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This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day-ahead (DA) and adaptive power pinch strategy. [ABSTRACT FROM AUTHOR]

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a dc–dc switch mode power converter. The proposed estimation algorithm is based on a novel combination between the classical Kalman filter (KF) and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the KF, with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky proportional, integral, derivative (PID) controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a precalculated average model. [ABSTRACT FROM AUTHOR]

This paper proposes a novel sensorless speed estimation for an induction motor (IM) based on a new stator-voltage model reference adaptive system (Vs-MRAS) scheme. This is utilized for torque-controlled drive (TCD) based on indirect rotor field oriented control technique in limp-home mode operation of electric vehicles (EV) applications. The Vs-MRAS scheme uses the error between the reference and estimated stator-voltage vectors and estimates the synchronous speed. Unlike existing MRAS schemes, the proposed sensorless scheme does not require the measured nominal values of stator resistance, stator inductance, and rotor resistance. This scheme is insensitive to variations of the aforementioned parameters. Moreover, using the proposed scheme eliminates the need for slip calculation. The proposed scheme is implemented and experimentally tested in a lab environment, on a 19-kW IM, and also applied on an electric golf buggy, powered by a 5-kW IM. The experimental results show that the proposed scheme is immune to parameter variations and is consistent in vehicle-starting from standstill and hill-starting tests. This scheme is also free from drift problems associated with a pure integration and is stable in the field weakening region. The test-drive results from the golf buggy confirm suitability of the proposed Vs-MRAS scheme over a wide range of speeds for the purpose of TCD in EV applications. [ABSTRACT FROM PUBLISHER]

This paper investigates the effect of the stator winding connection on the performance of a six-phase switched reluctance machine (SRM). Five winding connection types are proposed for the machine. Finite element analyses (FEAs) of flux distribution, output torque, and core losses are presented under single-phase and multiphase excitation for each connection and the results are used to compare the average torque and torque ripple ratio characteristics and to develop understanding of the respective contributions of mutual inductance in the torque development. Experimental tests on a six-phase conventional SRM verify the torque performance and mutual inductance effects of the different winding connections. An optimum winding configuration for a six-phase SRM is proposed. [ABSTRACT FROM AUTHOR]

This paper presents a novel speed estimation scheme for induction motors (IMs) based on back electromotive-force model reference adaptive system (back-EMF MRAS). The scheme is employed for the purpose of sensorless fault-tolerant torque-controlled drives used in a limp-home mode operation in electric vehicle (EV) applications. The proposed scheme was experimentally tested on a laboratory dynamometer using a 19-kW IM and a 29-kW controller, which are both currently used in the automotive industry for EV applications. The scheme was also implemented on an electric golf buggy which was equipped with a 5-kW IM. A performance comparison was carried out between the proposed and conventional back-EMF MRAS schemes for starting from standstill, sensitivity to parameter variations and constant speed operation with load variations. Utilizing the golf buggy, the behaviors of the new scheme was separately investigated for vehicle starting from standstill, wide speed range including field weakening region, and hill-starting operations. The proposed scheme is computationally easy to implement, robust against sensitivity to parameters variations, inverter nonlinearity and errors due to digitization in the field weakening region. This scheme is not only consistent for vehicle starting from standstill, it also provides a reliable vehicle-drive in the field weakening region and during vehicle hill-starting. The dynamometer and vehicle test-drive results show the suitability of the proposed scheme for the purpose of EV fault-tolerant limp-home mode operation. [ABSTRACT FROM PUBLISHER]

To achieve high-performance control of modern dc–dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc–dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc–dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc–dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method. [ABSTRACT FROM AUTHOR]

This paper presents a novel predictive model reference adaptive system (MRAS) speed estimator for sensorless induction motor (IM) drives applications. The proposed estimator is based on the finite control set-model predictive control (FCS-MPC) principle. The rotor position is calculated using a search-based optimization algorithm which ensures a minimum speed tuning error signal at each sampling period. This eliminates the need for a proportional–integral (PI) controller which is conventionally employed in the adaption mechanism of MRAS estimators. Extensive experimental tests have been carried out to evaluate the performance of the proposed estimator using a 2.2-kW IM with a field-oriented control (FOC) scheme employed as the motor control strategy. Experimental results show improved performance of the MRAS scheme in both open- and closed-loop sensorless modes of operation at low speeds and with different loading conditions including regeneration. The proposed scheme also improves the system robustness against motor parameter variations and increases the maximum bandwidth of the speed loop controller. [ABSTRACT FROM PUBLISHER]

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method. [ABSTRACT FROM PUBLISHER]

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg–Marquardt algorithm, which is developed and applied here for this purpose. [ABSTRACT FROM PUBLISHER]

An induction generator always absorbs lagging reactive power from the power network. In most previous power system optimization studies involving induction generator based distributed generation, this generator reactive power demand is calculated using an approximate empirical formula. In addition, shunt compensation capacitors have not been considered as part of the network optimization study. In this article, the use of the per phase equivalent circuit of the induction generator is proposed to compute its reactive power requirement, thus providing a more accurate estimation. A discrete particle swarm optimization algorithm is then employed to address the problem of simultaneous integration of the induction generators and shunt compensation capacitors. The proposed algorithm has the advantage of being able to cope with a mixed search space optimization problem of integer, discrete, and continuous variables. The study is carried out on a standard 69-bus benchmark distribution network. Results show that the use of the approximate empirical formula leads to an underestimation of the machine reactive power demand. The inclusion of shunt compensation capacitors in the optimization process results in lower network reactive power flows with improved network power losses, improved voltage profile, and increased levels of distributed generation integration. [ABSTRACT FROM PUBLISHER]

This study presents a new concept for single‐phase diode bridge rectifier circuits applied to automotive applications. The proposed circuit is able to enhance the output voltage to a much higher output level over a wide load range compared to the classical single‐phase diode bridge rectifier without using complex control algorithms. This is achieved by embedding two capacitors and two switches to a new designed diode bridge converter topology. The embedded capacitors are charged and discharged simultaneously with the help of two switches. The embedment of the capacitors and switches will provide two functions: energy storage as known from traditional rectifier circuits and voltage oscillation with the supply inductance to reduce supply current distortion. The switches are on/off event triggered and do not require any complex control algorithms. The operation and the performance of the proposed rectifier are simulated using Matlab/Simulink software. Experimental results are obtained from a 1 kW prototype test circuit, which is used to evaluate the performance of the proposed circuit. Test results show a significant increase in the output voltage in addition to a reduced supply current distortion. [ABSTRACT FROM AUTHOR]

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non‐invasive way is very challenging because of the inherent non‐linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo‐generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid. [ABSTRACT FROM AUTHOR]

Abstract—An optimization algorithm based on a novel discrete particle swarm optimization technique is proposed in this article for optimal sizing and location of distributed generation in a power distribution network. The proposed algorithm considers distributed generation size and location as discrete variables substantially reducing the search space and, consequently, computational requirements of the optimization problem. The proposed algorithm treats the generator sizes as real discrete variables with uneven step sizes that reflect the sizes of commercially available generators, meaning that it can handle a mixed search space of integer (generator location), discrete (generator sizes), and continuous (reactive power output) variables while substantially reducing the search space and, consequently, computational burden of the optimization problem. The validity of the proposed discrete particle swarm optimization algorithm is tested on a standard 69-bus benchmark distribution network with four different test cases. Two optimization scenarios are considered for each test case: a single objective optimization study where network real power loss is minimized and a multi-objective study in which network voltages are also considered. The proposed algorithm is shown to be effective in finding the optimal or near-optimal solution to the problem at a fraction of the computational cost associated with other algorithms. [ABSTRACT FROM PUBLISHER]

The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. It is common for the speed measurement from the motor encoder to be used for the vehicle speed feedback, after taking into account the gear ratio, but it is not valid to assume that motor and vehicle speeds are equal during transient conditions. In this study it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. Estimates of vehicle speed and mass have been realised using a Kalman filter (KF) and a recursive least‐squares estimator, and validated with experimental results. The study also shows the importance of finding the most optimal process noise matrix Q for the KF, this has been carried out using a genetic algorithm, with the estimation accuracy then compared with varying vehicle mass. [ABSTRACT FROM AUTHOR]

The performance of a stator current‐based model reference adaptive systems (MRAS) speed estimator for sensorless induction motor drives is investigated in this study. The measured stator currents are used as a reference model for the MRAS observer to avoid the use of a pure integrator. A two‐layer, online‐trained neural network stator current observer is used as the adaptive model for the MRAS estimator which requires the rotor flux information. This can be obtained from the voltage or current models, but instability and dc drift can downgrade the overall observer performance. To overcome these problems of rotor flux estimation, an off‐line trained multilayer feed‐forward neural network is proposed here as a rotor flux observer. Hence, two networks are employed: the first is online trained for stator current estimation and the second is off‐line trained for rotor flux estimation. Sensorless operation for the proposed MRAS scheme using current model and neural network rotor flux observers are investigated based on a set of experimental tests in the low‐speed region. Using a neural network rotor flux observer to replace the current model is shown to solve the stability problem in the low‐speed regenerating mode of operation. [ABSTRACT FROM AUTHOR]

Many control schemes have been proposed for induction motors, which are in themselves highly complex non‐linear and sometimes internally unstable systems. One of the most accurate control schemes is encodered rotor flux orientated vector control. The advantages and disadvantages of this control are well known and several variations, or reduced vector schemes, have been proposed. This study introduces an improved encoderless scalar, or approximated vector, control method for induction machines which can be applied to general purpose applications that do not require the most precise control. The proposed method overcomes practical difficulties and is suitable for industrial applications. The slip compensated stator flux linkage oriented scheme proposed in this study does not require flux estimation or a speed sensor, only requiring nameplate data, stator current and stator resistance measurement, which can easily be determined at start‐up. Simulation and experimental investigations including field weakening operation and the effect of stator resistance variation demonstrate the improved performance of the new scheme compared to previous open loop V/Hz and stator resistive compensated schemes especially at low rotor speeds. [ABSTRACT FROM AUTHOR]

Active power filter (APF) performance is affected by the system delay introduced in the reference signals and/or the actual injected current. This study introduces an artificial intelligent-based technique using a neural network control strategy for a shunt APF. The proposed system is capable of mitigating specific harmonic orders and consequently achieves low harmonic factor and reduced current total harmonic distortion (THD). In addition, the delay time introduced when extracting the harmonic reference current and when controlling the filter current is minimised. The control technique presented in this study is assessed on a medium voltage 6-pulse current source converter where the mains current dominant harmonics (5th, 7th, 11th and 13th) are compensated. The proposed system is simulated using MATLAB/SIMULINK. A prototype system is used to experimentally validate the proposed APF performance. [ABSTRACT FROM AUTHOR]

This paper investigates a nontriplen multiphase induction machine when fed with harmonic current injection with different sequences for an open loop optimized flux distribution that produce a quasi-square wave in the machine air gap. This maximizes iron utilization, giving more torque per ampere. The relation between the fundamental and other harmonic components can be determined for the best iron utilization using genetic algorithms where optimum flux distribution with different injected harmonic order can be obtained. This means, the target is to optimize the flux distribution during no-load to determine the optimum constants that guarantee approximate square wave air-gap flux. The paper focuses on an 11-phase machine that can be excited with harmonics up to the ninth. The technique is assessed using both winding function and finite element analysis methods. The prototype machine is fed from an 11-phase inverter. The system DSP control using genetic algorithm produces an optimum flux distribution by using winding sequence and harmonic current injection. Simulation results for the 11-phase dq model and prototype drive experimental results are presented. [ABSTRACT FROM AUTHOR]

In this paper, two novel adaptation schemes are proposed to replace the classical PI controller used in model reference adaptive speed-estimation schemes that are based on rotor flux. The first proposed adaptation scheme is based on sliding-mode theory. A new speed-estimation adaptation law is derived using Lyapunov theory to ensure estimation stability, as well as fast error dynamics. The other adaptation mechanism is based on fuzzy-logic strategy. A detailed experimental comparison between the new and conventional schemes is carried out in both open- and closed-loop sensorless modes of operation when a vector control drive is working at very low speed. Superior performance has been obtained using the new sliding-mode and fuzzy-logic adaptation mechanisms in both modes of operations. [ABSTRACT FROM AUTHOR]