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The performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors. [ABSTRACT FROM AUTHOR]

Domestic electric water heaters (DEWHs) can provide operational flexibility for load control due to their energy storage capacity. Load forecasting for aggregated DEWHs is important for providing information of baseline load and controlling electricity demand profile without negative impact to the normal end use. Advanced metering infrastructures nowadays provide more possibilities to further enhance forecasting with bottom-up method. This article proposes a bottom-up forecasting with Markov-based error reduction method to predict power consumption of aggregated DEWHs for multiple forecast horizons. DEWHs are randomly divided into small aggregations, whose power consumption is forecasted by independent forecast engines. In this paper, the engines are K-means and wavelet decomposition-based neural networks. After summing all forecasting of small aggregations up, a new Markov-based error reduction method is proposed to extract features in residuals and mitigate forecasting error accumulation introduced by the summation, providing opportunities to further improve forecasting accuracy for the total DEWH load. Differing from traditional Markov-based error reduction, two new compensation parameters (compensation coefficient, and compensation threshold) are proposed. They are determined by using particle swarm optimization algorithm. Experiments on real and simulated DEWH loads verified the effectiveness of the proposed forecasting method. The proposed method improved the forecast accuracy over selected benchmark algorithms by about 20% to 80%, according to four performance metrics: mean absolute error, mean absolute percentage error, root-mean-square error, normalized form RMSE. The aggregation effects on performance were also analyzed in theory and tested with simulated DEWHs, providing a good indication of the forecast dependence on the aggregation size. [ABSTRACT FROM AUTHOR]

In the modern scenario of smart-grids, the concept of virtual power plant (VPP) is undoubtedly a cornerstone for the smooth integration of renewable energy sources into existing energy systems with a high penetration level. A VPP is the aggregation of decentralized medium-scale power sources, including photovoltaic and wind power plants, combined heat and power units, as well as demand-responsive loads and storage systems, with a twofold objective. On one hand, VPP relieves the stability and dispatchability problems on the external smart grid since it can be operated on an individual basis, appearing as a single system on the whole. On the other hand, VPP improves flexibility coming from all the networked units and enable traders to enhance forecasting and trading programs of renewable energies. This paper proposes a novel distributed decentralized prediction method for the management of VPPs. The novelty of the proposed technique is to effectively combine the concepts of neural networks and machine learning with a distributed architecture that is suitable for the aggregation purposes of the VPP. [ABSTRACT FROM AUTHOR]

The growing interest in optimizing the generation, distribution, and delivery of electric power have motivated the implementation of several smart grid functions in many power systems around the globe. Among such functions are the peak-load management, demand response, direct load control, and integration of distributed power generation. Nowadays, smart grid functions are being implemented for industrial, residential, and/or commercial loads. One of the key requirements for implementing smart grid functions is the accurate and reliable load aggregation. The bottom-up, coordinated, and bus-split aggregation methods have been found applicable for different load types that are included in smart grid functions. This paper reviews the methods and approaches for performing the load aggregation, and provides a discussion for the critical role of load aggregation in power systems operating and smart grid functions. In addition, this paper discusses the applicability of the load aggregation methods in smart grid functions for residential loads. [ABSTRACT FROM PUBLISHER]

Variability in the solar irradiance level and ambient temperature of photovoltaic (PV) systems necessitates the use of maximum power point tracking (MPPT) of PV systems to ensure continuous harvesting of maximum power. This paper presents a sensorless current (SC) MPPT algorithm using model predictive control (MPC). The main contribution of this paper is the use of model-based predictive control principle to eliminate the current sensor that is usually required for well-known MPPT techniques such as perturb and observe (P&O). By predicting the PV system states in horizon of time, the proposed method becomes an elegant, embedded controller that allows faster response and lower power ripple in steady state than the conventional P&O technique under rapidly changing atmospheric conditions. This becomes possible without requiring expensive sensing and communications equipment and networks for direct measurement of solar irradiation changes. The performance of the proposed SC-MPC-MPPT with reduced load sensitivity is evaluated on the basis of industrial European Efficiency Test, EN 50530, that assesses the performance of PV systems under dynamic environmental conditions. The proposed control technique is implemented experimentally using dSPACE DS1007 platform to verify the simulation results. [ABSTRACT FROM PUBLISHER]

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under various fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications. [ABSTRACT FROM AUTHOR]

Bearing is a critical component widely used in rotary machines. Bearing failure can cause damages of other components and lead to a lengthy downtime of the machine and costly maintenance. To reduce the cost and downtime for maintenance of the machines, it is desirable to perform fault prognostics to enable predictive health management for bearings. This paper proposes a new data-driven approach for bearing fault prognostics, which is based on the Kolmogorov–Smirnov test, self-organizing map, and unscented Kalman filter (UKF). The proposed approach has two steps. The first step is to detect bearing's degradation process by learning the historical data and the second step is to predict the remaining useful life (RUL) with the aid of a degradation model and the UKF. The proposed approach is validated by bearing's life data obtained from a run-to-failure experiment. Results show that the proposed approach can detect the bearing degradation process successfully and predict the RUL effectively. [ABSTRACT FROM AUTHOR]

During the past one to two decades, the probabilistic forecasting of a wind-power generation has been regarded as a necessary input to decisions made for the purpose of reliable and economic power systems operations, especially since the penetration of the renewable energy has begun to grow rapidly. Probabilistic forecasting differs from traditional deterministic forecasting in which it takes uncertainty into account. This paper proposes a modified nonparametric method for constructing reliable prediction intervals (PIs). The lower upper bound estimation (LUBE) method is adapted to construct PIs for the wind-power generation, based on the ensemble wind-speed data from the numerical weather prediction system of the Central Weather Bureau of Taiwan. The charged system search (CSS) is used to adjust parameters in LUBE. The performance of the proposed method is examined using datasets from several wind farms in Taiwan. Simulation results demonstrate that the quality of PIs output by the proposed model significantly exceeded that of those constructed using the persistence model with a 1-h-ahead time horizon. [ABSTRACT FROM AUTHOR]

Probabilistic wind generation forecast results are crucial for power system operational dispatch. In this paper, a nonparametric approach for short-term probabilistic wind generation forecast based on the sparse Bayesian classification (SBC) and Dempster–Shafer theory (DST) is proposed. This approach is composed of the following four steps. 1) A spot forecast of wind generation is performed based on support vector machine (SVM). 2) The range of SVM forecast error is discretized into multiple intervals, and the conditional probability of each interval is estimated by a sparse Bayesian classifier. 3) DST is applied to combine the probabilities of all the intervals to form a unified probability distribution function (pdf) of the SVM forecast error. 4) The pdf of wind generation is achieved by combining the SVM wind generation spot forecast result and corresponding forecast error distribution. The distinguishing features of the proposed approach are as follows. 1) The approach is a nonparametric one and the forecast error caused by the misjudgement of probability distribution type can be avoided. 2) The proposed approach has good generalization capability by using the sparse learning mechanism. 3) The range constraint of wind generation can be systematically considered in the approach by applying DST. Tests on a 74-MW wind farm illustrate the improvement of spot forecast accuracy of the proposed approach and validate that this paper provides better calibrated and sharper probabilistic forecasts than the empirical approach and quantile regression model. Comparison to the state-of-the-art of the Global Energy Forecasting Competition 2014 demonstrates the probabilistic forecast performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The design and implementation of a robust multivariable model predictive control (MPC) on a quadruple tanks system (QTS) is addressed in this paper. Mismatch between the MPC’s model and the process may cause constraint violation, nonoptimized performance, and even instability. It is the objective of this paper to offset-free control the process in the presence of constraints and model mismatch. It is shown in this paper how this model mismatch is compensated by augmented state disturbances, and also how the steady-state error is eliminated. In the proposed method, an observer is designed to estimate the disturbances and states. The results show how the proposed control method increases the robustness of the model predictive controller in simulation and in real-time implementations on a new QTS proposed in this work together with techniques designed to identify the parameters of this novel plant. [ABSTRACT FROM AUTHOR]

The dc arc hazard is a great concern to industry. Quantitative arc hazard assessments are performed on dc systems to determine a nearby worker's potential incident energy exposure during an arcing event. Four viable dc assessment methods are reviewed in this paper. The most widely used model for predicting dc incident energy is based on Lee's theoretical arc model; the electrical arc power is determined from the maximum power transfer theorem, and the arc is depicted as a spherical radiant source with uniform heat transmission in all directions. Like Lee's model, Ammerman's model assumes complete conversion of electrical arc energy into thermal energy, but arc power is determined from an iterative technique constrained by arc power and circuit characteristics. Ammerman incorporates multiplying factors which account for the higher incident energies associated with arcing in enclosures. Based on dc arc testing, the applicability of an existing software package has been extended to dc systems through multiplying factors, and equations for dc rail and transit systems have also been developed. Model derivation is examined in this paper for suitability to arcing in general and dc specifically. Model performance is assessed using the available limited data (ac or dc). Example calculations are provided. [ABSTRACT FROM PUBLISHER]

This paper proposes a dual-mode model predictive direct current control (MP-DCC) of a permanent magnet synchronous generator (PMSG). The proposed algorithm is capable of minimizing switching losses in a two-level synchronous generator side converter (SGSC). A new prediction model that takes the converter dead time into consideration when choosing the optimal switching state is introduced. The proposed prediction model provides a more accurate state prediction, ensuring that the states of the system stay inside the control invariant set in the steady state, even in the case of a significant converter dead time. To guarantee recursive feasibility and closed-loop stability, a flexible control Lyapunov function (CLF) is employed as an optimization problem constraint, which enables the minimization of switching losses both during transients and in the steady state. The influence of the converter dead time on the performance of the proposed algorithm is considered, and accordingly, a control invariant set is determined. Simulation results show that stator currents are kept within the control invariant set if dead time is taken into account in the prediction model. Furthermore, the proposed algorithm is implemented in a digital control system and experimentally verified on a 375 kW interior PMSG. Experimental results verify that the proposed control algorithm provides a successful flying start of the PMSG and show that the application of the flexible CLF results in a lower switching frequency, but also in higher current ripple. By adjusting the upper bound of the control invariant set, a desired tradeoff between the low stator current ripple and the minimization of switching losses can be achieved. [ABSTRACT FROM PUBLISHER]

This paper presents the concept of three-phase module-integrated converters (MICs) incorporated in grid-tied large-scale photovoltaic (PV) systems. The current-source converter with dc voltage boost capability, namely the single-stage power conversion system, is proposed for the three-phase PV MIC system. A model predictive scheme with low switching frequency is designed to control the proposed topology in such a way that provides a certain amount of active and reactive power in steady-state operation and also provides a proper ratio of reactive power under transient conditions to meet the low-voltage ride through regulations. To predict the future behavior of current control values and switching states, a discrete-time model of the MIC is developed in the synchronous reference frame. It is demonstrated that the injected active and reactive power can be controlled by minimizing the cost function introduced in the predictive switching algorithm. The proposed structure is simulated in MATLAB/SIMULINK software. An experimental verification is provided to justify the performance of the proposed control method through a 300-VA laboratory prototype. The results verify the desired performance of the proposed control scheme for exchanging both active and reactive powers between the PV MIC and the grid within different operating conditions. [ABSTRACT FROM AUTHOR]

Wind generation forecast approaches are typically based on fixed model structures, which might fit well with existing data but fall short in forecasting out-of-sample data. The reason lies in that predetermined models cannot always capture the actual dynamic features of wind farms, resulting in unavoidable model errors. In this paper, a novel ultra-short-term wind generation forecast approach based on multivariate empirical dynamic modeling is proposed. The time series of wind generation and the explanatory variables are applied for attractor reconstruction according to Takens’ theorem. By this means, the dynamics of the original wind generation system can be accurately captured by the trajectories of the state variables in the reconstructed space. Then, using a simplex projection approach, wind generation can be directly forecasted with the trajectories. A unique feature of the proposed approach is that its effectiveness depends solely on the potential context hidden in the sequences of the state variables rather than the correctness of the predetermined models. Consequently, this approach can faithfully depict the dynamics of wind farms and improve the forecast accuracy. Case studies illustrate that the proposed approach can obtain advanced forecast results comparing with the benchmark methods. [ABSTRACT FROM AUTHOR]

This paper presents the validation of a first-order winding thermal model in machine operative conditions. The proposed model can be used in motor control strategies for the winding temperature prediction during transient overload, or vice versa, for the prediction of the maximum time duration of the overload maintaining the winding temperature within the limit imposed by the class of insulation. The thermal model has been validated using two different electrical machines. The first one is a 10-kW automotive starter-generator prototype for mini-hybrid powertrain equipped with distributed bar windings, while the second one is a 2.2 kW total enclosed fan cooled industrial induction motor equipped with conventional stranded wire windings. Depending on the application for both machines, a short-duty transient operation in overload conditions could be required. In particular, the automotive starter-generator must accomplish engine cranking and torque assistance during the vehicle acceleration and braking, while the industrial induction machine could operate in intermittent service in overload conditions when used in machine tool applications. As a consequence, an accurate stator winding temperature prediction is mandatory to fully exploit the machine performance. For both motors, the thermal model parameters have been evaluated by fast experimental approach, and, subsequently, the model has been validated during operative overload conditions. [ABSTRACT FROM AUTHOR]

The effect of inverter dead time on the inverter output voltage is well studied in the literature. This paper studies the effect of the dead time on the inverter input current. An expression for the switching-cycle-averaged inverter input current including the dead time effect is derived, which shows the dead time effect to primarily result in a reduction in the average value of the inverter dc input current. The effect of dead time on the input current is incorporated into the dc-link dynamic model of a diode-bridge-rectifier (DBR) fed voltage source inverter (VSI). A switching-cycle-average model of a DBR-VSI-fed induction motor is proposed, which considers the effect of dead time on both the ac output voltage and dc input current of the VSI. The proposed average model is used to study the light-load instability phenomenon in a 100-kW open-loop motor drive. The proposed model is shown to predict the region of oscillatory behavior (i.e., region exhibiting light-load instability) with better accuracy than existing average models. The proposed model also accurately predicts the dc-link voltage swing at such oscillatory operating points. While being comparable in accuracy in predicting the system dynamics, the proposed model is much more computationally efficient than the state-of-the-art switching model. The validity of the proposed average model is also confirmed by experimental measurements on a 100-kW induction motor drive. [ABSTRACT FROM AUTHOR]

With the rapid development of China high-speed railway, the low frequency oscillation (LFO) of electrical quantities appears more often recently and leads to some severe problems of train operation. To improve the traction line-side converter control and suppress the phenomenon economically and effectively, a model-based predictive direct power control (MPDPC) approach is proposed in this paper. The approach adopts a discrete-time model of traction line-side converter in d–q reference frame to predict the future values of the input active and reactive power. The optimal switching state is selected by minimizing a cost function of power to evaluate the power errors at the next sampling time. Through the theoretical analysis and simulations, the performance of MPDPC is compared with traditional transient direct current control (TDCC) that is widely adopted in China Railway High-Speed 3 electric-multiple-unit. The real-time online simulations based on Real-Time Laboratory (RT-LAB) are also realized to further validate the results. Moreover, two vehicle-grid cascade simulation systems are constructed, and the LFO suppressing capability of MPDPC and TDCC is compared. Finally, the steady-state characteristic, dynamic characteristic, and LFO suppressing capability of MPDPC are demonstrated through the analysis of key performance indexes. [ABSTRACT FROM AUTHOR]

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler–Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method. [ABSTRACT FROM AUTHOR]

Thermal overload protection techniques commonly use a simplified 1st order thermal model, which does not provide accurate stator winding temperature prediction and causes frequent undesirable stoppage of the motor in the processes. This paper proposes a higher order lumped parameter thermal network model to accurately predict stator winding temperature. First, the comparison of the predicted stator winding temperature from a 1st order thermal model with a higher order thermal model is performed. Second, a comparison between the actual and the predicted stator winding temperature rise is verified for a 20 hp totally enclosed fan-cooled copper rotor induction motor that has no rotor fins on its short-circuit ring to enhance heat dissipation compared with an aluminum rotor induction motor. [ABSTRACT FROM PUBLISHER]

Recently, model-predictive torque control (MPTC) has been introduced as a powerful control method for induction motor drives. However, the weighting factor for stator flux must be tuned carefully to obtain satisfactory performance at different operation points. Unfortunately, so far the tuning of weighting factor in MPTC is mostly based on an empirical procedure. This paper solves this problem by proposing a model-predictive flux control (MPFC), which uses the stator flux vector as the control variable and eliminates the complicated prediction of stator current at $k+2$ instant. As a result, the weighting factor in conventional MPTC is eliminated, and the control complexity is significantly reduced. Both MPTC and MPFC are tested and compared in detail, including steady-state performance, dynamic response, and low-speed operation. The simulation and experimental results prove that the performance of conventional MPTC is dependent on the weighting factor, and the improper weighting factor would lead to significant performance deterioration. On the contrary, the proposed MPFC achieves similar or even better overall performance over a wide speed range with very low tuning work. Hence, it is concluded that the proposed MPFC is more practical than conventional MPTC. [ABSTRACT FROM PUBLISHER]

Due to the deregulation of the power system, the electric power industry is undergoing a transformation in terms of its planning and operation strategies. Because of the importance in reducing financial and operational risk, improving load forecasting accuracy is paramount. In some load forecasting applications, K-means clustering is used to group customers prior to forecasting. This method has been shown to improve the accuracy of load predictions. However, there are situations where K-means clustering reduces load forecasting accuracy. This paper studies the factors that affect the performance of K-means clustering. Additionally, several strategies have been proposed to tackle the lower estimation accuracy problems for this clustering algorithm. The data used for validating the proposed strategies associated with the factors is from the Consolidated Edison Company of New York, Inc. (Con Edison). The mean absolute percent error and relative mean square error are utilized to evaluate the forecasting results of K-means-based least squares support vector machines (LS-SVM) and preprocessed K-means-based LS-SVM. Additionally, the outperformance of preprocessed K-means-based LS-SVM is demonstrated via the data results. [ABSTRACT FROM PUBLISHER]

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel stratification-based wind power forecasting method and develops a hybrid forecasting model at different stratifications using charged system search algorithm. The proposed model applies the concept of segmentation from the theory of optimal stratification to forecast short-term wind power outputs. Additionally, the proposed method elucidates different weighting values of each individual model at different segmentation blocks. Based on the forecasting results, the proposed stratification-based hybrid model outperforms traditional stand-alone models and unstratified hybrid models in terms of forecasting accuracy, which verifies the proposed forecasting model for accurate wind power forecasting. [ABSTRACT FROM AUTHOR]

This paper demonstrates light-load instability in a 100-kW open-loop induction motor drive on account of inverter deadtime. An improved small-signal model of an inverter-fed induction motor is proposed. This improved model is derived by linearizing the nonlinear dynamic equations of the motor, which include the inverter deadtime effect. Stability analysis is carried out on the 100-kW 415-V three-phase induction motor considering no load. The analysis brings out the region of instability of this motor drive on the voltage versus frequency (V-f) plane. This region of light-load instability is found to expand with increase in inverter deadtime. Subharmonic oscillations of significant amplitude are observed in the steady-state simulated and measured current waveforms, at numerous operating points in the unstable region predicted, confirming the validity of the stability analysis. Furthermore, simulation and experimental results demonstrate that the proposed model is more accurate than an existing small-signal model in predicting the region of instability. [ABSTRACT FROM AUTHOR]

In addition to providing charging service, an electric vehicle charging station equipped with a distributed energy storage system can also participate in the deregulated market to optimize the cost of operation. To support this function, it is necessary to achieve sufficient accuracy on the forecasting of energy resources and market prices. The deregulated market price prediction presents challenges since the occurrence and magnitude of the price spikes are difficult to estimate. This paper proposes a hybrid method for very short term market price forecasting to improve prediction accuracy on both nonspike and spike wholesale market prices. First, support vector classification is carried out to predict spike price occurrence, and support vector regression is used to forecast the magnitude for both nonspike and spike market prices. Additionally, three clustering techniques including classification and regression trees, K-means, and stratification methods are introduced to mitigate high error spike magnitude estimation. The performance of the proposed hybrid method is validated with the Electric Reliability Commission of Texas wholesale market price. The results from the proposed method show a significant improvement over typical approaches. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive direct power control strategy for a grid-connected inverter used in a photovoltaic system as found in many distributed generating installations. The controller uses a system model to predict the system behavior at each sampling instant. The voltage vector that generates the least power ripple is selected using a cost function and applied during the next sampling period; thus, flexible power regulation can be achieved. In addition, the influence of a one-step delay in the digital implementation is investigated and compensated for using a model-based prediction scheme. Furthermore, a two-step horizon prediction algorithm is developed to reduce the switching frequency, which is a significant advantage in higher power applications. The effectiveness of the proposed model predictive control strategy was verified numerically by using MATLAB/Simulink and validated experimentally using a laboratory prototype. [ABSTRACT FROM AUTHOR]

This paper presents a model for calculating the hysteresis loss in electrical machines. The model can achieve accurate and computationally efficient hysteresis loss calculations by utilizing both analytical equations and the Energetic hysteresis model. The model results are experimentally verified by comparing to a series of minor hysteresis loop measurements. The hybrid model is then implemented to calculate the core losses in a switched reluctance machine using finite-element simulation. The results show that precise machine core loss prediction requires having a model that is capable of calculating the hysteresis losses under a variety of minor hysteresis loops. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent workplace parking garage for plug-in hybrid electric vehicles (PHEVs). The system involves a developed smart power charging controller, a 75-kW photovoltaic (PV) panel, a dc distribution bus, and an ac utility grid. Stochastic models of the power that is demanded by PHEVs in the parking garage and the output power of the PV panel are presented. In order to limit the impact of the PHEVs' charging on the utility ac grid, a fuzzy logic power-flow controller was designed. Based on their power requirements, the PHEVs were classified into five charging priorities with different rates according to the developed controller. The charging rates depend on the predicted PV output power, the power demand by the PHEVs, and the price of the energy from the utility grid. The developed system can dramatically limit the impact of PHEVs on the utility grid and reduce the charging cost. The system structure and the developed PHEV smart charging algorithm are described. Moreover, a comparison between the impact of the charging process of the PHEVs on the grid with and without the developed smart charging technique is presented and analyzed. [ABSTRACT FROM AUTHOR]

A practical approach for the probabilistic short-term generation forecast of a wind farm is proposed in this paper. Compared with deterministic wind generation forecast, probabilistic wind generation forecast can provide important wind generation distribution information for operation, trading, and some other applications. The proposed approach is based on a sparse Bayesian learning (SBL) algorithm, which produces probabilistic forecast results by estimating the probabilistic density of the weights of Gaussian kernel functions. Furthermore, since the wind generation time series exhibits strong nonstationary property, a componential forecast strategy is used to improve the forecast accuracy. According to the strategy, the wind generation series is decomposed into several more predictable series by discrete wavelet transform, and then, the resulted series are forecasted using the SBL algorithm. To fulfill multilook-ahead wind generation forecast, a multi-SBL forecast model is constructed in the context. Tests on a 74-MW wind farm located in southwest Oklahoma demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Field-oriented control (FOC) has proven effective for controlling ac drives with good dynamic performance. However, operation at low-switching frequencies and the sensitivity of traditional feedforward loops to system parameters pose severe limitations on the achievable performance and require a tedious tuning procedure. In this paper, we present a systematic cascade explicit model predictive control framework for the FOC of electrical drives, resolving the aforementioned issues while being sufficiently simple to be widely implemented on various ac drive systems. The resulting closed-loop system exhibits high dynamic performance for all operating points, even at low-switching frequencies. We present experiments with a permanent-magnet machine and an induction motor, demonstrating the practical feasibility and the merits of the proposed framework over traditional controller designs for electrical drives. [ABSTRACT FROM AUTHOR]

The prediction algorithm is one of the most important factors in the quality of wind-power prediction. In this paper, based on the principles of wavelet transform and support vector machines (SVMs), as well as the characteristics of wind-turbine generation systems, two prediction methods are presented and discussed. In method 1, the time series of model input are decomposed into different frequency modes, and the models are set up separately based on the SVM theory. The results are combined together to forecast the final wind-power output. For comparison purposes, the wavelet kernel function is applied in place of the radial basis function (RBF) kernel function during SVM training in method 2. The operation data of one wind farm from Texas are used. Mean relative error and relative mean square error are used to evaluate the forecasting errors of the two proposed methods and the RBF SVM model. The means of evaluating the prediction-algorithm precision is also proposed. [ABSTRACT FROM AUTHOR]