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Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

A fast and precise dynamic phasor estimation algorithm using an adaptive improved second order Levenberg–Marquadt algorithm under stressed and off-nominal frequency circumstances is proposed in this paper. The proposed algorithm ventures a faster quasi-second-order method and in addition, the learning rate is also made adaptive to speed up the convergence. Moreover, the projected estimator essentially filters harmonics, estimates transients in a signal during faults and effectually performs in complex modulated setting which is found commonly in a power system with distributed energy resources. Outcomes reveal that the estimation errors and the convergence time of the proposed approach is way below the minimum requirement of the IEEE C37.118.1 standard. Besides, due to its reduced complexity, the estimator has been successfully implemented on the field programmable gate array (FPGA) platform and has been verified on a practical microgrid scenario including photo voltaic systems. The performance during testing indicates that the proposed technique has potential ability to perform under stressed conditions conforming the accuracy and speed of response. [ABSTRACT FROM AUTHOR]

The concept of energy Internet (EI) is very promising due to its ability to integrate distributed generators and storage devices. Energy routers (ERs) enable highly efficient, reliable, and flexible power system operation. To dynamically control and optimize energy flow in EI, an extensive communication is required. Considering the diversity of generators and ERs, it is crucial that such communication is achieved in a standardized and interoperable way. In this paper, an improved energy routing algorithm and its impact in EI operation are demonstrated. Furthermore, a full communication solution is developed for ERs and their interaction with grid operator. This paper proposes an International Electrotechnical Commission (IEC) 61850 communication-based power routing algorithm in microgrids. Optimal selection of source and routing path is achieved according to the proposed algorithm and communicated to the ERs via IEC 61850 based communication. The effectiveness of the proposed IEC 61850 communication-based routing algorithm is verified on network emulation platform comprising of IEC 61850 emulator tools and network simulator tool. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a novel near-optimal finite-control-set model predictive control (NOP-MPC) algorithm to control the grid-connected, pulsewidth-modulator-driven voltage source converters with LCL filters. Exploiting the convex and elliptical paraboloid properties of the cost error, NOP-MPC adopts a systematic iterative algorithm within each control cycle to progressively synthesize finite sets of virtual voltage vectors (VVs) for the control optimization stage. The synthesis has the inherent features of respecting the converter voltage limits and converging the sets of VV candidates toward the global optimal point. The fixed-switching-frequency feature of NOP-MPC is expected to ease the LCL filter design. Effects of computational delay, pulsewidth modulation delay, and deadtime are considered and compensated successfully. A two-vector-variable cost function is used to actively damp the inherent LC resonance through an adjustable, weighting-factor-based damping level. This paper is substantiated by theoretical consideration, simulation and experimental results, parameter sensitivity study, and a comparative study with the standard finite-control-set model predictive control that uses only actual VVs. [ABSTRACT FROM AUTHOR]

In most multiobjective optimization problems of electrical machines, the weighted function method is used to convert them into single-objective optimization problems. This paper applies a kind of new multiobjective evolutionary algorithms (MOEAs), called adaptive multiobjective black hole (AMOBH) algorithms, to achieve effective multiobjective optimization of a tubular coreless linear permanent magnet synchronous motor (LPMSM). To reduce the computation cost of the MOEAs, a one-layer analytical model (AM) is presented for the tubular coreless LPMSM in this paper. The accuracy of the simplified one-layer AM is verified by comparisons with multilayer AM and finite element analysis (FEA) under different structure parameters. It is found that the simplified AM has good accuracy and can decrease the computation cost significantly. AMOBH algorithm is subsequently introduced. The optimal Pareto front with regard to thrust, copper loss, and permanent magnet volume are analyzed, and more diversified optimization results are provided. The final Pareto solution can be selected directly by practical physical values according to the application requirements. Finally, a prototype is fabricated for the selected design; its experimental results are provided and compared with those of the FEA results. [ABSTRACT FROM AUTHOR]

In this paper, we propose adaptive algorithms of gain tuning for Kalman filters and switching Kalman and $\mathcal {H}_{\infty }$ filters for discrete systems. Both of the gain tuning and switching rely on square means of innovations. This paper also provides stability analyses on time-varying Kalman filters and derives a sufficient condition for their asymptotic stability, on which our gain-tuning algorithm is based. It should be noted that the stability condition is available for even nonstabilizable systems having their uncontrollable poles on the unit circle. To illustrate those algorithms, we perform simulations using a harmonic oscillator model that is nonstabilizable and has its poles on the unit circle. Furthermore, we apply the algorithms to estimation of a ship's oscillation, particularly, with time-varying frequencies by simulations and model experiments. Consequently, all the results of stability analyses, simulations, and experiments have convinced that the algorithms are solid and effective. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to estimate the number of shorted turns in a permanent magnet synchronous machine (PMSM) following the detection of interturn short-circuit (ITSC) fault and its location. In this proposed method, PMSM is excited through a low sinusoidal voltage at standstill condition to obtain the winding resistance and synchronous inductance by current response. It is shown that the ITSC fault introduces variation in the current response, which can be used to calculate the number of shorted turns under zero fault resistance assumption. Using this practical procedure, the fault severity can be estimated directly in a straight-forward manner. In other words, the severity estimation for a given machine can be done without complex machine modeling or experiments on ITSC prototype with multiple taps. The findings in this paper are essential for a comprehensive solution including fault mitigation algorithms and postfault operations. In order to verify the findings, a three-phase equivalent circuit model supported by finite element analysis results is used to take saturation and space harmonics into account. In addition, experimental results are presented to demonstrate the validity and practicability of the severity estimation. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

Time–frequency analysis (TFA) is considered as a useful tool to extract the time-variant features of the nonstationary signal. In this paper, a new method called demodulated high-order synchrosqueezing transform (DHST) is proposed. The DHST introduces a two-step algorithm, namely, demodulated transform and high-order synchrosqueezing method to achieve a compact time–frequency representation (TFR) while enabling the reconstruction of the signal from TFR. The performance of the proposed DHST method in this paper is validated by both the simulated and experimental signals including bat echolocation and a vibration signal. The results show that the proposed TFA method is more effective in processing the nonstationary signals with fast varying instantaneous frequency. [ABSTRACT FROM AUTHOR]

This paper investigates the use of large-scale wayside energy storage to reduce operational costs of ac electric traction power networks. This paper also introduces a method of estimating the battery capacity, based on manufacturer's charging and discharging specifications. The battery model is simple and easy to incorporate into load flow simulation algorithms. The concept and the battery model are validated through a feasibility study within Aurizon's heavy haul electric railway network. [ABSTRACT FROM AUTHOR]

Inclusion of power electronics allows increased controllability and stability in power systems. The simulation of such systems on a large-scale is challenging due to the presence of a large number of switches and nonlinear devices. This paper presents an advanced simulation algorithm to solve the aforementioned problem. The algorithm considers separation of differential algebraic equations (DAEs) on the basis of numerical stiffness and applies hybrid discretization algorithms to simulate the DAEs. The DAEs, in this paper, represent the nonlinear nonautonomous switched system dynamics of power systems. Stability analysis is performed on a general class of nonlinear nonautonomous switched systems to show the constraints under which the proposed algorithm is stable. To show the validity of the proposed algorithm, two case studies are considered: 1) single high-voltage direct current (HVdc) substation based on the modular multilevel converter (MMC); and 2) an example three-terminal MMC-HVdc system. Relaxation techniques are introduced to create a stable interface for the separated DAEs. The developed algorithms are also validated with PSCAD/EMTDC-detailed reference models. [ABSTRACT FROM AUTHOR]

To drive the machine tool along a curve, a commercial computer-aid design and computer-aided manufacturing system usually breaks the curve into a set of line segments. This paper provides a systematic way for generating smooth and fast machining velocity planning for contouring line segments in a machine tool. It is especially useful for mass manufacturing. In this paper, the particle swarm optimization algorithm is employed to optimize the design parameters. The algorithm allows for as many motion limits as possible, not only the limits along the path, but those on the axes, as well. It can automatically determine the existence of acceleration, constant speed, and deceleration zones of the S-curve velocity profile. Also, it can determine the optimal corner speed with a given corner error. The computation time of the algorithm is fast so that it allows for online operation. Simulation and experimental results demonstrate that machine tools using the proposed algorithm go fast where possible and slow down where needed, dramatically increasing the productivity. [ABSTRACT FROM AUTHOR]

In recent years, vehicle detectors built on deep convolutional neural network (DCNN) have been widely used in autonomous driving. Under the complex conditions of road traffic, the detector is expected to run in high speed and high accuracy. However, due to the limited computing power and storage space on the autonomous vehicle, the deployment of advanced DCNN detectors is often restricted. The design of lightweight and powerful detectors is in a great desire. Recently, group convolution, as a novel convolution algorithm, has been proposed to reduce the floating-point operations and make the detection network lighter and faster. However, in practice, it is found that the increase of group number does not always boost the detection speed, but sometimes leads to the performance degradation. In addition, the existing guidelines for network design do not indicate how to choose the group number in the group convolution in order to maximize the overall detection speed. To this end, in this paper, we propose three new guidelines to determine the valid range of the group number, and design a lightweight detection network—DenseLightNet—based on these new design criteria. The proposed detector runs at a speed of three times faster than the current real-time detector YoloV3, while holding a much smaller model size. [ABSTRACT FROM AUTHOR]

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Force estimation methods enable robots to interact with the environment or humans compliantly and safely without additional sensing device. In this paper, we present a novel method for estimating unknown contact forces exerted on a robot manipulator. The force estimation method is divided into two steps. The first step is to identify a robot dynamics model. A parametric model is derived first based on rigid-body dynamic (RBD) theory. To improve the model accuracy, a nonparametric compensator trained with multilayer perception (MLP) is added to compensate for errors of the RBD model. The result is a semiparametric model that provides better model accuracy than either the RBD model or the MLP model alone. The second step is to construct a force estimation observer. A novel estimation method called disturbance Kalman filter (DKF) is developed in this paper. The design of DKF based on a time-invariant composite system model is presented. DKF can take both manipulator's dynamics model and disturbance's dynamics model into account. As with Kalman filter, it can provide robust and accurate estimation against uncertainty. Simulation and experimental results, obtained using a six-degrees-of-freedom Kinova Jaco2 manipulator, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly. [ABSTRACT FROM AUTHOR]

This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald–Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, we design a linear time-invariant two-input-two-output controller for the permanent magnet synchronous motor (PMSM). First, the nonlinear system of a PMSM is approximated using a linear system with structured uncertainties according to the geometric structures of PMSM. We then design a linear controller for the approximated linear system using a standard $\mu$-synthesis robust control method. The main contribution of this paper is that we recognize that the nonlinear PMSM can be reduced to a linear system to apply the mature modern control theory, without referring to classical PID control, thus largely reducing the design effort. The newly designed robust controller is not only easy to calculate, it is also easy to implement and requires less sensors. By virtue of modern robust control theory, it responds fast to exogenous inputs, and robust against parameter uncertainties as well. The applicability of the designed robust controller is both numerically simulated and experimentally verified on a TMS320 F28335 based control board, with performance further compared with the widely used field-oriented controller. [ABSTRACT FROM PUBLISHER]

Data-driven soft sensors, estimating the pivotal quality variables, have been widely employed in industrial process. This paper proposes a novel soft sensor modeling approach based on a two-stream ${\rm{\lambda }}$ gated recurrent unit ($TS - {\rm{\lambda }}$ GRU) network. First, factors ${{\rm{\lambda }}_1}$ and ${{\rm{\lambda }}_2}$ are implemented to alter the linear constraint existing in the original GRU unit, enriching the information passing through. Then, a two-stream network structure is designed, equipped with some advanced network parameter adjustment techniques, such as batch normalization and dropout rate, to learn diverse features of the various process data. Finally, the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label. The application in melt viscosity index estimation for a real polymerization industrial process has demonstrated that the proposed $TS - {\rm{\lambda }}$ GRUs algorithm for soft sensor modeling is more accurate and promising than other existing methods. [ABSTRACT FROM AUTHOR]

The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size ($n$ $ >$ 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a control strategy for the position and posture control of a planar four-link underactuated manipulator with a passive second link based on a back-propagation neural network (BPNN) model. First, using the differential evolution (DE) optimization algorithm, the target angles of all links are calculated. The third and fourth links are then controlled from their initial angles to zero. Next, the control process is divided into the following two steps: the control objective of the passive link is implemented in the first step, and the control objectives of the active links are implemented in the second step. In the first step, the system is reduced to a planar virtual Pendubot, a BPNN model based on the DE algorithm is trained to obtain the coupling relationship between the links of this Pendubot. This model gives the speed of the first link to move the passive link to approach the target angle. Then, a fuzzy controller is designed to indirectly control the passive link to its target angle. In the second step, the position mode of the servo controller is adopted to control all active links to their target angles. Experimental results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Motivated by the fact that gossip algorithms are asynchronous and suitable for the sensor networks with random link failures, a gossip-based distributed algorithm for the economic dispatch problem in smart grids is addressed in this paper. Random communication link failure phenomena is very common in a realistic communication network. By modeling a network as a Bernoulli network, the proposed algorithm can solve the economic dispatch problem in the imperfect communication network. Mean square consensus and almost sure consensus are adopted to analyze the convergence of the algorithm. Using matrix theory and perturbation theory, we prove that the algorithm can achieve mean square consensus and almost sure consensus. Therefore, the proposed algorithm converges to the optimal solution of the economic dispatch problem with probability 1. The convergence speed of the algorithm is discussed and simulation results show that the convergence rate is governed by the second largest eigenvalue of the system matrix. Case studies are designed to illustrate the effectiveness and plug-and-play capability of the algorithm. [ABSTRACT FROM AUTHOR]

An accurate and efficient model for selective catalytic reduction (SCR) systems plays an important role in diagnosis and control of diesel-engine after-treatment systems. In this paper, we investigate the data-driven modeling of SCR systems and outlet NO $_{x}$ concentration estimation based on the developed data-driven model and the algorithm of unbiased finite impulse response (UFIR) filtering. The structure used for the data-driven model is an autoregressive exogenous (ARX) model and the method of partial least square is utilized to identify the parameters of the corresponding ARX model. Moreover, the approach of fuzzy c-means is employed to partition the data and derive multiple local linear models with a better performance on approximating the system nonlinearities. Finally, the algorithm of UFIR filtering is adopted to estimate the outlet NO $_{x}$ concentration due to its strong robustness without the statistics of process and measurement noises. The performance of proposed approaches on SCR systems is validated with simulations based on experimental data. In addition, comparisons show the improvement of the adopted algorithm on the estimation accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a precise control algorithm for active brake pressurization based on our self-designed electro-booster (Ebooster) brake system is proposed. First, the inertial characteristic, nonlinear hydraulic characteristic, and dynamic friction characteristic of the Ebooster system are analyzed and modeled. We design a cascade control algorithm including a pressure loop by the feedforward-feedback method, position loop by the triple-step nonlinear method, and current loop by the current distribution, flux-weakening, and current control, and consider the system's nonlinear and dynamic characteristics. We evaluate the controller through simulations and hardware in the loop tests, and the results demonstrate satisfactory accuracy and response speed. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid battery parameterization technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centered around an opportunistic initialization of a dual extended Kalman filter (DEKF) algorithm using pseudorandom binary sequence (PRBS) battery excitation. A second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilized to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC, and subsequently its SOP in real time. The experimental results obtained from an electrochemical impedance spectroscopy method give confidence to the performance of the proposed hybrid battery parameterization technique. [ABSTRACT FROM AUTHOR]

This paper is devoted to the industrial practices and theoretical approaches for detection and isolation of quality-related multiple faults in large-scale processes. In contrast to the previous schemes, the main innovations are as follows: 1) it is the first time a hierarchical detection and isolation framework for quality-related multiple faults in large-scale processes is developed; 2) a combination method of adaptive kernel canonical variable analysis and Bayesian fusion for real-time and hierarchical detection of varying and unknown quality-related multiple faults is presented; and 3) a robust sparse exponential discriminant analysis algorithm for accurate isolation of multimode quality-related multiple faults is proposed. Finally, the whole framework is applied to a typical large-scale process, i.e., hot strip mill process, where the performance and effectiveness are further demonstrated from real industrial data. [ABSTRACT FROM AUTHOR]

The key to successful detection of a localized bearing fault lies in extracting fault-induced impulse responses. Unfortunately, these responses are often contaminated by background noise. The popular K-singular value decomposition (K-SVD) denoising algorithm can be used to extract impulse responses from noise, but it may obtain weak impulse responses against reliable fault detection. An SOSO boosting technique is proposed to improve the performance of the K-SVD denoising algorithm. Given an initial denoised signal, this paper proposes iteratively repeating the following SOSO boosting procedure: 1) Strengthen the underlying signal by adding the previous denoised signal to the noisy input signal; 2) Operate the K-SVD denoising algorithm on the strengthened signal; 3) Subtract the previous denoised signal from the restored signal-strengthened outcome; and 4) Operate the modified K-SVD denoising algorithm again on the resulting signal after the subtraction. The last step, i.e., the secondary denoising step, plays a crucial role in preventing the enhancement of the residual noise that originates from the first denoised result. The results of numerical and experimental studies show that the SOSO-based K-SVD denoising algorithm not only enhances weak impulse responses significantly but also reduces potential residual noise effectively compared to the SOS-based and the original K-SVD denoising algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a phase-locked loop algorithm adequate for applications regarding single-phase power grids. The proposed approach is based on the correlation of the input signal with a complex one obtained from an adaptive filter aiming at minimizing computational burden and increasing accuracy when compared with a former algorithm previously proposed in the literature. Thus, it is possible to obtain high disturbance rejection, especially when dealing with the presence of subharmonics and interharmonics in the frequency spectrum of the supply voltage. Performance is thoroughly evaluated through experimental tests considering both steady-state and dynamic behaviors, while a proper comparison is established with other similar solutions. [ABSTRACT FROM AUTHOR]

Due to the private ownerships of various resource and equipment in a microgrid, it is essential to design a proper power trading strategy for a mutually satisfying partnership among suppliers and users. This paper proposes a novel discounted min-consensus algorithm to discover the optimal electrical power trading route with minimal power loss in dc microgrids. The proposed distributed algorithm only requires information exchange among neighbors based on a peer-to-peer communication protocol, which is robust to topology changes and enables parallel data processing to achieve fast decision-making. The convergence and optimality of the proposed discounted min-consensus algorithm are guaranteed by rigorous analysis. Simulation results of the 10-bus, 68-bus, and 1062-bus power systems demonstrate the effectiveness of the proposed distributed optimal routing strategy for electrical power trading. Moreover, the proposed novel distributed algorithm sheds light on applying distributed control and optimization approaches to solve problems arising in artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper presents an efficient double-layer ant colony optimization algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms that run independently and successively. First, a parallel elite ant colony optimization method is proposed to generate an initial collision-free path in a complex map, and then, we apply a path improvement algorithm called turning point optimization algorithm, in which the initial path is optimized in terms of length, smoothness, and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Grid condition information extraction is very important for converter control systems in order to perform special functionalities, especially grid support during low voltage ride through (LVRT). As a part of grid condition detection, the voltage sag detection provides information about the start of a fault and obtains indices that determine the behavior of the whole control system of the converter during a fault. Estimation of the magnitude of voltage sag (MoVS) is essential for obtaining proper grid support. This paper presents an algorithm for MoVS (as defined in IEEE Std 1564-2014) prediction. The existence of correlation between magnitudes of a set of low-order harmonics during the transient of voltage PQ events with MoVS is for the first time determined and statistically proved. Correlation is mathematically formulated and the prediction function is obtained on the basis of real grid measurements. Complete algorithm for magnitude prediction is explained, and its possible application in distributed generation control is prese-nted. It is shown that significant time gain is achieved, which may be used as an additional benefit in LVRT operation. [ABSTRACT FROM AUTHOR]

In order to obtain a good optimization method for the electrical transformer design with optimal selection of parameters, performance evaluation of three evolutionary algorithms (EAs), namely, genetic algorithm (GA), differential evolution algorithm, and nondominated sorting GA (NSGA-II), is carried out. The aim of this paper is to optimize parameters of transformer design (core thicknesses, primary-turn number, secondary-turn number, primary conductor area, and secondary conductor area) for minimization of total power losses (no-load losses and load losses) in three-phase transformer topology while maintaining high efficiency and low cost. The method used for this optimization scheme combines the finite-element method (FEM) and EAs to provide an accurate selection of parameters together with the optimized magnetic flux density and decreased loss. Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers. [ABSTRACT FROM AUTHOR]

In this paper, a novel guided motion planning method is proposed for snake-like robots, which decomposes the configuration space into the fiber space and the gait space, with the motion planned in the low-dimensional fiber space, so as to avoid the dimensionality curse problem. More specifically, first, based on geometry mechanics, the kinematic connection is derived, which successfully maps the motion in the fiber space into that in the gait space. According to the kinematic connection, a novel bidirectional motion control method for snake-like robots is proposed to improve the flexibility that can produce not only the forward locomotion but also the backward locomotion additionally. The convergence of the bidirectional motion control method is proved based on averaging theory. Then, the motion planning problem is solved in the low-dimensional fiber space, for which the HJB equation is used to generate the optimal control in the fiber space to avoid the well-known curse of dimensionality. Finally, combining the bidirectional motion control and the motion planned in the fiber space, a guided motion planning method in the configuration space is proposed. Numerical simulations and experiments based on the real snake-like robot platform are performed, whose results demonstrate that the proposed algorithm is valid and robust. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control is an emerging technique for the control of power electronic converters. This paper introduces a simple and efficient predictive torque control (PTC) algorithm for induction motor drive. The proposed technique eliminates the need for flux-weighting factor for the conventional PTC. As a result, tedious offline tuning is no longer required. At the same time, the proposed technique ensures that the relative importance between torque and flux ripples is determined in an online fashion. Moreover, unlike the conventional method that needs to evaluate the cost function seven times (for two-level three-phase inverter case), the proposed method needs only to test four voltage vectors at each control sample which leads to a significant reduction in the computation time and switching frequency without sacrificing the performance. The effectiveness of the proposed method is demonstrated by both simulation and experimental results with comprehensive comparisons with the reported literature. [ABSTRACT FROM AUTHOR]

This paper proposes an improved maximum power point tracker (MPPT) for photovoltaic (PV) system. The scheme is a hybrid between the adaptive perturb and observe and particle swarm optimization (PSO). The algorithm incorporates the search-skip-judge (SSJ) mechanism to minimize the region within the P−V curve to be searched by the PSO. Furthermore, the PSO performance is enhanced by ensuring that the regions that have been previously explored (by other particle) will not be searched again by (another particle). Thus, the unnecessary movement of particles is minimized—leading to faster convergence. The proposed method is evaluated against four well-known MPPT techniques, namely the modified incremental conductance, the original version of SSJ, the modified cuckoo search, and the hybrid PSO. In addition, an experimental prototype, which is based on PV array simulator is used to verify the simulation. The competing algorithms are tested with a buck-boost converter, driven by the TMS320F240 DSP on the dSPACE DS1104 platform. It was found that the proposed scheme converges to the global maximum power point (GMPP) most rapidly and the GMPP tracking is guaranteed even under complex partial shading conditions. [ABSTRACT FROM AUTHOR]

It is very important to uniform the strand current in stator bars’ design of a large generator. As the stator bars are made of several parallel strands that are closely adjacent to one another, the strand current cannot be measured by straight-through current transformer. In order to measure the strand current without changing the structure of the stator bar, a measuring method for strand current of stator bars based on the superposition magnetic field measurement surround the strand by using search coils is proposed in this paper. The optimal position assembly is determined by the structure and the magnetic field of the stator bar in end region. The leakage magnetic field surrounding the measuring position is measured by search coils, and the strand current is solved by the algorithm of least square error function. The correctness of this method is verified by comparing with the result of current transformers. This measuring method not only provides an evaluation basis for the transposition design of stator windings in large generators but also provides a technical support for the current measuring of adjacent transmission lines and large capacity parallel cables. [ABSTRACT FROM AUTHOR]

This paper presents a highly efficient 3.3-kW single-stage on-board charger (OBC). The proposed OBC consists of two interleaved cells sharing a full-bridge diode rectifier. Each cell is composed of an active clamp circuit and a series resonant circuit. The proposed OBC provides zero-voltage switching of all switches and reduces the reverse-recovery problem by zero-current switching of the output diodes. The interleaved technique maintains the proper power level with high efficiency. The proposed control algorithm enables power factor correction by the OBC without any auxiliary circuit and balances the input current between the two cells. In addition, the proposed converter requires no electrolytic dc-link capacitor, ensuring compact size and a long lifetime. Owing to these features, the proposed OBC efficiently outputs a high power factor over a wide power range and achieves reliability with a simply structured, single-power processing step. Therefore, the proposed OBC is potentially feasible for automotive applications. Finally, the theoretical analysis is confirmed in a 3.3-kW prototype and validated in experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a promising cost-effective phase current detection scheme for different multiphase switched reluctance motors (SRMs) by employing the multiplexed current sensors. According to the phase current characteristics for the odd-numbered multiphase SRM with m phases and even-numbered multiphase SRM with n phases, the phase windings are specially divided into (m + 1)/2 and n/2 groups, respectively, and each group shares one Hall-effect current sensor. By building the matrix functions to express the relationship between phase currents and sensor currents, the phase currents can be calculated in real time with a simple algorithm and fewer current sensors. Compared to existing phase current detection schemes, the developed method has high universality and can achieve the intact phase current detection without losing any excitation, freewheeling, and demagnetization currents. Moreover, the presented method is easy to implement without any converter change and pulse injection, which shows great potential for massive industrial application to avoid the voltage penalty, current distortion, higher switching loss, and further electromagnetic interference issues. The simulation and experiments are carried out to validate the feasibility and effectiveness of the developed phase current detection scheme. [ABSTRACT FROM AUTHOR]

In sensor networks (SNs), how to allocate the resources so as to optimize data gathering and network utility is an important and challenging task. This paper studies the distributed optimization problem in SNs. A distributed hybrid-driven algorithm based on the coordinate descent method is presented for the optimization purpose. The proposed optimization algorithm differs from the existing ones since the hybrid driven scheme allows more choices of actuation time, resulting a tradeoff between communications and computation performance. Applying the proposed algorithm, each sensor node is driven in a hybrid event time manner, which removes the requirement of strict time synchronization. The convergence and optimality of the proposed algorithm are analyzed, and then verified by simulation examples. The developed results also show the tradeoff between communications and computation performance. [ABSTRACT FROM AUTHOR]

This paper focuses on developing new navigation algorithms for cooperative unmanned vehicles in costmap environments. The vehicles need to plan optimal paths subject to mutual diversity. We first propose three algorithms that can find optimal paths in costmap environments with improved computational efficiency. The proposed algorithms include 1) the scaled A*; 2) the A* plus-plus; and 3) the A* plus-plus with reconstruction. The heuristics of the three algorithms are proved admissible. Then, a diversity-based path planning algorithm is proposed based on the new A star variant algorithms for multiple vehicles to plan diverse paths for risk management. In particular, we propose an iterative segment planner, which generates a set of spatially diverse paths by iteratively planning segments of all paths in a sequential manner. To prevent the generation of spatially close paths, the iterative segment planner utilizes a penalty function to increase the cost of paths that lie near the route(s) assigned to other vehicles. By varying the penalty gains, different degrees of path diversity can be achieved. Some illustrative examples are provided to compare the performance of the proposed algorithms with the standard A* and Dijkstra's algorithms, and show the effectiveness of the proposed diversity-based path planning algorithm. [ABSTRACT FROM AUTHOR]

Temperature rise of an induction machine increases losses and decrease in the efficiency. The temperature rise depends on the machine design. It takes several hours after starting a machine to reach to thermal stability. In most of the in situ efficiency estimation methods, it is required to obtain the operating data of the machine at a thermally stable condition, which needs a long running time of the machine. In this paper, a method based on a particle swarm optimization (PSO) algorithm is proposed, which can estimate the machine efficiency at different loads with thermal stability. The machine operation data at the first 30 min after the start rather than data at a thermal stability condition are used in the method. The proposed algorithm utilizes two approaches to predict a full-load temperature at a thermally stable condition. The first approach is based on the insulation class of the machine and uses the equivalent circuit. The second approach is based on the trend of the temperature rise in the first 30 min of running the machine after the start. Furthermore, a method is proposed to narrow the parameters range, which helps the PSO to converge to the right answer. All results are validated by the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, an optimal design for nuclear magnetic resonance and magnetic resonance imaging based multiphase flow meter is presented. The overall apparatus consists of an electromagnetic coil surrounded by 12 rings of Halbach arrays of 12 magnets each. Both the coil and the magnets arrays were simultaneously and optimally designed using a three-dimensional (3-D) finite-element method (FEM) package and particle swarm optimization algorithm. The aim of the design is to uniformly prepolarize the hydrogen spins of the atoms composing the flowing fluid, with a predefined static magnetic field, before they enter into the measurement area within which they are homogenously polarized with a perpendicular ac magnetic field. Cheap and lightweight hardware are the other two design parameters of the design. Results of extensive 3-D simulations of the design indicate that an optimized and homogenous static magnetic field distribution could be achieved within an area of 40 mm diameter and 606 mm length. In addition, using a multiturn coil of 6 cm diameter and 2000 turns, an ac magnetic field of 13 mT amplitude and 112 ppm homogeneity could be achieved, which is enough to handle fluids flowing at speed of up to 2 m/s. Experimental validation, which was done using newly constructed two Halbach arrays of cuboid and trapezoid magnet elements, respectively, indicates a good match with FEM simulations. [ABSTRACT FROM AUTHOR]

As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications. [ABSTRACT FROM AUTHOR]

Soft robots may potentially be used for human services and complex environmental locomotion. The bodies of the soft robots are made from soft materials to make them safe for human interactions and to enable them to deform enough to adapt to soft or rough terrains. However, their practical applications are limited because their body stiffness is too low and the deformation is continuous, which make it difficult to control their movements. In this paper, the design and implementation of a soft robotic arm driven by shape memory alloy (SMA) coils are reported. The arm is made from soft silicone, and there are three linear Hall sensors in certain slots on the body wall to measure the changes in height as the arm bends. With a compression compensation algorithm, a proportional-integral differential controller is used to precisely control the bending movement. This soft robotic arm can follow a two-dimensional motion with a relatively high accuracy; indeed, it displays quite a remarkable performance considering its low cost, simple structure, easy control, and small bending error. Since this soft arm can be used as the basic driving unit of a soft robot capable of manipulating and locomoting, it has shed light on the applications of SMA soft robots. [ABSTRACT FROM AUTHOR]

This paper proposes a light, flexible, and yet reliable multiparametric estimation algorithm for grid-connected voltage-source-converter-based systems. This approach combines the nonlinear least squares (NLSQ) fitting algorithm and the steady-state extended harmonic domain (EHD) model to take into account the harmonic effects. The EHD model is used to take advantage of the harmonic content of the electrical signals to provide robustness and improved performance to the NLSQ estimation methodology. In addition, this harmonic formulation reduces the necessity of synchronized measurements. Details of the implementation are provided by an experimental case study, in which the grid equivalent (Thevenin voltage, inductance, and resistance), the ac-side filter (inductance and resistance), and the converter switching and conduction loss resistance are accurately estimated for three different operating conditions. [ABSTRACT FROM AUTHOR]

The efficiency, durability, and health of the energy storage system components depend on controlling the power flow subjected to proper operational constraints. It is achieved by energy management algorithm (EMA). Conventional EMAs restrict the ultracapacitor (UC) operation to a reference voltage to prevent it from overcharging/undercharging. This leads to very narrow utilization of the UC voltage range. However, UC voltage can safely be varied from zero to maximum-rated voltage. Rather than employing a UC voltage control loop that operates concurrently to transient load demand, a time-share-based approach has been used for UC charging/discharging. Hence, EMA has been modified in the present work by utilizing the UC voltage band instead of a UC reference voltage, which increases its power delivery capacity by approximately two to four times. In this paper, the proposed EMA improves the transient performance of the system and reduces the UC size. An experimental prototype of the system is designed, and the proposed EMA is tested in the different operating regions for validation. [ABSTRACT FROM AUTHOR]

In a conventional finite control set model predictive control (FCS-MPC) formulation, active and reactive power control loops rely on the predictive controller, whereas the dc-bus voltage is usually governed by a PI-based control loop. This originates from fact that the dynamic equations for describing the predictions of these variables are heavily coupled. In this paper, a cascade-free FCS-MPC for single-phase grid-connected power converters is presented. The proposed control algorithm is formulated in terms of established dynamic references design, which was originally proposed to directly govern active and reactive power, and dc-voltage in three-phase power converters. In this paper, the dynamic reference design concept is extended to control single-phase grid-connected power converters. The proposed control algorithm does not use instantaneous ac-power calculations; instead, it directly formulates the optimal control problem on the grid-current in the original stationary reference frame. The experimental results obtained with a single-phase grid-connected neutral point clamped (NPC) converter confirm a successful design, where system constraints, e.g., maximum power and weighted switching frequency, are easily taken into account. [ABSTRACT FROM PUBLISHER]

This paper formulates an optimization-based algorithm for the compensation of unwanted current terms by means of distributed electronic power converters, such as active power filters and grid-connected inverters. The compensation goal consists in achieving suitable load conformity factors, defined at the source side and within a feasible power region in terms of the power converter capability. Based on the measured load quantities and on a certain objective function, the algorithm tracks the expected source currents, which are thereupon used to calculate proper scaling coefficients and, therefore, the compensation current references. It improves the power quality at the point of common coupling and enables full exploitation of distributed energy resources, increasing their efficiency. The compensation is based on a decoupled current decomposition and on an optimization-based algorithm. In this paper, the strategy is applied to nonlinear and unbalanced three-phase four-wire circuit, under nonsinusoidal and asymmetrical voltage conditions. The steady-state and dynamic behaviors have been analyzed by theoretical, simulation, and experimental results. Furthermore, the proposed approach is also compared to other compensation strategies showing its effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents predictive current-mode control for a single-phase high-frequency transformer-isolated dual-active bridge dc-to-dc converter. The predictive control algorithm increases the bandwidth of the current loop of the converter which enables tracking of the current reference within one switching cycle. The paper further demonstrates that the application of the predictive control algorithm can remove transient dc offset from the current in high-frequency isolation transformer within one switching cycle. Direct control of the converter current protects the transformer from saturation even at transient conditions. The control algorithm has been implemented on an experimental setup and transient tests have been performed to validate controller performance. Since the predictive control algorithm is dependent on the measured value of the leakage inductance of the transformer, a compensator has been implemented to improve the parameter insensitivity of the proposed controller. [ABSTRACT FROM PUBLISHER]

The fourteen papers in this special section focus on the design and control of linear machines (LEMs). The subjects of these papers embrace the main subjects of interest of the scientific community related to LEMs, ranging from innovative procedures for the optimal design of such machines, to their analysis with finite elements, to advanced control techniques, suitably devised for specific applications. [ABSTRACT FROM AUTHOR]