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Research and development are critical for driving economic growth. To realise the UK government’s Industrial Strategy, we develop an energy research and innovation infrastructure roadmap and landscape for the energy sector looking to the long term (2030). This study is based on a picture of existing UK infrastructure on energy. It shows the links between the energy sector and other sectors, the distribution of energy research and innovation infrastructures, the age of these infrastructures, where most of the energy research and innovation infrastructures are hosted, and the distribution of energy research and innovation infrastructures according to their legal structure. Next, this study identifies the roadmap of energy research and innovation infrastructures by 2030, based on a categorisation of the energy sector into seven subsectors. Challenges and future requirements are explored for each of the sub-sectors, encompassing fossil fuels and nuclear energy to renewable energy sources and hydrogen, and from pure science to applied engineering. The study discusses the potential facilities to address these challenges within each sub-sector. It explores the e-infrastructure and data needs for the energy sector and provides a discussion on other sectors of the economy that energy research and innovation infrastructures contribute to. Some of the key messages identified in this study are the need for further large-scale initiative and large demonstrators of multi-vector energy systems, the need for multi-disciplinary research and innovation, and the need for greater data sharing and cyber-physical demonstrators. Finally, this work will serve as an important study to provide guidance for future investment strategy for the energy sector.

Nowadays, the requirement to ameliorate efficiency in power conversion systems along with the demand for increased power rating gives rise to the implementation of interleaving operation. Interleaving in conjunction with digital state feedback control provide the ability to create sophisticated control schemes which allow for high efficiency under a wide range of operating conditions and restrictions. Along these lines, the interleaved boost converter finds widespread application in a variety of cases such as battery charging, renewable energy sources and distributed power systems. A very salient aspect concerning the performance of the converter is the occurrence of limit cycle instabilities that can have an adverse effect on the operation of the converter resulting in efficiency and lifetime reduction. These instabilities are a trait of the piecewise linear nature of the system dynamics, in which case, a bifurcation analysis is required to investigate their influence on the system. However, in the case of interleaving along with digital control the standard implementation of the bifurcation analysis for determining the Monodromy matrix is impeded by the dependency of the system of past sampled states. As a consequence, the conventional approaches found in literature are inadequate when it comes to predicting and avoiding these kind of instabilities. This paper addresses the specific issues and presents a novel approach on defining the Monodromy Matrix and deciding upon the stability of the limit cycle. The proposed approach relieves the dependence of the system on past samples by augmenting the first return map with expressions that describe the evolution of the control laws. The interleaved boost converter is used as a case study. Finally, numerical, analytical and experimental results validate our work.

Due to the significance of hybrid energy systems, selecting the appropriate sizing-energy management strategy combination represents a key factor to ensure the efficient operation while offering a competitive operating cost. This paper presents an integrated framework for finding the best size-energy management strategy combination for a hybrid energy system. Fundamental to this framework is utilizing finite automata to develop multiple energy management strategies that fully take into consideration the dynamic relationship between all the assets in the hybrid energy system. The proposed integrated framework consists of three main steps. First, an analytical and economic sizing approach is performed to find the initial sizes of the hybrid energy system assets based on an initial energy management strategy; second, using finite automata to implement the initial energy management strategy and instantiate different energy management strategies; and third, an evaluation model is developed to assess the instantiated energy management strategies and extract the featured conditions to create new-improved one. This new energy management strategy is used to re-exercise the analytical and economic sizing to obtain the best size-energy management strategy combination. The novelty in this work can be summarized as taking the impact of selecting the right energy management strategy on the sizing of a hybrid energy management. This can lead to better performance and can be explained in our integrated framework by reducing the cost, reducing the diesel generator and fuel cell working hours and increasing the photovoltaic utilization. Moreover, using finite automata in implementing and instantiating multiple energy management strategies to attain an improved one has not been reported. A comparison between the results of the proposed framework and the results of the analytical and economic sizing approach is carried out. The size of the photovoltaic is reduced from 140 kW to 60 kW when using the integrated framework and therefore the size of the electrolyzer and the hydrogen tank reduced to the half. Moreover, a reduction in the diesel generator working hours by 35% and in the levelized cost of energy by 40% are achieved.

System identification is fundamental in many recent state-of-the-art developments in power electronic such as modeling, parameter tracking, estimation, self-tuning and adaptive control, health monitoring, and fault detection. Therefore, this paper presents a comprehensive review of parametric, non-parametric, and dual hybrid system identification for dc–dc switch mode power converter (SMPC) applications. The paper outlines the key challenges inherent with system identification for power electronic applications; speed of estimation, computational complexity, estimation accuracy, tracking capability, and robustness to disturbances and time-varying systems. Based on the literature in the field, modern solutions to these challenges are discussed in detail. Furthermore, this paper reviews and discusses the various applications of system identification for SMPCs; including health monitoring and fault detection.

Linear ramp slope compensation (LRC) and quadratic slope compensation (QSC) are commonly implemented in peak-current-mode-controlled dc-dc converters in order to minimize subharmonic and chaotic oscillations. Both compensating schemes rely on the linearized state-space averaged model (LSSA) of the converter. The LSSA ignores the impact that switching actions have on the stability of converters. In order to include switching events, the nonlinear analysis method based on the Monodromy matrix was introduced to describe a complete-cycle stability. Analyses on analog-controlled dc-dc converters applying this method show that system stability is strongly dependent on the change of the derivative of the slope at the time of switching instant. However, in a mixed-signal-controlled system, the digitalization effect contributes differently to system stability. This paper shows a full complete-cycle stability analysis using this nonlinear analysis method, which is applied to a mixed-signal-controlled converter. Through this analysis, a generalized equation is derived that reveals for the first time the real boundary stability limits for LRC and QSC. Furthermore, this generalized equation allows the design of a new compensating scheme, which is able to increase system stability. The proposed scheme is called polynomial curve slope compensation (PCSC) and it is demonstrated that PCSC increases the stable margin by 30% compared to LRC and 20% to QSC. This outcome is proved experimentally by using an interleaved dc-dc converter that is built for this work.

This paper evaluates several state-feedback control design methods for a multi-phase interleaved DC–DC boost converter with an arbitrary number of legs. The advantages of state-feedback control laws are numerous since they do not burden the system with the introduction of further zeros or poles that may lead to poorer performance as far as overshoot and disturbance rejection is concerned. Both static and dynamic full state-feedback control laws are designed based on the converter’s averaged model. Building on previous work, this paper introduces significant extensions on the investigation of several undesirable bifurcation phenomena. In the case of static state-feedback it is shown that interleaving can give rise to more severe bifurcation phenomena, as the number of phases is increased, leading to multiple equilibria. As a remedy, a bifurcation analysis procedure is proposed that can predict the generation of multiple equilibria. The novelty of this paper is that this analysis can be integrated into the control design so that multiple equilibria can be completely avoided or ruled out of the operating region of interest. The proposed control laws are digitally implemented and validated in a 2-leg case study using both simulation and experimentation.

The available controller chips for power electronic systems are used for specific transient and steady-state performances. In such systems, the parameter ranges for stable operation are delimited by slow-timescale as well as fast-timescale instabilities. The usual practice is to design the control loop based on the state-space averaging technique, which cannot predict the fast-timescale instabilities. In this study, the first attempt has been made to propose a new design of the controller chip that can suppress these instabilities, thus extending the stable operating range in the parameter space. For this, the authors make use of the Filippov method which can effectively predict both types of instabilities. In this approach, the stability of the system is obtained in terms of the state transition matrices across each subsystem and saltation matrices across each switching event. The basic idea is to exercise control over the saltation matrices to increase the stability margin of the periodic orbit. Since in the Filippov approach an increase in the number of switching events in a cycle does not increase the complexity of the analysis, the proposed controller chip will be particularly useful in complex power electronic systems such as interconnected converters, multi-input multi-output converters, resonant converters, micro-grids and so on.

Summary This paper presents a unified and exact nonaveraged approach to derive a frequency-domain control-oriented model for accurate prediction of the fast timescale dynamics and performances of switching converters with fixed frequency naturally sampled pulse width modulation and integrating feedback loop. Because the approach avoids averaging and approximations related to this process, a very good accuracy of the derived model is obtained. The main difference between the presented approach and the existing methodology for accurately predicting the behavior of switching converters is that, here, we break the feedback loop and we focus on analyzing the open-loop gain and the effect of the system parameters on relative stability. This results in an approach much similar to control systems techniques rather than nonlinear dynamical system approaches. Consequently, the relative stability is tackled easily in the frequency domain. In particular, by treating the modulator as a gain depending on the operating point, the new model is formulated in such a way that standard control-oriented tools such as Bode diagrams and root-loci can be easily used. Therefore, the proposed approach gives some important issues like gain and phase margins that are highly useful in controller design. It is noticed that the crossover frequency, gain, and phase margins predicted by using the averaged model may deviate significantly from the actual values given by the proposed approach. The paper points out the sources of discrepancies and the theoretical results are validated by simulations using a circuit-level switched model.

One of the main advantages of digital control is the ability to design more sophisticated control strategies to enable high-performance dc–dc converters. One such example is a buck converter operating with a digital state-feedback controller. Previous works characterise the nonlinear dynamics of such systems under ideal operating conditions. However, in practical applications, these conditions cannot be guaranteed. The focus of this work is on the behaviour of such systems when they operate in the presence of strong interference signals. Previous works on the effect of noise have shown that intermittent operation is possible when the frequency of the noise signal is close to the switching frequency. Intermittent operation can be characterised by long periods of stable operation interspersed with periods of unstable or chaotic operation, which greatly downgrade the efficiency and performance of the converter and reduce its lifetime as for example increase the current ripple or add extra AC components at its output. Typically, such behaviour is avoided by modifying the circuit parameters. However, little or no work exists on developing design guidelines in order to effect its elimination. This is the focus of this research, that is, by utilising Filippov's theory on discontinuous differential equations, to set out a design procedure that can be applied to any dc–dc converter, to tune its controller in order to eliminate intermittent operation. As a case study, the digitally controlled buck converter with a state-feedback control law is selected. Copyright © 2017 John Wiley & Sons, Ltd.

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

This work proposes a systematic approach for the adaptive identification and implementation of efficient power management strategies (PMS) in the course of operation of hybrid renewable energy microgrids. The approach is based on the temporal evolution of the system power grand composite curve (PGCC), which is adaptively shaped on-line and within short-term time intervals to form a sequence of decisions indicating the instant and duration of activation of different subsystems. It builds on from previous work where the potential for system performance enhancement could not be exploited through pre-specified PMS identified off-line. More specifically, it involves a stored energy targeting step that exploits the PGCC to identify the desired operational profile of an accumulator during a prediction horizon in order to satisfy the system operating goals. The identified energy targets are subsequently enforced through a sequence of control actions that enable the exact matching of the PGCC hence resulting in a new PMS. The method is elaborated graphically for multiple potential operating goals and is supported by a formal mathematical model that captures system structural and temporal characteristics. It is implemented on an actual hybrid microgrid considering multiple RES-based energy generation and storage options for expected and unexpected weather conditions.

Distributed power systems are considered to be a key element of future power grids. Their main characteristic is the local production and consumption of energy that is accomplished using a number of power converters that interconnect local sources and loads. In this paper, a current mode controlled dc-dc converter which feeds another current mode controlled dc-dc converter is considered to meet the demands of the load. It shows that the existence of the second converter at the output has destabilising effect on the overall system. Such a system may also exhibit interactions of various types of instability that conventional modeling methods cannot predict. Slow-timescale oscillation in standalone switching converters is usually attributed to the occurrence of a Neimark-Sacker bifurcation of the fundamental periodic orbit of the discrete-time model of the system. But in the studied cascaded system, the underlying mechanism is quite different. This paper fully explains the mechanisms as well as the conditions of the occurrence of such instabilities by employing the analytical and numerical tools of the exact switched model of the system. These results can be useful for developing design guidelines to avoid such problems. Finally, the results have been validated experimentally.

With the evolution of smart grids, penetration of distributed energy resources (DERs) in the distribution networks has become ever-increasing problem. To improve network reliability, the complexity of the two important aspects of adequacy and security must be well assessed. There is a trade-off between adequacy of DERs, and the distribution network security, i.e. improving the adequacy can reduce the security. In this study, enhancement of the distribution network adequacy and security is proposed. In this regard, capacity of simultaneous reconfiguration and DERs sizing are utilised to improve the adequacy and security of an active distribution network. In the reconfiguration process, graph theory concept is adopted to implement a fast reconfiguration method. Since DERs are active, a combined bus and line security index is used to overcome security concerns of their existence. The IEEE 33-bus distribution network as a widely used standard test system in reconfiguration studies, and a practical 83-bus distribution network of Taiwan Power Company (TPC) which is a part of a real distribution network, are used to test the performance of the proposed method. The simulation results demonstrate the performance of the proposed framework.

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems' utilization and overall process optimization. For this purpose, a flexible network-based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of Systems Sunlight S.A. facilities. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.

Resonant dc–dc converters have found increasing application in industry in recent times. Yet, the methods of dynamical analysis and parameter design for this kind of system are not well developed. The averaging method cannot be used in such converters as the small-ripple assumption does not hold. The sampled-data model, which seeks to obtain a closed form expression of the state at a clock instant in terms of that at the previous clock instant, also becomes unwieldy for converters with many topological modes – a condition prevailing in all resonant converters. In this study the authors show that the Filippov method can be effectively applied for accurate s-domain small signal analysis as well as time domain stability analysis by locating the stability boundaries in the paramater space for such systems. The authors apply this method to three classes of resonant converters – the switch resonant converter, the resonant transition converter and the load resonant converter – and present the mechanisms by which these converters may lose stability as the parameters are varied. The theoretical results corresponding to the resonant transition converter are validated experimentally.

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.

This work proposes the use of the power grand composite curves (PGCC) method to identify energy recovery targets in renewable energy smart grids and to adaptively adjust their operation in short-term energy requirements through appropriately selected power management strategies (PMS). A PMS is the sequence of decisions offering efficient utilization of resources and equipment to meet specific targets. The aim is to identify the appropriate PMS within recurrent subsequent time intervals that efficiently serves the desired operating goals in view of operating variability. This is approached by predicting the PGCC for a time horizon extending into the future. Subsequently, the PGCC is appropriately shifted to set a target for the minimum energy inventory needed by the end of the current interval for which decisions about the system operation are sought in order to satisfy the system operating goals. The target energy inventory is guaranteed in the current interval by selecting the PMS that best matches the identified target. A formal mathematical framework is presented, associating Pinch analysis with PMS within a generic model considering numerous structural and temporal grid interactions. The proposed method is implemented on an actual hybrid smart grid considering multiple RES-based energy generation and storage options.

A new self-compensation technique is proposed for eliminating subharmonic oscillation and chaotic regimes in dc–dc switching converters under peak current mode control. The proposed method extracts a control signal from the error between the inductor current and a suitable reference and does not require an external signal generator as in the conventional ramp compensation scheme. The analytical expressions of the control domain using Filippov method are obtained. Simulation results show that the proposed technique can be implemented using standard analogue devices and can effectively eliminate subharmonic and chaotic oscillations and can ensure a stable operation for a wide range of the duty ratio.

This paper proposes a new methodology for designing robust affine state-feedback control laws, so that wide-range safe and efficient operation of switched-mode DC–DC boost converters is guaranteed. Several undesirable nonlinear phenomena such as unstable attractors and subharmonic oscillations are avoided through bifurcation analysis based on the bilinear averaged model of the converter. The control design procedure also relies on constrained stabilization principles and the generation of safety domains using piecewise linear Lyapunov functions, so that robustness to supply voltage and output load variations is ensured, while input saturation is avoided and additional state constraints are also respected. The technique has been numerically and experimentally validated.

The use of photovoltaic panels has become very attractive in distributed power generation systems as they provide a clean and cheap form of energy. There are various converter topologies that are employed in order to connect these sources to the grid but almost always the main component is a DC–DC converter. Most readily available DC–DC converters are designed to work under a (nearly) constant voltage source and therefore their behaviors may not be as expected when connected to a variable current source like a photovoltaic panel. In fact, as it is reported in this paper, the behavior of the overall system (PV panel/DC–DC converter) can be drastically different from the desired one which may have a detrimental effect on the grid. As a case study, this paper explores the dynamics and stability of a boost converter that is fed from a photovoltaic panel under an ohmic load. All major control methods (peak/average current mode control, voltage mode control) are considered. We show with numerical, experimental and analytical results that the converter can behave unpredictably (or chaotically) when the output of the PV varies in response to the variation in solar radiation, and we report for the first time how the domain of stability in the parameter-space and the mechanisms of instability are affected by the characteristics of the photovoltaic source. The dynamical features are explored from circuit theory and nonlinear dynamics points of view. This knowledge will help in compensating for the aforementioned uncertainty and can be used to design converters that remain stable throughout the range of incident solar radiation and load values. The results have been experimentally validated.

Hybrid renewable power generation systems considering storage of energy and/or hydrogen involve considerable complexity as heterogeneous equipment need to be interconnected and activated or deactivated at different time instances. This work employs generic and flexible decision making models to develop complex power/hydrogen management strategies and investigate their performance on systems integrating multiple power generation and storage devices. These models significantly facilitate the analysis of both a standalone power generation and storage system forming a microgrid currently in operation as well as three interconnected microgrids of different functionalities considered in this work. These systems are used to investigate the performance of a large number of power management strategies developed using the proposed models.

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.

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.

The dynamical behaviour of a fuel cell feeding a boost converter is studied in this paper. A nonlinear model of the combined system is derived including the effect of the switching action of the converter. Using Filippov's theory, it is possible to analytically study the bifurcation patterns of the system and to demonstrate that the system loses stability through a period doubling bifurcation. To overcome this instability, we inject a high frequency sinusoidal signal into the system that forces the system to remain stable while at the same time retaining its basic slow scale properties (like the steady state error). This controller is simple to implement and does not require any special hardware. The stability analysis and new controller design method presented in this paper allow for the re-design of the converter to stabilize circuit operation with a substantially reduced inductor size, reducing the size and cost of the converter while maintaining its average currents and voltages and other circuit steady-state behaviour characteristics. The results are confirmed by using numerical and analytical tools.

An impact oscillator is a non-smooth dy- namical system with discontinuous state jumps whose dynamical behavior illustrates a variety of non-linear phenomena including a grazing bifurcation. This spe- cific phenomenon is difficult to analyze because it co- incides with an infinite stretching of the phase space in the neighborhood of the grazing orbit, resulting in the well-known problem of the square-root singular- ity of the Jacobian of the discrete-time map. A novel Takagi-Sugeno fuzzy model-based approach is pre- sented in this paper to model a hard impacting system as a non-smooth dynamical system including discon- tinuous jumps. Employing non-smooth Lyapunov the- ory, the structural stability of the system is analyzed to predict the onset of the destabilizing chaotic be- havior. The proposed stability results, formulated as a Linear Matrix Inequality (LMI) problem, demonstrate how the new method can detect the loss of stability just before the grazing bifurcation.

This paper presents an alternative technique for the adaptive control of power electronic converter circuits. Specific attention is given to the adaptive control of a dc-dc converter. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). The method is computationally efficient and based around a dichotomous coordinate descent (DCD) algorithm. The DCD-recursive least squares (RLS) algorithm has been employed as the adaptive PEF to reduce the computational complexity of existing RLS algorithms for efficient hardware implementation. Results show that the DCD-RLS is able to improve the dynamic performance and convergence rate of the adaptive gains (filter taps) within the controller. In turn, this yields a significant improvement in the overall dynamic performance of the closed-loop control system, particularly in the event of abrupt parameter changes. The proposed controller uses an adaptive proportional-derivative+integral (PD +I) structure which, alongside the DCD algorithm, offers an effective substitute to a conventional proportional-integral-derivative (PID) controller. The nonadaptive integral controller (+I), introduced in the feedback loop, increases the excitation of the filter tap weight and ensures good regulation. The approach results in a fast adaptive controller with self-loop compensation. This is required to minimize the prediction error signal and, in turn, minimize the voltage error signal in the loop by automatically calculating the optimal pole locations. The prediction error signal is further minimized through a second-stage FIR filter (adaptation gain stage). This ensures that the adaptive gains converge to their optimal value. This paper presents detailed simulation analysis and experimental validation on a prototype synchronous dc-dc buck converter. The experimental results clearly demonstrate the superior dynamic performance and voltage regulation compared to conventional PID and adaptive LMS control schemes, with only a modest increase in the computational burden to the microprocessor.

It is known that the Jacobian of the discrete-time map of an impact oscillator in the neighborhood of a grazing orbit depends on the square-root of the distance the mass would have gone beyond the position of the wall if the wall were not there. This results in an infinite stretching of the phase space, known as the square-root singularity. In this paper we look closer into the Jacobian matrix and find out the behavior of its two parameters---the trace and the determinant, across the grazing event. We show that the determinant of the matrix remains invariant in the neighborhood of a grazing orbit, and that the singularity appears only in the trace of the matrix. Investigating the character of the trace, we show that the singularity disappears if the damped frequency of the oscillator is an integral multiple of half of the forcing frequency.

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.

A new method is described which considerably improves the performance of rotor flux model reference adaptive system (MRAS)-based sensorless drives in the critical low and zero speed regions of operation. It is applied to a vector-controlled induction motor drive and is experimentally verified. The new technique uses an artificial neural network (NN) as a rotor flux observer to replace the conventional voltage model. This makes the reference model free of pure integration and less sensitive to stator resistance variations. This is a radically different way of applying NNs to MRAS schemes. The data for training the NN are obtained from experimental measurements based on the current model avoiding voltage and flux sensors. This has the advantage of considering all drive nonlinearities. Both open- and closed-loop sensorless operations for the new scheme are investigated and compared with the conventional MRAS speed observer. The experimental results show great improvement in the speed estimation performance for open- and closed-loop operations, including zero speed.