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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.

This paper introduces a novel technique for online system identification. Specific attention is given to the parameter estimation of dc-dc switched-mode power converters; however, the proposed method can be applied to many alternative applications where efficient and accurate parameter estimation is required. The proposed technique is computationally efficient, based on a dichotomous coordinate descent algorithm, and uses an infinite impulse response adaptive filter as the plant model. The system identification technique reduces the computational complexity of existing recursive least squares algorithms. Importantly, the proposed method is also able to identify the parameters quickly and accurately, thus offering an efficient hardware solution that is well suited to real-time applications. Simulation analysis and validation based on experimental data obtained from a prototype synchronous dc-dc buck converter is presented. Results clearly demonstrate that the estimated parameters of the dc-dc converter are a very close match to those of the experimental system. The approach can be directly embedded into adaptive and self-tuning digital controllers to improve the control performance of a wide range of industrial and commercial applications.

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.

The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability.

Higher order converters, without an external voltage control loop, have been known not to exhibit a fast-slow scale bifurcation interaction, as the slow-scale bifurcation has generally been believed to be caused by parameter changes in the outer loop. In this paper, it is shown that a current-mode controlled Cuk converter can exhibit an interaction between fast-scale and slow-scale bifurcations even in the absence of this closed outer voltage control loop. The phenomenon is probed using an approach based on the system's monodromy matrix that does not only predict this instability but also provides a systematic method for the development of new control strategies to avoid the onset of this bifurcation. Analytical and numerical results prove that the new controller greatly extends the stable region of operation of the converter.

The current-mode controlled Cuk converter, being a fourth-order nonlinear nonsmooth system, does not lend itself to any simple analysis. The difficulty lies mainly in the complex modeling of the circuit needed to capture all the essential nonlinearities that occur in fast time scale such as border-collision bifurcation and chaos. This paper extends the Takagi-Sugeno fuzzy modeling approach to capture these fast time-scale nonlinearities. Non-smooth Lyapunov theory is employed to study the stability of the system and to locate the operating point at which the converter loses its stable period-1 operation.

One common problem in drives applications is the presence of noise that corrupts the useful information in measurements such as of current, due to sensor imperfections. Digital low pass filters are a solution to the problem but they cannot cope when the useful information has time varying high frequency characteristics. In this paper, wavelet analysis, seldom used as yet in electric drives, is analysed and compared to classical methods. The key points of wavelet analysis are presented in a way that is appropriate for drives. Application of this new method to a typical practical current signal demonstrates the advantages and limitations of these methods over more conventional techniques. The true power of the wavelet transform is revealed when it is applied to a speed estimation problem where the rotor speed of a permanent magnet machine is modulated and coupled with high frequency components.

This paper describes some of the preliminary outcomes of a UK project looking at control education. The focus is on two aspects: (i) the most important control concepts and theories for students doing just one or two courses and (ii) the effective use of software to improve student learning and engagement. There is also some discussion of the correct balance between teaching theory and practise. The paper gives examples from numerous UK universities and some industrial comment.

The scope of this work is to present the design and development of an embedded supervisory automation framework for hybrid hydrogen-enabled power generation systems. More specifically, the hybrid system is responsible for the charging of battery stacks and it relies on a High Temperature Polymer Electrolyte Membrane (HT-PEM) fuel cell system and an LPG reformer. The HT-PEM fuel cell is used as a primary power source for the charging of a Li-Ion battery stack. The necessary hydrogen is provided either by a renewable hydrogen production station or from an LPG reformer, which are both, integrated and operated by CERTH. Overall the applied methodology resulted to an automated system that can provide power upon-demand.

Dry-friction oscillators are mechanical systems with dry friction and stick-slip vibrations. In the context of control theory, the stability analysis of this type of dynamical systems is important since they exhibit nonsmooth bifurcations, or most famously a sliding–grazing bifurcation inducing abrupt chaos. This paper develops a Lyapunov-based framework to study the so-called structural stability of the system, predicting the onset of such unique bifurcations. To achieve this, the nonlinear system is first represented as a nonsmooth Takagi–Sugeno (TS) fuzzy model, and the structural stability is then formulated as linear matrix inequalities (LMI) feasibility problems with less conservative formulation. Solving the resulting LMI problem, the onset of sliding–grazing bifurcation can be accurately predicted.

This work presents the design and development of a supervisory control methodology for the management of energy decisions in a hybrid powered system. More specifically the methodology implements an energy management strategy (EMS) which is applied in a fuel cell – battery system for vehicular applications and explores the behaviour of the system under a realistic operation scenario. The EMS relies on a Hybrid Timed Automaton (HTA) combined with propositional rules and formalizes the operation of the system as it thoroughly describes the dynamic transitions between each operating state. A sensitivity analysis reveals the influence of the power distribution between the two energy sources and an optimization study determines the values of the selected operating parameters. Finally the response of the Fuel Cell Electric Vehicle (FCEV) system is assessed through a combined urban and high-way driving cycle.

Hybrid energy smart grids operate based on power management strategies (PMS) addressing the time-varying reallocation of resources to meet specific targets. The identification of the PMS that best exploits the weather variability is crucial for their efficient operation. In this work, this is approached within a framework which exploits the Power Grand Composite Curves (PGCC) method to perform two functions: a) the identification of renewable energy recovery targets within a short term prediction horizon, and b) the temporal reallocation of the grid subsystems within a control horizon based on the PMS that best matches the identified targets. The PGCC is exploited within a model predictive control framework that enables the satisfaction of the system operating goals by ensuring the maintenance of a targeted minimum energy inventory. The desired energy inventory is guaranteed by selecting the appropriate PMS during the control interval that best matches the identified target. The method is presented within a formal mathematical framework and illustrated for a smart grid with power and hydrogen generation and storage features in year-round operation.

In this paper a Recurrent Neural Network (RNN) for solar radiation prediction is proposed for the enhancement of the Power Management Strategies (PMSs) of Hybrid Renewable Energy Systems (HYRES). The presented RNN can offer both daily and hourly prediction concerning solar irradiation forecasting. As a result, the proposed model can be used to predict the Photovoltaic Systems output of the HYRES and provide valuable feedback for PMSs of the understudy autonomous system. To do so 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. As a result, the RNN after training with meteorological data of the aforementioned area is applied to the specific HYRES and successfully manages to enhance and optimize its PMS based on the provided solar radiation prediction.

A method for reducing the size of filter inductance in DC-DC converter circuits based on Filippov’s theory is presented in this paper. In this method, the state transitional matrix of the system over one complete switching cycle, including the state transitional matrices across the converter switching events, is used to stabilize converter operation with a substantially reduced inductor size while maintaining circuit average currents and voltages. An analysis of circuit conduction losses shows that losses are not significantly affected by higher inductor current ripple resulting from the use of the smaller inductor. The switching frequency of the converter will not vary or change compared to a conventionally designed DC-DC converter. The new control/design method is demonstrated using an experimental voltage-mode-controlled buck converter.

In this paper, Particle Swarm Optimization (PSO) is employed to size and locate Distributed Generation (DG) in a distribution network to provide a more effective loss reduction and voltage support. Unlike most previous studies, DG nodes are modelled as generator (PV) nodes in this study. Three network scenarios are considered: a network with no generation but with reactive power compensation capacitors, the same network with the inclusion of a DG and the network operating with DG but no capacitive VAr compensation. Scenario one network is considered as the base case. For scenario two and three network, two cases are considered. The first involves the integration of one generator of optimal size and at an optimal location to minimise network losses. The second case considers the optimisation of the size of a single generator connected at the minimum voltage bus. The study is carried out on a 16-bus radial distribution network. The results show that DG is effective in minimizing network power loss and providing ancillary service of voltage support when operated to inject real power and regulate the node voltage to which it is connected. Results of the optimization process show that the simultaneous application of shunt capacitors and DG produced the best loss reduction and network voltage profile. (4 pages)

This paper presents a predictive PID controller for DC-DC converters using an adaptive prediction error filter (PEF) in the controller feedback loop. The concept of using an adaptive PEF is clearly described, and mathematical analysis of central concepts, such as the Auto-Regressive (AR) process generator and Moving Average (MA) identifier are explained in detail. The direct relationship between an adaptive PEF and a digital Proportional-Derivative (PD) controller is also presented. This leads to the development of an adaptive Proportional-Derivative + Integral (PD+I) control structure which is an effective substitute to a conventional PID controller. Experimental validation shows that the approach results in a fast adaptive controller with self-loop compensation. (6 pages)

The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. This is mainly caused by the feedback only being available from the motor encoder, with no measurement of the actual vehicle speed. In this paper it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. This has been realised using a Kalman Filter (KF) and a Recursive Least Squares (RLS) estimator, and validated with experimental results. (6 pages)

Fuel Cells (FCs) are part of the most promising and environmentally friendly technologies that have attracted the attention of both industrial and basic research in the recent years. Power conversion systems should be developed in order to apply FCs on different applications such as electric vehicles and meet variable power demands. The produced voltage and current at the FC output is not constant. In most typical applications the FC feeds a boost converter which must be properly designed. In this paper, the nonlinear dynamics of the FC are combined with the non-smooth properties of the DC-DC converter resulting in a complicated, nonlinear, 3rd order piece-wise smooth model which is used for the proper design of the converter. The results clearly show that the combined system has a stable and fast response. Using Filippov's theory the stability of the system is guaranteed even when the inductance is reduced which makes the overall system more efficient, cheaper and lighter.

In this paper an experimental evaluation of a novel Model Reference Adaptive System (MRAS) speed observer for induction motor drives based on stator currents is presented. In this scheme the measured stator currents are used as the reference model for the MRAS observer to avoid the use of a pure integrator. A two-layer Neural Network (NN) stator current observer is used as the adaptive model which requires the rotor flux information that can be obtained from the current model. Speed estimation performance of the new MRAS scheme is studied and compared with the classical rotor flux MRAS when applied to an indirect vector control induction motor drive. Experimental results are shown for the two schemes in the low speed region of operation including tests for the regenerating mode. These results complement the simulation results presented for the proposed scheme in a recent work.

Dynamical system theory has proved to be a powerful tool in the analysis and comprehension of a diverse range of problems. Over the past decade, a significant proportion of these systems have been found to contain terms that are non-smooth functions of their arguments. These problems arise in a number of practical systems ranging from electrical circuits to biological systems and even financial markets. It has also been demonstrated that Fuzzy engineering can be effectively employed to identify or even predict an array of uncertainties and chaotic phenomena caused by discontinuities typical of this class of system. This chapter presents a review of the most recent developments concerned with the confluence of these two fields through real-life examples and current advances in research.

Synchronous machines are the most widely used electrical machine in power generation. Identifying the parameters of these machines in a non invasive way is very challenging due to the inherent nonlinearity of machine performance. This paper proposes a synchronous machine parameter identification method using particle swarm optimization (PSO) with a constriction factor. PSO is an intelligent computational method based on a stochastic search that has been shown to be a versatile and efficient tool for complicated engineering problems. A modified version of PSO allows a synchronous machine model output to be used as the objective function, thus allowing a new, more efficient method of parameter identification. This paper highlights the effectiveness of the proposed method for the identification of synchronous machine model parameters, using both simulation and manufacturers measured experimental data.

Synchronous machines are the most widely used machines in power generation. Identifying their parameters in a non invasive way is very challenging due to the inherent nonlinearity of machine performance. This paper proposes a synchronous machine parameter identification method using particle swarm optimization (PSO) with a constriction factor. The PSO allows a synchronous machine model output to be used as the objective function to give a new, more efficient method of parameter identification. This paper highlights the effectiveness of the proposed method for the identification of synchronous machine model parameters, using both simulation and manufacturers measured experimental data. The paper will also consider the effectiveness of the method as the number of parameters to be identified is increased. (4 pages)

The presence of saliency/anisotropy in a Permanent Magnet Synchronous Machine (PMSM) is essential to track the rotor position when using high frequency signal injection based sensorless control. At high frequency d-q incremental inductances become dominant over the d-q normal (chord) inductances. In addition when load is applied to the machine cross-coupling inductance arise since the saliency/anisotropy angle, which is synchronised with the rotor position at no and light load applications, is delayed from the actual rotor position. To estimate accurately the rotor position, cross-coupling and self incremental inductances have to be known at various operating points. In this paper online inductances determination technique based on the Goertzel algorithm is proposed to compensate for the position error. Analytical interpretation and simulation results are shown to validate the proposed technique.

Teaching control systems in Newcastle University, at the School of Electrical, Electronic and Computer Engineering, is presented in this paper. The design, delivery and assessment of the module are described. The use of interactive teaching methods that are employed is thoroughly discussed and the teaching experience of the students is presented through their feedback questionnaires.

Fast-scale instabilities in full-bridge converter dc drives are analysed in this paper using Filippov's method of differential inclusions. This method is very suitable for stability analysis of systems with switching nonlinearities and has been successfully applied to mechanical switching systems and dc-dc converter circuits. This paper presents the first application of the method in a variable speed drive application. By obtaining the state transition matrix over one complete cycle we were able to determine analytically the parameter values at which the nominal period-1 orbit loses stability. Based on the structure of the state transition matrix across the switching manifold (the saltation matrix) an optimal controller was developed capable of extending the parameter range for safe period-1 operation. (6 pages)

The classical Recursive Least Squares (RLS) algorithm is used in many adaptive control strategies. Unfortunately, these schemes often require significant signal processing and microprocessor hardware to implement. This can be a problem in cost sensitive systems. For this reason, this paper proposes the application of 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 method is computationally efficient and based around a Dichotomous Coordinate Descent (DCD) algorithm. The proposed controller uses an adaptive PD+I structure which, alongside the DCD algorithm, offers an effective alternative to a conventional Proportional-Integral-Derivative (PID) controller.

This paper presents a new control method developed to enhance the performance of distributed static series compensator (DSSC) employed as a FACTS device on an 11KV distribution line. The new feedback based control strategy, based on the calculation of instantaneous power flows, is used to grantee an orthogonal injected voltage with respect to the line current while minimizing the exchange of active power between the DSSC device and the power system. (5 pages)

System identification has an important role in control system design, particularly in auto-tuning and adaptive control applications. Recently, it has emerged as a significant research topic in power converter design. For this reason, this paper proposes a new on-line approach to identify the parameters of a dc-dc converter. The proposed method is capable of rapidly detecting and accounting for abrupt load changes during transient periods. In most situations the controller is blind to this change. However, the estimation algorithm is able to update the model parameters before the output reaches the peak overshoot value. As a result, the controller can minimise the effect of any parameter change. The proposed method depends on prediction error variation during a rapid change of load and is designed around a simple fuzzy logic structure. An adaptive forgetting factor is used to optimise the identification process, and this varies very quickly and smoothly with the step load change. Simulation results show that the identification model matches the plant during the transient period. Importantly, the convergence rate and parameters error, the two factors use to validate the algorithm, are very good during system startup and after any abrupt load change.

The behaviour of searching maximum power point tracking (MPPT) algorithms utilizing direct duty ratio perturbation control is shown to exhibit nonlinear phenomena, including slow scale bifurcations and chaos like behaviour, when applied to a dc photovoltaic (PV) system. The performance characteristics of a dc photovoltaic water pumping system, in which the searching MPPT algorithm is applied at the frequency of the PWM signal of the converter, are examined. The nonlinear behaviour of the system is analyzed and the effects of different parameters on system performance are discussed.

This paper presents a novel model reference adaptive system (MRAS) speed observer for induction motor drives based on stator currents. The measured currents are used as reference model for the MRAS observer to avoid the use of a pure integrator. A two layer Neural Network (NN) stator current observer is used as the adaptive model which requires the rotor flux information. This can be obtained from the voltage or current model but instability and dc drift can downgrade the overall observer performance. To overcome these problems another off-line trained multilayer feedforward NN is proposed here as a rotor flux observer. Speed estimation performance of the MRAS scheme using the three different rotor flux observers is studied and compared when applied to an indirect vector control induction motor drive. Promising results have been obtained when using the NN flux observer with less sensitivity to parameter variation and stability in the regenerating mode of operation.

An autonomous Cuk converter controlled by a hysteresis current controller is studied in this paper. Filippov's method is employed for the first time to investigate the stability of an autonomous power converter. The Neimark bifurcation, via which the converter loses its stabile period one operation, was stabilized by applying two alternative control strategies based on the analysis of the behaviour of the complex eigenvalues of the monodromy matrix of the system.

This paper proposes a novel adaptation mechanism to replace the classical PI controller used in model reference adaptive speed estimation schemes which are based on rotor flux. The proposed adaptation mechanism is based on a 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 fuzzy logic adaptation mechanism in both modes of operations.

The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.

The nonlinear behavior of a self-excited, smooth air gap, cage induction generator feeding an inductive load is analyzed in this paper, allowing for the effects of machine saturation. The self autonomous system is shown to exhibit a transition from a periodic orbit to a quasi-periodic orbit through a Neimark bifurcation.

This paper presents the application of the wavelet transform in electric drives schemes. Wavelets can significantly improve the behavior of a Kalman filter used for speed estimation. Furthermore an adaptive multiresolution wavelet analysis method is proposed for current denoising especially targeted for electric drives applications that have a V/f structure. This adaptive method compensates for the delay problem that every wavelet based denoising scheme imposes.

This paper presents a novel neural network-based flux observer to solve the low speed problems associated with a model reference adaptive speed estimation scheme which is based on rotor flux. A multilayer feedforward artificial neural network is proposed for rotor flux estimation which is more robust to noise and stator resistance variation and does not have dc-drift problems which are usually associated with these adaptive schemes. A comparison between the performance of the neural network based strategy and conventional scheme is carried out using a validated simulation of an indirect vector controlled induction motor drive working at a low speed.

Intermittent chaotic phenomena can appear in a dc-dc step down converter when the later is coupled with spurious high frequency signals. It is therefore important to understand why and how these phenomena occur and what is their relationship with the frequency characterises of the spurious injected signals. This paper uses a fast wavelet transform to isolate the frequency of these signals even when the converter is in a chaotic or subharmonic mode of operation.

The bifurcation behavior of switching power electronic converters can be altered by appropriately changing the switching manifold. This paper examines this bifurcation control strategy and proposes new ways in which it can be implemented. A detailed comparison which includes transients as well as steady state properties is also presented. This method can be applied to control any piecewise smooth system to stabilize a desired periodic orbit.

The appearance of nonlinear phenomena like bi-furcations and chaos in dc-dc converters are mainly studied by using the Poincare map of the system. This paper presents an alternative method based on the eigenvalues of the state transition matrix over one full cycle which provides better insight of the system and its stability properties. The paper shows how the state transition matrix for a full cycle can be applied to a wide class of power electronic circuits to investigate the stability of various limit cycles and offers considerable advantages over other convectional methods without increasing the complexity of the analysis. Another advantage of this method is its ability to explain and predict the length of intermittent subharmonic phenomena which occur when these converters are coupled with spurious signals.

High conversion ratio switching converters are used whenever there is a need to step-up dc source voltage level to a much higher output dc voltage level such as in photovoltaic systems, telecommunications and in some medical applications. A simple solution for achieving this high conversion ratio is by cascading different stages of dc-dc boost converters. The individual converters in such a cascaded system are usually designed separately applying classical design criteria. However these criteria may not be applicable for the complete cascaded system . This paper first presents a glimpse on the bifurcation behavior that a cascade connection of two boost converters can exhibit. It is shown that the desired periodic orbit can undergo period doubling leading to subharmonic oscillations and chaotic regimes. Then, in order to simplify the analysis the second stage is considered as constant current sink and design-oriented analysis is carried out to obtain stability boundaries in the parameter space by taking into account slope interactions between the state variables in the two-different stages.