article introduces various reports published within the issue, including one about fuel cells, one about energy management and another on hybrid electric vehicles.

Acute myopericarditis is a well-recognized but rare complication of numerous viral infections. Here we report a case of fulminant myopericarditis presenting with acute heart failure and a state of shock in a previously healthy young woman. H1N1 influenza A virus sequences were identified in throat and pericardial fluid, suggesting a viral source of the infection. [ABSTRACT FROM AUTHOR]

In this paper, we propose a topology-cognizant optimal power flow (OPF) paradigm with additional safety constraints for multi-terminal direct current (MTDC) grids. The resulting formulation is concerned with the optimization of controller set-points, i.e., voltage and power levels at each bus, and the switching status of transmission lines that collectively referred to as grid topology. A pair of additional safety constraints are integrated into the problem formulation to prevent voltage violations caused by power fluctuations in between two controller set-point updates. Searching for a grid topology that offers more efficient operation leads to a mixed-integer nonlinear program (MINLP) which is computationally challenging due to: i) Non-convex power flow equations, (ii) Non-convex converter loss equations, and iii) Binary variables accounting for the operational status of transmission lines. Non-convexities of power flows and converter loss equations are tackled by means of a mixed-integer second-order cone programming (MISOCP) relaxation, while the optimal switching status of transmission lines are determined via a branch-and-bound search. Numerical results for the modified IEEE 14, 30, and 57-bus systems are used to verify the merits of the proposed method. Furthermore, this method is experimentally validated using the CIGRE B4 DC grid benchmark in a real-time hardware-in-the-loop platform. [ABSTRACT FROM AUTHOR]

This paper copes with the state estimation and topology identification problems in direct current (DC) networks. This problem is challenging due to binary decisions and nonlinear relations between sensor measurements and state variables. We introduce a non-convex nuclear norm estimator whose non-convexity is addressed by incorporating two inertia terms. In the presence of noise, penalty terms are integrated into the objective function to estimate unknown noise values. Numerical results for the modified IEEE 9-bus, 14-bus, and 30-bus systems corroborate the merits of the proposed technique. Furthermore, this technique is experimentally validated for a converter-augmented 14-bus system in a real-time hardware-in-the-loop platform. [ABSTRACT FROM AUTHOR]

Power buffers can help improve the inertia in DC microgrids and supply the additional power demand during short load transients. This article investigates distributed event‐triggered control of power buffers over an unreliable network, subject to packet losses and transmission delays. For each power buffer, a distributed event‐triggered control law regulates the input impedance while altering its stored energy. Moreover, a dynamic event‐triggered communication mechanism determines the time sequence of control updates and data transmissions among power buffers. An appealing feature of this mechanism is a designable positive minimum inter‐event time despite packet losses and transmission delays. The closed‐loop system is shown to be exponentially stable. Controller/hardware‐in‐the‐loop studies validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article investigates distributed event-triggered control (ETC) of power buffers in a direct current (DC) microgrid. In order to facilitate the control design, a linear interconnected system model is derived that captures the physical coupling among power buffers. Then, a distributed ETC law regulates the stored energy and input impedance of each power buffer, and a decentralized dynamic event-triggering mechanism determines when each power buffer communicates with its neighboring buffers. This strategy eliminates the need for both continuous controller updates and continuous communication among the power buffers. The resulting closed-loop system is shown to be exponentially stable under a mild assumption on the communication network. The proposed event-triggering mechanism guarantees not only the exclusion of the Zeno behavior but also the existence of a positive minimum interevent time that can be adjusted by the control design parameters. Simulation studies validate the effectiveness of the proposed theoretical results for a multibuffer DC microgrid. [ABSTRACT FROM AUTHOR]

Emerging distributed control paradigms rely on communication among converters of a microgrid. We investigate the secondary control of dc microgrids with a distributed dynamic event-triggering mechanism. The physical and cyber layers are represented by different graph topologies. To reduce the communication burden, a distributed dynamic event-triggering mechanism is designed. Therein, the Zeno behavior is excluded and in addition, a positive minimum interevent time (MIET) is determined. Asymptotic stability of the closed-loop system dynamics is proven, and the adjustment of the positive MIET is discussed. Compared to the existing static event-triggering mechanisms, the proposed dynamic one guarantees the existence of a positive MIET, whose value can be tuned by the design parameters. Controller/hardware-in-the-loop implementation results validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article details and solves a small-signal stability-constrained optimal power flow (SSSC-OPF) for inverter-based ac microgrids. To ensure a sufficient stability margin during optimal generation, a small-signal stability constraint is embedded into the conventional OPF formulation. This condition is enforced using a Lyapunov stability equation. A reduced-order model of the microgrid is adopted to alleviate the computational burden involved in solving the resulting SSSC-OPF. Even then, the resulting stability conditions are highly nonlinear and cannot be handled using the existing methods. To tackle the nonconvexity in the SSSC-OPF due to the presence of the nonlinear stability constraint, two distinct convex relaxation approaches, namely semidefinite programming and parabolic relaxations, are developed. A heuristic penalty function is added to the objective function of the relaxed SSSC-OPF, which is solved sequentially to obtain a feasible point. While off-the-shelf tools fail to produce any feasible point within hours, the proposed approach enables us to solve the SSSC-OPF in near real time. The efficacy of the proposed SSSC-OPF is evaluated by performing numerical studies on multiple benchmarks as well as real-time studies on a microgrid system built in a controller/hardware-in-the-loop setup. [ABSTRACT FROM AUTHOR]

A cooperative control paradigm is used to establish a distributed secondary/primary control framework for dc microgrids. The conventional secondary control, that adjusts the voltage set point for the local droop mechanism, is replaced by a voltage regulator and a current regulator. A noise-resilient voltage observer is introduced that uses neighbors' data to estimate the average voltage across the microgrid. The voltage regulator processes this estimation and generates a voltage correction term to adjust the local voltage set point. This adjustment maintains the microgrid voltage level as desired by the tertiary control. The current regulator compares the local per-unit current of each converter with the neighbors' and, accordingly, provides a second voltage correction term to synchronize per-unit currents and, thus, provide proportional load sharing. The proposed controller precisely handles the transmission line impedances. The controller on each converter communicates with only its neighbor converters on a communication graph. The graph is a sparse network of communication links spanned across the microgrid to facilitate data exchange. The global dynamic model of the microgrid is derived, and design guidelines are provided to tune the system's dynamic response. A low-voltage dc microgrid prototype is set up, where the controller performance, noise resiliency, link-failure resiliency, and the plug-and-play capability features are successfully verified. [ABSTRACT FROM AUTHOR]

In this paper, input–output feedback linearization is used to design distributed controls for multi-agent systems with nonlinear and heterogeneous non-identical dynamics. Using feedback linearization, the nonlinear and heterogeneous dynamics of agents are transformed to identical linear dynamics and non-identical internal dynamics. Based on the dependence of agent outputs on agent inputs, feedback linearization may lead to a first-order or high-order tracking synchronization problem. The controller for each agent is designed to be fully distributed such that each agent only requires its own information and the information of its neighbors. The effectiveness of the proposed control protocols are verified by simulation on a microgrid test system. [ABSTRACT FROM AUTHOR]

This article identifies the parameters of an induction machine using limited and nonintrusive observations of available input voltages, stator currents, and the rotor speed. Parameter extraction is formulated as a nonconvex estimation problem, which is then relaxed to a convex conic optimization problem. While the resulting relaxation could exhibit a satisfactory performance, there might be cases where the solution of convex relaxation fails to satisfy the dynamic equations of the machine. This is remedied through a local search approach initiated using the solution obtained from the relaxed problem. The proposed method is experimentally verified on a squirrel-cage induction machine with missing measured data. Using the measured signals as the benchmark, time-domain transients produced by the parameters estimated using the proposed method show almost 20% better match compared to time-domain transients produced by the parameters obtained via conventional testing. [ABSTRACT FROM AUTHOR]

A Cúk converter operating in discontinuous capacitor voltage mode (DCVM) is an inherent power factor correction converter. A reduced-order switch-network model of a Cúk converter in DCVM is developed, which is valid for both dc–dc and ac–dc applications. The model can be used for controller analysis and design. Moreover, a fast dynamic response controller is proposed, which is an integration of a switching-frequency control unit and a proportional-integral (PI) controller. The PI controller is in charge of duty ratio adjustment. In the proposed controller, unlike conventional PI controllers, switching frequency is no longer fixed and varies as load changes. The switching-frequency control unit reacts much faster than the PI controller and instantly compensates for the output voltage in case of drastic load change, thus significantly improving the transient response. Furthermore, the proposed control scheme keeps the switch voltage stress constant, which eases switch selection and improves converter reliability. [ABSTRACT FROM PUBLISHER]

The waveform relaxation (WR) technique is used to accelerate the time-domain simulation of high-fidelity models of supercapacitors. Because of their high power density, supercapacitors are suitable energy storage options in electrified transportation fleets and renewable energy systems. Given their fast charging/discharging profile, high-fidelity models, such as transmission-line models or multistage ladder structures, are conventionally adopted for design and simulation purposes. High-fidelity models, that include fast dynamic modes, can slow down the simulation process. This is problematic, in particular, since supercapacitors are parts of a larger, more complex system, e.g., a power train, with a wide dynamic range. In this paper, frequency-domain characterization of a supercapacitor is conducted by the electrochemical impedance spectroscopy. Then, the equivalent multistage ladder model of the supercapacitor is extracted and parameterized. The high-fidelity model is verified by considering the measured charging profile of the supercapacitor. The WR technique, including circuit partitioning and time windowing, is considered for the resulting high-fidelity model. WR results in an order-of-magnitude improvement in simulation speed while maintaining an excellent agreement with the hardware measurement and conventional simulations. [ABSTRACT FROM AUTHOR]

Space constraint is a limiting factor for the widespread commercial adaptation of switched reluctance generators (SRGs). Maximizing the output power and minimizing the dc bus filter size are thus highly desired. The output power profiles of a typical SRG are determined by applying various excitation angles. Excitation for maximum output power is obtained while limiting the rms value of the phase currents. The dc bus current of an SRG drive, operating in single pulse mode, is highly distorted by low-frequency ripples. Introducing a freewheeling angle in the excitation pattern can lower the current ripple factor. A novel transform is proposed to establish a relationship between the conventional and the freewheeling excitation patterns. An optimal symmetric freewheeling excitation is accordingly proposed that uses an optimal freewheeling angle and minimizes the ripple factor of the dc bus current while keeping other operational variables intact. Consequently, the output power maximization and current ripple minimization processes are decoupled. This decoupling technique accelerates the overall optimization process. Numerical simulation and hardware measurements verify the proposed methodology. [ABSTRACT FROM PUBLISHER]

This paper proposes a new experimental procedure for magnetic characterization of switched reluctance machines. In the existing methods, phase voltage and current data are captured and further processed to find the flux linkage. Conventionally, assuming zero initial flux value, the flux linkage can be found by integrating the corresponding voltage term. However, the initial flux value is usually unknown, e.g., it can be nonzero when the current is zero due to the residual flux effect, and, thus, imposes error in magnetic characterization. The proposed method addresses this issue by considering an additional equation in steady state. This method injects a low-frequency sinusoidal current to one of the phase windings when the rotor is blocked at a given position. Since the phase is excited by a sinusoidal current, the averaged flux over an excitation cycle is zero, even though the residual flux and core loss exist. This additional equation together with the voltage integration make it possible to avoid errors associated with the core nonidealities and accurately solve for the magnetic flux. Furthermore, an analytical expression is proposed that precisely fits the magnetic curves. The proposed characterization methodology and analytical model are verified using the experimental results from a 3-phase 12/8 switched reluctance machine. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for designing a fixed shunt capacitive compensator to improve first swing stability margin of a micro-grid including synchronous machine-based distributed generators (SMDGs). The notion of trajectory sensitivity analysis is used to determine the sensitivity of the SMDGs' relative rotor angles and velocities with respect to the compensator susceptance. For a given fault contingency, the minimum required susceptance and compensator location are determined to maintain the first swing stability of SMDGs. The proposed methodology can be used for a micro-grid with an arbitrary number of SMDGs, various fault scenarios, and both grid-connected and autonomous (islanded) modes of operation. The proposed algorithm is verified using time-domain simulation of a modified benchmark system of the IEEE standard 399-1997. [ABSTRACT FROM PUBLISHER]

The analytical derivation of the average-value models (AVMs) for synchronous-machine-fed rectifier systems depends on many factors and is challenging in general. Various models presented in the literature rely on a single operating mode and a dc filter configuration. A recently proposed parametric approach utilizes a detailed simulation as a basis to establish an AVM that uses a proper full-order qd generator model. This paper presents a procedure to extract the appropriate nonlinear algebraic functions to represent the rectifier averaged behavior. In contrast to existing methods, the proposed procedure does not require significant analytical derivations and/or computational resources and results in a highly accurate dynamic AVM. The proposed methodology is validated with the results of detailed simulation and hardware experiment, and is shown to be very accurate in predicting the large-signal time-domain transients as well as the small-signal frequency-domain characteristics. [ABSTRACT FROM AUTHOR]

A new average-value model of a rectifier circuit in a synchronous-machine-fed rectifier system is set forth. In the proposed approach, a proper state model of the synchronous machine in the qd-rotor reference frame is used, whereas the rectifier/dc-link dynamics are represented using a suitable proper transfer function and a set of nonlinear algebraic functions that are obtained from the detailed model using numerical averaging. The new model is compared to a detailed simulation as well as to measured data and is shown to be very accurate in predicting the large-signal time-domain transients as well as small-signal frequency-domain characteristics. [ABSTRACT FROM AUTHOR]

A power buffer is a power electronics converter with a large capacitor that shields a weak DC grid from abrupt load changes. Distributed control solutions have been shown to be superior to the decentralized ones; however, the effects of the communication network topology on the control performance of these buffers have not yet been studied. This article offers a data-driven optimal solution to reduce the interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods deal with the optimal control of nonlinear systems, and a Tabu Search method addresses the resulting combinatorial problem. The proposed solutions are validated for a DC microgrid in a controller/hardware-in-the-loop environment. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of optimizing voltage set points and switching status of components in direct current power networks subject to physical and security constraints. The problem is cast as a mixed-integer nonlinear programming with two sources of computational complexity: i) Non-convex power flow equations, and ii) The presence of binary variables accounting for the on/off status of network components. A strengthened second-order cone programming (SOCP) relaxation is developed to tackle the non-convexity of power flow equations, and a branch-and-bound search is employed for determining optimal network configurations. The efficacy of the proposed method in optimizing the operation while mitigating contingencies is experimentally validated in a real-time hardware-in-the-loop environment using IEEE benchmark data. [ABSTRACT FROM AUTHOR]

Power buffers are power electronic converters, with large capacitors, that decouple volatile loads and a low-inertia distribution network in a DC microgrid. In this work, a set of distributed optimal control policies enable power buffers to reciprocally assist each other during abrupt load changes. While the majority of existing control paradigms are localized, enabling communication among buffers extends their effective range of assistance and helps them minimize a shared objective in a cooperative fashion. The control law's weights surfaces are learned for a mesh of reference loads of each power buffer. Hamilton-Jacobi-Bellman equation is solved by a continuous-time adaptive dynamic programming (ADP) approach with off-policy learning to directly provide a feedback controller, instead of existing approaches that obtain open-loop policies via Pontryagin's minimum principle. This paper presents the first attempt in using ADP techniques for the control of power buffers that respects their original nonlinear dynamics, overcoming the limitations of previous approaches based on small-signal analysis. Compared to the current literature, the proposed approach provides trained controllers that are known a priori, avoiding player-by-player solutions or real-time optimization procedures that could degrade performances or become computationally intensive. Hardware-in-the-loop emulations of a low-voltage DC microgrid validates the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the acceleration of numerical simulations executed on Field-Programmable Gate Arrays (FPGAs) for electric machines presented by voltage-behind-reactance (VBR) models. In VBR models, the stator dynamics are modeled in abc coordinates, while the rotor dynamics are formulated in qd reference frame. Both induction motors and synchronous generators, operating without and with magnetic saturation, are considered. Once VBR models of these machine types are reviewed, their dynamic models are discretized using Runge-Kutta numerical routines. The detailed mapping of such discrete models to FPGA is provided using High-Level Synthesis, which directly converts untimed descriptions into VHDL or Verilog. An automated method finds the fastest FPGA architecture by finding the best set of synthesis options. Experimental results show that our FPGA-based acceleration flow leads to about 92-168 times average simulation speed-up for various machine types compared to the MATLAB simulation. [ABSTRACT FROM AUTHOR]

We extract the lumped-parameter model of a wound-rotor synchronous machine from its physics-based magnetic-equivalent circuit model. Model extraction is formulated as a weighted least square optimization with nonlinear constraints in which time-domain trajectories of flux linkages, currents, and the electromagnetic torque are used as input data to obtain the parameters of the $qd0$ model of the machine. The resulting problem is non-convex and cannot be solved using standard methods. The optimization problem is, therefore, convexified using a cone programming relaxation. The solution to the relaxed problem is used as an initial point for the interior-point method, leading to a reliable framework. Accurate estimations on stator resistance, leakage and mutual inductances in stator and rotor, rotor speed, effective turns-ratio between the field and stator windings, and the number of poles are obtained. Estimated parameters are validated against measured and estimated values reported in literature, and are used to develop a behavioral $qd0$ macromodel of the machine. [ABSTRACT FROM AUTHOR]

This paper presents an approach to obtain reduced-order models for power networks involving power electronic converters (PEC) via the frequency-domain balanced realizations (FDBR) technique. PECs play an essential role in power processing and energy conversion in modern electrical networks, such as the interconnection of renewable generators, HVDC links, and active filters. Integration of PECs into dynamic equivalents needs model-order reduction (MOR) in both low- and high-frequency ranges to account for both slow and fast dynamics due to the network and switching natures. The objective of the FDBR technique is to obtain an internally balanced system, i.e., an equally controllable/observable system, that can be reduced according to its dominant dynamics within the limited frequency bandwidths. This allows accounting for specific band-limited phenomena, such as those generated within a power network caused by PECs, which is the focus of this paper. The results show that faster yet accurate simulations are achieved by reduced-order models through FDBR compared to their full-order counterparts. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven model-free inverse reinforcement learning (RL) algorithm to reconstruct the unknown cost function of the demonstrated discrete-time (DT) dynamical systems with antagonistic disturbances. We propose an inverse RL policy iteration scheme that uses system dynamics and the input policies, for deriving our main result of a data-driven off-policy inverse Q-learning algorithm using only demonstrated trajectories of the antagonistic system without knowing system dynamics and the control policy gain. This data-driven algorithm consists of Q -function evaluation, state-penalty weight improvement, and action policies update. We guarantee unbiased estimates in the data-driven algorithm when exploration noises exist for the persistence of the excitation. An example verifies the proposed algorithm. [ABSTRACT FROM AUTHOR]