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This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

Unavoidably, the controlsystem of any permanent magnet synchronous motor (PMSM) is subjected to perturbations, e.g., motor nonidealities and inverter nonlinearities. To reject these perturbations, a new super-twisting (ST) control variant, called speed adaptive super-twisting (S-AST) control, is proposed in this paper. In the S-AST control, a proportional-integral (PI) and a ST controllers are merged using an adaptive law that is based on the motor speed measurement. This is done to reduce the chattering problems that the standard ST control presents. In addition, a mathematical design for the S-AST control is proposed, where the relationship between the chattering amplitude, the switching frequency and the system perturbations is shown. The main contribution in this paper is the application of the S-AST controller to reduce the total harmonic distortion (THDi) of the PMSM currents. By comparing the S-AST control against PI control, the THDi is reduced from 9.9% to 1.27%, in simulation, and the THDi is reduced from 22.79% to 1.56%, experimentally. These results demonstrate how the S-AST control is an excellent alternative to improve the waveform of the PMSM currents. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the sensorless voltage control schemes for the promising wound-rotor brushless doubly-fed induction generator (BDFIG) in both the stand-alone and grid-connected systems. For sensorless target, a new rotor position observer using the space vector flux relations of BDFIG is suggested. The proposed sensorless algorithm does not require any additional observers for obtaining the generator speed by directly detecting the rotor position, which may reduce the cost of the overall control system. For the voltage control purpose, a direct voltage control (DVC) scheme is applied for the stand-alone systems. However, this control strategy did not handle the control of the generated-voltage phase angle and also did not consider the phase sequence issue which are very essential provisions for the smooth connection target in grid-connected applications. Hence, DVC strategy cannot guarantee all the intended provisions for a soft and smooth connection process with the grid side and therefore, an effective grid-synchronization algorithm is proposed. A comprehensive analysis to confirm the functionality of the proposed control strategies is discussed through both simulation and experimental results. The presented study in this paper verifies the efficacy of the proposed schemes for sensorless voltage control target of BDFIG in both the stand-alone and grid-connected applications with a good transient behavior. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch. [ABSTRACT FROM AUTHOR]

This paper presents a simple pulse width modulation algorithm for control of an ultracapacitor based energy storage system used for source and load support applications. As opposed to dual loop control philosophy that is widely used for backup applications, this paper presents a simple single loop current control that is only useful for controlling the ultracapacitor based DC/DC converters used in source and load support applications. The proposed PWM blocking method necessitates the DC/DC converter to operate in five states and takes into consideration the essential operational limits associated with the ultracapacitors. The performance of the proposed algorithm is verified experimentally using a 24 V laboratory prototype setup. Further, the shortcomings of dual loop control when applied to source and load support applications are demonstrated. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction is an important way to improve the durability and reduce the cost of the proton exchange membrane fuel cell. This paper presents a novel method to predict the remaining useful life of proton exchange membrane fuel cell under different load currents based on support vector regression and grey wolf optimizer algorithm. The proposed method considers the influence of 17 operating conditions and historical voltage. Firstly, the measured data are reconstructed through robust locally weighted smoothing method to reduce the calculation amount and filter disturbances. Then, support vector regression with fewer hyperparameters is used to establish the degradation model. Finally, the hyperparameters of support vector regression are optimized through grey wolf optimizer algorithm to improve the accuracy of degradation prediction. The proposed method is validated by two degradation experiments under different load currents. The test results show that grey wolf optimizer algorithm can effectively improve the accuracy of degradation prediction based on support vector regression. Compared with other methods, the proposed method has the highest accuracy. The proposed method can predict the fuel cell degradation with a mean absolute percentage error of less than 0.3%. The proposed method can predict the remaining useful life of 492 hours. [ABSTRACT FROM AUTHOR]

–This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme. [ABSTRACT FROM AUTHOR]

Conventional model predictive control (MPC) for three-level active neutral point clamped converter (ANPC) utilizes weighting factors to achieve the optimal control of multiple control objectives in a single cost function. However, the selection of these weighting factors has an impact on the performance of the controlled objectives significantly. Tuning of weighting factors is usually tedious and lacks theoretical background. Moreover, the predictive and evaluation process for multiple objectives optimization have to be carried out by enumerating all admissible switching states within one loop. To simplify the complexity of this process and eliminate the effect of weighting factors, this paper proposes a cascaded predictive control scheme for a 3L-ANPC inverter fed induction machine (IM) drive. With the proposed approach, three separate cost functions for three control objectives, stator flux vector, neutral point voltage and device loss distribution are evaluated in a cascaded way, so that the optimization of these control objectives is performed independently. A 3L-ANPC inverter driven IM drive is studied in this paper to verify the effectiveness of the proposed algorithm. Experimental results in different operating conditions confirm that the proposed method achieves satisfactory steady-state and transient performances comparable to the conventional predictive control approach. The weighting factors used in the conventional method are eliminated and the computational effort is reduced by 68% compared to the conventional method without any sacrifice in the drive performance. [ABSTRACT FROM AUTHOR]

Load-commutated inverters (LCI's) are often used to supply dual-three-phase synchronous motors in high-power variable-speed applications. A pair of LCIs is used in this arrangement to feed the two motor three-phase winding sets. In order to cope with inter-harmonic issues, a drive configuration with an interconnection of the two LCI dc-links has been proposed. In this paper, such a drive design is shown to produce an increased voltage stress on motor windings compared with traditional configurations. The problem is investigated in the paper by proposing an algebraic algorithm capable of predicting the steady-state voltage waveform applied to the motor terminals and arising between the star points of the two winding sets. Unlike conventional dynamic simulations, the proposed approach gives practically instantaneous results, making it possible to quickly investigate a wide number of possible operating conditions. Furthermore, it requires a limited knowledge of system parameters, which are often hardly available. Its reliability and accuracy are assessed by comparison with measurements on a test drive system and examples are given of the method application to the sizing of motor insulation system. [ABSTRACT FROM PUBLISHER]

This paper proposes an enhanced particle swarm optimization (EPSO)-based support vector classifier (SVC) that extracts the support vector from databases, in order to diagnose vibration faults in steam turbine-generator sets (STGS). SVC has been successfully applied to the classification of data with linear or nonlinear features, because it allows generalization. However, the design of the best SVC model for the acquisition of the best hyperplane is often difficult and depends heavily on the operators’ experience or on trial-and-error experiments. In this paper, an EPSO algorithm is used to automatically tune the control parameters of an SVC. Since EPSO is an excellent optimization tool, it is easily sufficient for the design of an optimal SVC model. The proposed approach is applied to an STGS, to test its diagnostic accuracy. The test results demonstrate that the proposed EPSO-based SVC method has a higher diagnostic accuracy and a shorter learning time than classical neural network-based methods. This study also provides advice on handling a loss of data features for unknown reasons. [ABSTRACT FROM AUTHOR]

To solve learning problems with vast number of inputs, this paper proposes a novel learning structure merging a number of small fuzzy neural networks (FNNs) into a hierarchical learning structure called a merged-FNN. In this paper, the merged-FNN is proved to be a universal approximator. This computing approach uses a fusion of FNNs using B-spline membership functions (BMFs) with a reduced-form genetic algorithm (RGA). RGA is employed to tune all free parameters of the merged-FNN, including both the control points of the BMFs and the weights of the small FNNs. The merged-FNN can approximate a continuous nonlinear function to any desired degree of accuracy. For a practical application, a battery state-of-charge (BSOC) estimator, which is a twelve input, one output system, in a lithium-ion battery string is proposed to verify the effectiveness of the merged-FNN. From experimental results, the learning ability of the newly proposed merged-FNN with RGA is superior to that of the traditional neural networks with back-propagation learning. [ABSTRACT FROM AUTHOR]

This paper presents a new single-source three-phase seven-level inverter with an inherent three-time voltage boosting capability for medium-voltage applications. The proposed topology is comprised of series connection of two switched-capacitor (SC) networks with an added half-bridge module per phase. Each of such integrated SC networks requires a single capacitor associated with three active power switches. The three-time voltage boosting feature of the proposed topology can reduce the dc-link voltage requirements by 50% in comparison to the traditional neutral point clamped (NPC), flying capacitors, active NPC (ANPC), hybrid clamped ANPC, and cascaded H-bridge topologies, and 75% to advanced ANPC topologies. It can also reduce the number of required switches and capacitors as well as their voltage stresses compared to those state-of-the-art topologies reported in the literature. By integrating “n” number of added SC networks in the proposed seven-level basic topology, the number of output voltage levels can be extended to 2n+3 per phase. A finite control set model predictive control algorithm is also derived to control the converter in both the active and reactive power exchanges modes without distorting the generated grid current quality. The capacitor voltage balancing is inherent in the proposed topology, and thus, there is no need for any additional voltage balancing circuit, which further reduces the control complexity. The performance of the proposed topology and its control algorithm are validated by several measurement results. [ABSTRACT FROM AUTHOR]

This paper proposes a direct speed and flux control system for Dual Stator Winding Induction Machines (DSWIMs) based on Super Twisting Sliding Mode Control (STSMC). The proposed nonlinear controller which has finite time convergence of error to zero, is designed such that the Lyapunov stability condition is satisfied. Furthermore, a novel torque-sharing algorithm is introduced for DSWIMs. This algorithm enables them to operate in a wide speed region, including zero speed, such that the required electromagnetic torque is supplied by two winding sets based on their power ratings. In addition, a Sliding Mode Control (SMC) based full order observer is proposed for DSWIMs. This observer, which is able to estimate the winding fluxes, flux angles and rotor speed with high-accuracy, guarantees the optimal flux condition. The functionality of the proposed sensor-less DSWIM drive scheme is evaluated through some experimental tests performed on a 3.3 kW DSP-based DSWIM drive system. The experimental results confirm the effectiveness of the proposed control method, torque sharing algorithm and the full order observer in various speed regions. [ABSTRACT FROM AUTHOR]

Conventionally, the converter of switched reluctance motor (SRM) is fed by the uncontrollable diode bridge rectifier (DBR), which leads to a low grid-side power factor (PF) and high current total harmonic distortion (THD). In this paper, an alternative solution for the grid-connected high-speed SRM drive system with improved PF is proposed. In the proposed drive system, the three-level active front end (AFE) is connected in cascade with the midpoint converter for SRM operation. A centralized strategy, which controls the AFE and SRM together, is proposed to govern the motor speed and grid-side PF by regulating the real power and reactive power of the system, respectively. Specifically, the real power, reactive power, and the voltage balancing of split capacitors are controlled by the model predictive directed power control (MP-DPC) algorithm, which significantly reduces the control complexity and guarantees the fast dynamic response. Consequently, satisfying speed regulation, high PF, low current THD, and bi-directional power-transfer capability are achieved. An idea-proofed testbench is constructed in laboratory, and the applicability of the proposed drive system is verified by a series of experimental results. [ABSTRACT FROM AUTHOR]

This paper formulates a maximum power point tracking (MPPT) technique that accurately tracks the maximum power point of photovoltaic (PV), which undergoes simultaneous or independent sudden changes in load resistance and irradiance. The proposed algorithm is fundamentally divided into three parts; current and voltage perturbation algorithm (IPA/VPA), perturbation step-size reduction algorithm (PSSRA) and a deviation avoidance loop. The use of dual perturbation parameters using IPA and VPA ensures high tracking speed. PSSRA iteratively reduces the perturbation step-size of IPA and VPA and helps in reducing the power oscillations around MPP. Finally, a deviation avoidance loop is developed to detect a change in irradiance by examining the sign of the slope of the two power curves, namely P-V and P-I. The algorithm compares the per unit change in voltage and current of PV, to determine a simultaneous change in both irradiance and load resistance. The proposed algorithm is compared with two recently developed MPPT algorithms. The results show that the proposed technique can track the MPP with high speed and low steady-state oscillations and does not deviate from the MPP tracking path regardless of fast changes in irradiance and load resistance. [ABSTRACT FROM AUTHOR]

This paper presents a new open-circuit fault diagnosis algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault diagnosis algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault diagnosis algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault diagnosis algorithm is verified through the detailed simulation and experiment results. [ABSTRACT FROM AUTHOR]

Power generation systems (PGSs) based on hybrid renewable energy are one of the promising solutions for future distributed generation systems. Among different configurations, hybrid photovoltaic-wind turbine (PV-WT) grid connected PGSs are the most adopted for their good performance. However, due to the complexity of the system, the optimal balance between these two energy sources requires particular attention to achieve a good engineering solution. This paper deals with the optimal sizing of PV-WT by adopting different multicriteria decision analysis (MCDA) optimization approaches. Sensitivity of MCDA algorithms has been analyzed, by considering different weighting criteria techniques with different fluctuation scenarios of wind speed and solar radiation profiles, thus highlighting advantages and drawbacks of the proposed optimal sizing approaches. The following study could be assumed as a powerful roadmap for decision makers, analysts, and policy makers. [ABSTRACT FROM PUBLISHER]

Model validation of power plant equipment is expected to be mandated imminently by the North American Electric Reliability Corporation. These standards will result in a great need for performing model validation for a large fleet of existing and future power plants. The historic approach to model validation of synchronous generators has typically been a battery of offline tests. In addition, the process of validation depends on a trial and error series of simulations, comparing the results of such simulations with measured test data, to gradually tend toward a validated model and its parameters. Such a method presents significant challenges for application on a large fleet of units. This paper documents a practical approach to model validation. The method is based on collecting online data from the generating unit, typically near or at base-load. Data are collected either from digital-fault recorders capturing system disturbances, or from digital excitation system for an imposed small voltage-reference step. The data are then used in a software tool to perform model validation using an automated optimization algorithm-–the entire process may be said to be semiautomated. The benefits are explained in the paper through actual examples of the application. [ABSTRACT FROM PUBLISHER]

This paper presents a high-gain observer of the rotor position for the control of surface permanent magnet synchronous machines under loaded conditions, e.g., saturation. Determining the rotor flux position of permanent magnet AC machines under no load is simpler, since its flux and rotor position coincide. However under load and in the presence of saturation in particular, the flux is comprised of two components: one due to the rotor magnets and the other due to the stator current. This causes the magnetic axis to shift from the rotor position, which in turn causes an error in a control algorithm that uses conventional rotor position estimation. This paper characterizes this error through a nonlinear model of the machine and finite element analysis. Design and analysis of a general position observer algorithm for PMAC machines accounting for saturation is presented, along with its implementation for a control methods using back EMF measurement and high-frequency Injection. Experimental results validating the nonlinear model of the machine, existence of the saturation-induced error, and performance of the proposed observer are also given. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries have emerged as the major power source for today's electronic products because they can offer advantages such as high energy density, low maintenance requirement, high open-circuit voltage, low self-discharge rate, and lack of memory effect. The performance and lifespan of Li-ion batteries are closely related to the quality of the charging pattern. Therefore, an optimal charging pattern is essential for Li-ion batteries to achieve shorter charging time and longer cycle life. In this paper, a Taguchi-based algorithm is presented. Consecutive orthogonal array techniques are utilized to determine the optimal charging pattern for the five-step constant current charging method. The problem formulation, implementation procedure, and parameter setting method are described and explained in detail. Experimental results show that the obtained charging pattern is capable of charging the Li-ion batteries to 95% capacity. Comparing to the conventional constant current–constant voltage method, the benefits of the obtained pattern are faster and safer charging and longer battery cycle life. [ABSTRACT FROM AUTHOR]

The concept of anticipatory control applied to wind turbines is presented. Anticipatory control is based on the model predictive control (MPC) approach. Unlike the MPC method, non-controllable variables (such as wind speed) are directly considered in the dynamic equations presented in the paper to predict response variables, e.g., rotor speed and turbine power output. To determine future states of the power drive with the dynamic equations, a time series model was built for wind speed. The time series model was fused with the dynamic equations to predict the response variables over a certain prediction horizon. Based on these predictions, an optimization model was solved to find the optimal control settings to improve the power output without incurring large rotor speed changes. As both the dynamic equations and time series model were built by data mining algorithms, no gradient information is available. A modified evolutionary strategy algorithm was used to solve a nonlinear constrained optimization problem. The proposed approach has been tested on the data collected from a 1.5 MW wind turbine. [ABSTRACT FROM AUTHOR]

The penetration level of wind energy is continuously growing, and it is especially relevant in European countries such as Denmark, Germany, and Spain. For this reason, grid codes in different countries have been recently revised, or are now under revision in order to integrate this energy in the network taking into account the security of supply. This paper is related to reactive compensation, which is one aspect usually included in these codes. On the other hand, a great number of installed wind parks are formed by fixed speed wind turbines equipped with induction generators. The typical scheme for reactive compensation in this kind of wind parks is based on capacitor banks locally controlled in each machine. This configuration makes very difficult to follow the requirements of the new grid codes. To overcome this problem, a configuration with a central controller that coordinates the actuation over all the capacitor steps in the wind park is proposed in this paper. A central controller algorithm that is based on a dynamic programming is presented and evaluated by means of simulation. At this time, the proposed scheme has been installed at the Sotavento Experimental Wind Park (Spain) and it is currently being tested. [ABSTRACT FROM AUTHOR]

This paper presents new solution algorithms based on an evolutionary particle swarm optimization (EPSO) for solving the pumped-storage (P/S) scheduling problem. The proposed EPSO approach combines a basic particle swarm optimization (PSO) with binary encoding/decoding techniques as well as a mutation operation. The binary encoding/decoding techniques are adopted to model the discrete characteristics of a P/S plant. The mutation operation is applied to accelerate convergence and escape local optimums. The optimal generation schedules for both P/S and thermal units are concurrently obtained within the evolutionary process of a scoring function. Therefore, hydrothermal iteration is no longer needed. The proposed approach is applied with great success to an actual utility system consisting of four P/S units and 34 thermal units. Experimental results indicate the attractive properties of the EPSO approach in a practical application, namely, a highly optimal solution and robust convergence behavior. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for the solution of nonlinear optimal scheduling problems. This algorithm is called ‘multipass iteration particle swarm optimization’ (MIPSO). A new index called ‘iteration best’ is incorporated into ‘particle swarm optimization’ (PSO) to improve solution quality. The concept of multipass dynamic programming is applied to further modify the PSO to improve computation efficiency. The MIPSO algorithm is used to solve the optimal operating schedule of a battery energy storage system (BESS) for an industrial time-of-use (TOU) rate user with wind turbine generators (WTGs). The effects of wind speed uncertainty and load are considered in this paper, and the resulting optimal operating schedule of the BESS reaches the minimum electricity charge of TOU rates users with WTGs. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms. [ABSTRACT FROM AUTHOR]

A maximum power point tracking algorithm for single-stage converters connecting photovoltaic (PV) panels to a single-phase grid is presented in this paper. The algorithm is based on the application of the "ripple correlation control" using as perturbation signals the current and voltage low-frequency oscillations introduced in the PV panels by the single-phase utility grid. The proposed control technique allows the generation of sinusoidal grid currents with unity power factor. The algorithm has been developed to allow an array of PV modules to be connected to the grid by using a single-stage converter. This simple structure yields higher efficiency and reliability when compared with standard solutions based on double-stage converter configurations. The proposed maximum power point tracking algorithm has been numerically simulated and experimentally verified by means of a converter prototype connected to a single-phase grid. The results are presented in the paper, showing the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

This paper presents the calculation of the optimum installation angle for the fixed solar-cell panels based on the genetic algorithm. (GA) and the simulated-annealing (SA) method. The output power of the solar-cell panel is highly affected by the sunlight Incident angle and its efficiency can be improved if the solar-cell panel is properly installed with the optimum angle. The relationship between the sunlight incident angle and the sunlight radiation Intensity on the solar-cell panel surface is also presented in this paper. Both GA and SA with climatic data are utilized to calculate the optimum installation angle of the solar-cell panel for different locations in Taiwan. The best monthly and annual installation angles obtained by computer simulations are presented. Hardware experimental results indicate that the actual best monthly installation angles are very close to the computer simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (P80) algorithm is presented. This paper demonstrated in detail how to employ the P80 method to search efficiently the optimal PIP controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PB) controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed P80-PB) controller, a new time-domain performance criterion function was also defined, compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system. [ABSTRACT FROM AUTHOR]

Multiple-voltage-region control, in which the bus-voltage range is divided into several regions, is usually implemented for dc microgrid operation in distributed manner. Voltage/power droop relationships are imposed for active power sharing among slack terminals. Conventionally, threshold voltages for voltage region partition are determined with fixed percentage of variation around the nominal value, which may result in unevenness of droop coefficients in different regions. If system droop coefficient is too high, significant bus-voltage step change due to load variation will occur. On the other hand, significant power sharing error among slack terminals will be induced if the droop coefficient is too low. In this paper, a compromised solution with power-capacity-based bus-voltage region partition is proposed to equalize the droop coefficients in different regions. However, the droop coefficients are determined based on the rated power capacity of system units. Bus-voltage discontinuity appears when the power capacity reduces in actual implementation. To eliminate the voltage discontinuity, online droop coefficient tuning according to the real-time power capacity is implemented. Algorithms for local power capacity estimation of solar photovoltaic (PV) and battery energy storage have been proposed. A lab-scale dc microgrid has been developed for verification of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural network (NN)-based vector control method for a three-phase induction motor. The proposed NN vector control utilizes the rotor flux-oriented reference frame, and the role of the NN controller is to substitute the two decoupled current-loop proportional-integral (PI) controllers in conventional vector control techniques. The objective of NN training is to approximate optimal control and the NN controller was trained by Levenberg–Marquardt (LM) algorithm. Forward Accumulation Through Time algorithm for induction motor was developed to calculate Jacobian matrix needed by the LM algorithm. The simulations showed that the NN vector control can provide better current tracking ability than the conventional vector control, such as less oscillations and low harmonics. Especially, the NN vector control can better overcome the problem of detuning effects than the conventional vector control. The hardware experiments further demonstrated the great advantage of the NN vector control. The NN vector control can succeed in driving the induction motor without audible noise using relatively lower switching frequency or lower sampling rate compared with the conventional vector control, and thus has the potential to improve efficiency and reduce size and cost of an induction motor drive system. [ABSTRACT FROM AUTHOR]

This paper focuses the analysis around a grid-connected converter that operates under unbalanced voltage and current conditions. Two different scenarios have been selected to validate the proposed algorithms. In the first scenario, the converter operates as a reactive power compensator, exchanging reactive power with the grid, and minimizing the dc bus voltage oscillations. The second scenario is more challenging, since it operates as a load balancer. In this application, the unbalanced current consumption of the load is compensated by the converter, exchanging positive and negative sequence currents with the grid. Another objective in this second scenario is to compensate the reactive power of the unbalanced load. It is important to highlight that in both scenarios, any maximum limit of the converter can be exceeded due to the specific unbalanced voltage and current conditions. Three variables are considered critical for the converter: 1) output ac current limit; 2) output ac voltage limit; and 3) dc bus voltage oscillation limit. Thus, this paper proposes a control method, which limits online the reactive power reference in the first scenario and the exchanged current references in the second scenario in order not to exceed any of the mentioned critical variables. The corresponding experimental results are shown in Part II of this paper so as to validate the limitation algorithm obtained by means of the mathematical analysis carried out in Part I. [ABSTRACT FROM AUTHOR]

The perturb and observe (P&O) algorithm is one of the most commonly utilized maximum power point tracking (MPPT) control schemes for photovoltaic (PV) generators. However, the operation of this algorithm at high perturbation frequencies, when the system response to MPPT perturbations is never allowed to settle, has not been given adequate attention in the literature. This paper characterizes system behavior in this mode of operation for standalone PV systems feeding resistive loads and motor-pump loads. Simulation and experimental results show that the P&O algorithm operating at a high perturbation frequency may offer higher energy utilization efficiency and better system performance, despite the resulting nonperiodic waveforms of the system. [ABSTRACT FROM AUTHOR]

In this paper, we study the identification of acoustic noise spectra in induction motors by using a recently developed frequency-domain cross-power spectrum estimation algorithm. This algorithm is a noniterative high-resolution spectral estimator. In a test rig, from multiple experiments sound data are collected by an array of five-microphones placed hemispherically around motors in a reverberant and noisy room. In order to explore the issue of assembly micromisalignments, each motor is removed from the test rig and then replaced, after which the experiment is then repeated. The identification algorithm is used to detect changes in acoustic noise spectra of induction motors due to mechanical and electrical faults most frequently encountered in industry. Not only the autopower spectra of the individual microphones, but also the cross-power spectra of the microphone pairs are estimated. As a byproduct, it is demonstrated that one microphone is sufficient to identify noise spectra. The estimated acoustic spectra, or more compactly statistics extracted from them, can be used in the development of preventive maintenance programs for induction motors in service. [ABSTRACT FROM AUTHOR]

The traditional stator single-phase-to-ground fault protective schemes are hard to completely satisfy the requirement of selectivity for bus-connected Powerformers. In order to overcome the aforementioned problem, a new stator single-phase-to-ground fault protective scheme based on the hierarchical clustering algorithm is proposed in this paper. The direction and magnitude of zero-sequence current and leakage current are discussed and selected as feature vectors. Then, the data set are divided into two groups by hierarchical clustering algorithm, and cluster center of each group is calculated. Finally, the space relative distance among detected pattern and two cluster centers is calculated to discriminate the faulty Powerformer. The fundamental component and the third harmonic component of the data set are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 kΩ. [ABSTRACT FROM PUBLISHER]

Inductors are a significant contributor to mass in many power applications including the dc link in high-power drives and in dc–dc power conversion equipment. Permanent magnet inductors (PMIs) may be used in place of traditional inductors in dc biased applications with significant (40%) mass savings. This paper sets forth a rigorous, systematic, and multifaceted design paradigm for a PMI, using a buck converter application as an example. The algorithm includes electrical, magnetic, and thermal analysis and minimizes overall converter mass and loss. The algorithm is demonstrated in the design and testing of a 4 kW laboratory scale dc–dc converter. [ABSTRACT FROM PUBLISHER]

In the single-phase photovoltaic (PV) grid-connected distributed generation (DG) system located at the end of the feeder, it is necessary to provide quickly active and reactive power to the local loads. In this paper, an improved reactive current detection and power control method are proposed to realize active power control and reactive power compensation. To detect quickly the loads reactive current, a fast reactive current detection method using the derivative and ip\!\!-\!\!iq algorithm is presented, and it can overcome the long delay in a conventional phase-shift method. In the inner current loop, the quasi-proportional-resonant (QPR) control method with grid voltage feed-forward is presented to control accurately the grid current with zero steady error, and to reduce the current distortion due to the frequency offset and distortion of the grid voltage. The inner loop stability is analyzed, and the proper parameters are selected. In the outer loop, proportional–integrator (PI) controller is applied to stabilize the dc-link voltage, and power feed-forward is introduced to speed up system response. Simulation and experimental results verified the validity of the proposed control method. [ABSTRACT FROM PUBLISHER]

An algorithm is developed for multimodal design optimization of a V-shaped magnet interior permanent magnet synchronous machine (IPMSM). This algorithm is capable of finding all the local and global minima of a given single- or multi-objective function. The paper demonstrates the algorithm to minimize the total active weight of an IPMSM while minimizing its losses considering mechanical, thermal, and magnetic constraints. Predicted operating characteristics of a preliminary, nonoptimized IPMSM are compared with the global and local optimal solutions obtained. The proposed algorithm provides the designer with a complete view of alternative optimal machine designs from which a suitable design may be selected. The close agreement between the simulation and experimental results for the globally optimized IPMSM design verifies the accuracy of the optimization approach. [ABSTRACT FROM AUTHOR]

An electric machine equipped with n stator phases is characterized by a set of n \times n phase inductances as concerns the stator section. In the case of nonuniform air gap, these inductances vary as the rotor moves. For their numerical determination as functions of the rotor position an accurate computation algorithm is proposed in this paper based on winding function theory. For permeance function identification, a numerically efficient method is employed based on the magnetostatic finite-element analysis of only three appropriately simplified machine models. In the presence of a field circuit, computation of mutual inductances among it and stator phases is also covered with the same approach. An extension of the method to permanent-magnet machines is also presented. Results are assessed on a six-phase synchronous generator prototype equipped with either a salient-pole wound-field rotor or with an interior permanent-magnet rotor. [ABSTRACT FROM AUTHOR]

Online magnetic flux monitoring via permanently installed air-gap flux probes is a well-established technology to determine the presence of shorted turns in a turbine generator rotor winding. Flux measurements are normally performed using flux probes installed in the machine air gap (on a stator wedge) and connected to portable or permanently installed instruments. In this “flux probe” test, to achieve a reliable diagnostic, the signal from the flux probe has to be measured under different load conditions ranging from no load to full load. This requirement presents a serious obstacle when applying this method on base load units. Recently, a new design of magnetic flux probe installed on the stator tooth was implemented. In addition, new algorithms used to analyze measurements using different types of flux probes were developed to minimize the need for tests at different load conditions. A finite element model (FEM) has been created to verify these new algorithms in different loading conditions. Based on this model, and real-world measurements, it has been demonstrated that accurate detection of shorted turns can be obtained without the need to vary the load on the machine if suitable sensors and algorithms are applied. This paper describes the new method and its advantages, comparing results obtained from online measurements on working generators and the FEM created to simulate different rotor conditions. [ABSTRACT FROM AUTHOR]

The novel scenario of power systems with massive utilization of distributed generation (DG) imposes that islanding detection methods for voltage source inverter (VSI)-interfaced DGs must be evaluated taking into account multi-inverter configurations. This paper presents the experimental validation of an active islanding detection algorithm for multi-inverter systems proposed by the authors in previous work. Field programmable gate array implementation of power control and islanding detection algorithms has been used to test this proposition in a multi-inverter system based on grid-connected insulated gate bipolar transistor VSIs. The proposed islanding detection algorithm offers a very low impact on power quality and rapid and efficient islanding confirmation which is demonstrated by simulation and experimentation. The performance of the algorithm was tested under critical conditions as resonant load with high quality factor and unity power factor and also for cases where load and VSI powers are matched. [ABSTRACT FROM PUBLISHER]

This paper presents a technique for noise and vibration reduction in low-speed sensorless control algorithm of surface mounted permanent magnet synchronous machines (SM-PMSM). The signal processing involved in high-frequency-based sensorless control algorithms has a significant effect on the vibration and noise accompanying such algorithms. Therefore, a wavelet filtering approach is proposed. This wavelet filter is capable of extracting the position information even at low values of injection voltage amplitude where conventional filters fail to extract the information. This leads to a reduction in the machine's noise and vibration. Furthermore, an optimization using hardware-in-the-loop method based on genetic algorithms was implemented in real time to obtain the optimum value of the injected voltages amplitude and frequency causing the least possible vibration with acceptable position estimation accuracy. Consequently, a significant reduction in vibration and noise was achieved. The proposed techniques were implemented on a 2-hp SM-PMSM motor both experimentally and through simulations. Comparisons between numerical and test results confirm the validity of the proposed techniques. [ABSTRACT FROM PUBLISHER]

This paper presents a robust natural-frame-based interfacing scheme for grid-connected distributed generation inverters. The control scheme consists of a dead-beat line-voltage sensorless natural-frame current controller, adaptive neural network (NN)-based disturbance estimator, and robust sensorless synchronization loop. The estimated uncertainty dynamics provide the necessary energy shaping in the inverter control voltage to attenuate grid-voltage disturbances and other voltage disturbances caused by interfacing parameter variation. In addition, the predictive nature of the estimator has the necessary phase advance to compensate for system delays. The self-learning feature of the NN adaptation algorithm allows feasible and easy adaptation design at different grid disturbances and operating conditions. The fact that converter synchronization is based on the fundamental grid-voltage facilitates the use of the estimated uncertainty to extract the position of the fundamental grid-voltage vector without using voltage sensors. Theoretical analysis and comparative evaluation results are presented to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

This paper presents a comparison of different existing and proposed wave prediction models applicable to control wave energy converters (WECs) in irregular waves. The objective of the control is to increase the energy conversion. The power absorbed by a WEC is depending on the implemented control strategy. Uncertainties in the physical description of the system as well as in the input from irregular waves provide challenges to the control algorithm. We present a hybrid robust fuzzy logic (FL) controller that addresses the uncertainty in the model and the short-term tuning of the converter. The proposed robust controller uses the energy from the power take off as input, while the FL controller portion is used for short-term tuning. The FL controller action is based on a prediction of the incoming wave. Simulation results indicate that the proposed hybrid control yields improved energy production, while being robust to modeling errors. [ABSTRACT FROM AUTHOR]

Speed control during startup is one of the most critical tasks of gas turbine power plant operation. This paper introduces the PI fuzzy gain-scheduling (PI-FGS) controller to solve the foremost speed-control problems, including tracking of the acceleration pattern and rejection of disturbances caused by operation events throughout startup. Fundamentally, the PI-FGS synthesizes a GS controller from multiple locally tuned generalized proportional integral (PI) algorithms by means of a fuzzy system. The PI-FGS structure permits independent tuning of the tracking and rejection responses at any operating point. In addition, tuning can be carried out on-site based on the operator experience and inspection of the current plant response. Simulation experiments show that the PI-FGS may improve speed-control performance well over that provided by commercially available speed controllers, which are based on a single conventional PI algorithm. [ABSTRACT FROM PUBLISHER]

A new dead time compensation method for a pulsewidth modulation (PWM) inverter is proposed. In the PWM inverter, voltage distortion due to the dead time effects produces fifth and seventh harmonics in the phase currents of the stationary reference frame, and a sixth harmonic in the d- and q-axis currents of the synchronous reference frame, respectively. In this paper, the sixth harmonic of the integrator output of the synchronous d-axis proportional-integral (PI) current regulator is used to compensate the output voltage distortion due to the dead time effects, since the integrator output has ripple corresponding to six times the stator fundamental frequency. The proposed method can be easily implemented by feedforwardly adding compensation voltages to the output reference voltage of the synchronous PI current regulator. The proposed method, therefore, has some significant advantages such as simple implementation without additional hardware, easy mathematical computation, no offline experimental measurements, and application in both the steady state and the transient state. The validity of the proposed compensation algorithm is shown through several experiments. [ABSTRACT FROM AUTHOR]

This paper addresses a robust decentralized proportional-integral (P1) control design for power system load- frequency regulation with communication delays. In the proposed methodology, the P1-based load-frequency control (LFC) problem is reduced to a static output feedback control synthesis for a multiple-delay system. The proposed control method gives a suboptimal solution using a developed iterative linear matrix inequalities algorithm via the mixed H2 /H∞ control technique. The control strategy is suitable for LFC applications that usually employ the PI control. To demonstrate the efficiency of the proposed control strategy, an experimental study has been performed at the Research Laboratory, Kyushu Electric Power Company, Japan. [ABSTRACT FROM AUTHOR]

Because of their deployment in dispersed locations on the lowest voltage portions of the grid, photovoltaic (PV) systems pose unique challenges to power system engineers. Computer models that accurately simulate the relevant behavior of PV systems would thus be of high value. However, most of today's models either do not accurately model the dynamics of the maximum power point trackers (MPPTs) or anti-islanding algorithms, or they involve excessive computational overhead for this application. To address this need, a MATLAB/Simulink model of a single-phase grid-connected PV inverter has been developed and experimentally tested. The development of the PV array model, the integration of the MPPT with an averaged model of the power electronics, and the Simulink implementation are described. It is experimentally demonstrated that the model works well in predicting the general behaviors of single-phase grid-connected PV systems. This paper concludes with a discussion of the need for a full gradient-based MPPT model, as opposed to a commonly used simplified MPPT model. [ABSTRACT FROM AUTHOR]

The paper presents a multiple-input single-output fuzzy logic governor algorithm that can be used to improve the transient response of a diesel generating set, when supplying an islanded load. The proposed governor uses the traditional speed input in addition to voltage and power factor to modify the fueling requirements during various load disturbances. The use of fuzzy logic control allows the use of proportional-integral-derivative (PID) type structures that can provide variable gain strategies to account for nonlinearities in the system. Fuzzy logic also provides a means of processing other input information by linguistic reasoning and a logical control output to aid the governor action during transient disturbance. The test results were obtained using a 50 kVA naturally aspirated diesel generator testing facility. Both real and reactive load tests were conducted. The complex load test results demonstrate that, by using additional inputs to the governor algorithm, enhanced generator transient speed recovery response can be obtained. [ABSTRACT FROM AUTHOR]

The most critical attribute of a power control strategy for a fuel cell (FC) and ultracapacitor (UC) based hybrid vehicular power system is the sharing of load demand between available power sources. Transients and rapid changes of the power demand may cause drying of the FC membrane resulting in degradation of FC lifetime. The load demand profile of a hybrid electric vehicle (HEV) is a nonstationary fluctuating signal and consists of transients. Wavelet transforms are suitable for analyzing and evaluating such signals for dynamic systems. This paper focuses on the integration of a developed novel-wavelet-based load sharing algorithm and dynamic model of the FC/UC hybrid vehicular power system in order to ensure efficient power flow control strategy. [ABSTRACT FROM AUTHOR]

An availability index, Generation Ratio Availability (GRA), is proposed to evaluate the electrical system of offshore wind farms (OWF). The GRA is the probability that at least a certain percent of wind power could be transferred to the grid system through the concerned electrical system. The GRA does not depend on the load demand and has weaker correlation with the wind speed in comparison with other reliability indices, such as loss-of-load probability, expected energy not supplied, etc. On the basis of the particular characteristics of the OWFs, the algorithm to compute the GRA is proposed. Comprehensive studies have been conducted to investigate the influence of the network design, component parameters, and wind-speed regimes on the GRA. The analysis presented in this paper is useful for both future wind farm planning and existing OWF evaluation. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive self-tuning maximum torque per ampere (MTPA) vector controller for an interior permanent-magnet synchronous motor (IPMSM) drive system. The control scheme consists of a synchronous frame decoupling current controller, MTPA torque controller, and adaptive parameter estimator. The estimator is applied to the q-axis current dynamics as the d-axis inductance can be assumed to be constant without loss of accuracy. Since the q-axis current dynamics is being disturbed by the magnet's back-EMF voltage, the proposed estimator is combined with a robust active-state decoupling scheme to ensure unbiased parameter estimate. The robust decoupling scheme is realized by estimating the magnet's flux linkage by a simple adaptation algorithm based on the steepest descent method. The system's model is greatly simplified when the robust decoupling scheme is combined with the q-axis current dynamics. Relying on the simplified model, a natural adaptive observer is used to estimate the q-axis current. Unknown motor parameters are estimated by minimizing the state estimation error using an iterative gradient algorithm offered by the affine projection. The estimated parameters are used for the self-tuning control. Experimental results are presented to demonstrate the validity and usefulness of the online parameter estimation and control loop tuning technique. [ABSTRACT FROM AUTHOR]