Search

This paper presents a self-identification approach for reluctance synchronous machines (RSMs) in combination with analytical flux linkage prototype functions. Due to the severe magnetic saturation, good machine knowledge is required for high-performance electrical drive systems. The proposed self-identification method injects bipolar voltage pulses to an inverter-fed machine at standstill, without additional test equipment and requirements. Within an extremely short time, nonlinear flux linkages, including the magnetic saturation and cross-coupling effects, can be obtained. Instead of saving the samples as lookup tables (LUTs), they are approximated by analytical flux linkage prototype functions, which are parametrized by few parameters, and continuously differentiable throughout the whole operation range. The effectiveness of the developed self-identification method is experimentally validated for a nonlinear 4.0kW RSM. The results prove (i) the simple and time-saving procedure of the self-identification method, (ii) the high approximation accuracy and (iii) the benefits of the flux linkage prototype functions for real-time applications.

A generic current control system for modular multilevel cascade converters (MMCCs) is proposed. It is based on (i) input/output-linearization (I/O-linearization), (ii) feedforward disturbance compensation and (iii) parallelized proportional-integral-resonant controllers to compensate for arbitrarily many harmonics (including positive and negative sequence). The controller design is performed in state space. It is generic and flexible and combines pole placement and LQR tuning. Moreover, it allows to take all system parameters such as cluster or output (grid) inductances or resistances individually into account during controller design and tuning. Moreover, the typically imposed simplifying assumptions on system dynamics (e.g., identical parameters) or operating conditions (e.g., balanced loading) can be overcome. The proposed control system is validated for a dual-star bridge-cell MMCC. The simulation results illustrate its excellent control performance under ideal but also non-ideal conditions, where conventional approaches usually fail.

With the decreasing number of directly grid-connected synchronous machines (SMs) forming the grid voltage, the risk of power system instability increases, especially regarding the grid frequency due to the decreasing power system inertia. Grid-forming (GFM) control of inverter based resources (IBRs) with inertia emulation capability is required to face these challenges and to guarantee reliable operation of the future power system. This paper proposes virtual synchronous machine (VSM) control for doubly-fed induction machine (DFIM) based wind energy conversion systems (WECS) that (i) enables feedforward torque control (FTC) and thus, does not degrade the WECS performance during normal operation, (ii) adapts the VSM damping based on the DFIM operating point, and (iii) protects the VSM against overloading to avoid loss of synchronism.

An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider measurable OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time).

Synchronous machines (SMs) like reluctance synchronous machines (RSMs) or interior permanent magnet synchronous machines (IPMSMs) are characterized by nonlinear stator flux linkages which are crucial for optimal control and operation management. This paper presents a novel disturbance observer that allows to estimate the nonlinear flux linkages online for any SM (with constant or no excitation). Moreover, a simple identification sequence with ramp-like voltage signals is proposed to extract key information of the nonlinear stator flux linkage maps within a short period of time and without the need of any controller, torque sensor or prime mover. Finally, the proposed observer with identification sequence is implemented and validated for a real and highly nonlinear IPMSM by realistic and detailed simulation results.

Reluctance synchronous machines (RSMs) are characterized by high nonlinearities, cross-coupling effects and high torque ripple. This paper presents a nonlinear machine model of three-phase RSMs based on a nonlinear electromagnetic model including iron losses and a novel (extended) torque equation. The γ-component is used to consider asymmetries. The novel torque model provides a precise approximation of the machine’s torque ripple. Iron losses are covered as well. The nonlinear machine model with extended torque equation is implemented based on current, angle and speed dependent flux linkage and iron loss maps. Finally, the nonlinear and extended machine model is validated by comparing with state-of-the-art results obtained by finite-element analysis (FEA).

Modular Multilevel Cascade Converter (MMCC) have achieved a great importance in high voltage converter applications. As they require a high amount of capacitors whose voltages must be known for the control of the MMCC, many voltage sensors are needed. This paper presents a nonlinear Lyapunov-based observer for cell voltage estimation. The proposed observer does solely rely on the knowledge of the phase currents i.e. no voltage sensors are required and the convergence of the estimator is shown with a stability proof by Lyapunov. The performance of the observer is validated simulatively using MATLAB/Simulink.

Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.

In this work, a novel three-phase grid inverter topology with split DC-link and LC-filter is discussed. A proportionalintegral (PI) state-feedback current and voltage controller is designed based on linear quadratic regulator (LQR) theory. The midpoint current ripple is suppressed by introducing the filter capacitor voltage as control target. Moreover, a 3-dimensional Space Vector Modulation (3D-SVM) for this topology is implemented, which allows for larger voltage amplitudes compared to conventional Pulse Width Modulation (PWM). Realistic simulation results show that the combination of PI state-feedback controller and 3D-SVM achieves faster and more accurate closedloop dynamics with less oscillations and better decoupling under varying and unbalanced grid conditions.

In order to analytically solve the optimal feed-forward torque control (OFTC) problem of induction machines (IMs), the unified theory for synchronous machine introduced in [1] is extended by considering relevant IM nonlinearities and incorporating stator and rotor copper losses. Instead of the well known Maximum Torque per (stator) Current (MTPC) operation strategy, Maximum Torque per (copper) Losses (MTPL Cu ) is realized and extended by the Maximum (rotor) Current (MC r, ext ) strategy due to stator and rotor current limitations. Modeling magnetic saturation and cross-coupling effects leads to a con-strained nonlinear optimization problem which is solved based on the idea of sequential quadratic programming (SQP). The second order Taylor approximations are formulated in implicit form as quadrics. Applying the Lagrangian formalism to the quadratic problem leads to analytical solution for the optimal rotor currents. For a doubly-fed induction machine (DFIM), a decision tree for optimal operation management is presented and the OFTC is validated in simulations for a real nonlinear IM.

The unified theory introduced in [1] allows to solve analytically the optimal feedforward torque control (OFTC) problem of anisotropic synchronous machines (SMs). In this paper, the theory is extended by considering relevant machine nonlinearities and incorporating copper and iron losses, thus minimizing the overall (steady-state) losses in the machine. Instead of the well known maximum torque per current (MTPC) operation strategy, maximum torque per losses (MTPL) is realized. The unified theory for the derivation of the analytical solution is briefly recapitulated. Moreover, current and speed dependent iron losses, as well as magnetic saturation and cross-coupling effects are considered. The resulting nonlinear optimization problem is solved via online linearization of the relevant expressions. The linearization is exemplified for flux linkages and machine torque, respectively. Furthermore, a decision tree is presented, which guarantees an optimal operation management and smooth transitions between all operation strategies such as MTPL, field weakening (FW), maximum current (MC) and maximum torque per voltage (MTPV). Finally, the extended unified theory is validated in simulations for a highly nonlinear SM.

ObjectiveTo analyse the remodelling and recovery of a relocated intravitreal injection (IVI) service with an adapted treatment regimen in a tertiary referral centre during a nationwide lockdown with initial cancellation of all non-emergency treatments caused by the COVID-19 pandemic.Methods and analysisFor this retrospective observational study at Hanusch Hospital, Vienna, between 16 March 2020 and 5 May 2020, we conducted an analysis of an appointment booking system based on prioritisation incorporating disease class, severity and fellow eye status by evidence-based impact on irreversible structural impairment and survey data from telephone interviews. Recapture time was defined as the time-to-discard the backlog of patients in need for treatment. Non-attendance was stratified as treatment refusal for personal reasons and non-attendance due to lockdown-related restrictions.ResultsOf the 1109 patients, 241 (21.7%) were considered as highly urgent, 269 (24.3%) as urgent, 402 (36.2%) as semiurgent and 197 (17.8%) as non-urgent. Recapture time was 15 days for highly urgent patients, 22 days for urgent patients, 43 days for semiurgent patients and 46 days for non-urgent patients. The proportion of patients who refused treatment due to personal reasons was 5.2%, with a mean age of 82.4 years; 29 patients (2.6%) could not attend due to lockdown-related restrictions.ConclusionBy streamlining treatment based on urgency as well as increasing the number of bilateral IVI, recapture time was fast. We could provide a safe treatment environment for healthcare professionals and patients after resetting the injection service outside of the hospital with increased levels of protection.

A unified method is presented which allows toestimate all harmonic components, DC-offset and fundamen-tal frequency in arbitrarily distorted single-phase grids us-ing a frequency-adaptive observer (FAO) consisting of modi-fied Second-Order Generalized Integrators (mSOGIs), a DC-Integrator (DCI) and a modified Frequency Locked Loop(mFLL). DCI and mSOGIs are tuned by pole placement which allows for an arbitrarily fast detection of DC-offset and harmonic components if the fundamental frequency is known. If the fundamental frequency must be estimated as well, a mFLL with Gain Normalization (GN), Rate Limitation (RL), Anti-Windup (AW) strategy and low-pass filters (LPF) must be employed. The effectiveness of the proposed FAO is validated by experimental results and its enhanced performance is shown by comparisonsto existing estimation methods.

A unified method is presented which allows toestimate all harmonic components, DC-offset and fundamen-tal frequency in arbitrarily distorted single-phase grids us-ing a frequency-adaptive observer (FAO) consisting of modi-fied Second-Order Generalized Integrators (mSOGIs), a DC-Integrator (DCI) and a modified Frequency Locked Loop(mFLL). DCI and mSOGIs are tuned by pole placement which allows for an arbitrarily fast detection of DC-offset and harmonic components if the fundamental frequency is known. If the fundamental frequency must be estimated as well, a mFLL with Gain Normalization (GN), Rate Limitation (RL), Anti-Windup (AW) strategy and low-pass filters (LPF) must be employed. The effectiveness of the proposed FAO is validated by experimental results and its enhanced performance is shown by comparisonsto existing estimation methods.

Purpose Epiretinal membranes (ERM) are macular disorders leading to loss of vision and metamorphopsia. Vitrectomy with membrane peeling displays the gold standard of care. Aim of this study was to assess risk factors for postoperative intraretinal cystoid changes in a study population randomized for balanced salt solution and air-tamponade at the end of surgery. Methods A prospective randomized study, including 69 eyes with idiopathic ERM. Standard 23-gauge three-port pars plana vitrectomy with membrane peeling, using intraoperative optical coherence tomography (OCT), was performed. Randomization for BSS and air-tamponade was performed prior to surgery. Results Best-corrected visual acuity improved from 32.9 letters to 45.1 letters 3 months after surgery. Presence of preoperative intraretinal cystoid changes was found to be the only risk factor for presence of postoperative intraretinal cystoid changes 3 months after surgery (p = 0.01; odds ratio: 8.0). Other possible risk factors such as combined phacoemulsification with 23G-ppv and membrane peeling (p = 0.16; odds ratio: 2.4), intraoperative subfoveal hyporeflective zones (p = 0.23; odds ratio: 2.6), age over 70 years (p = 0.29; odds ratio: 0.5) and air-tamponade (p = 0.59; odds ratio: 1.5) were not found to be significant. Conclusion There is strong evidence that preoperative intraretinal cystoid changes lead to smaller benefit from surgery.

Purpose: Preoperative and postoperative optical coherence tomography (OCT) of macular pathologies can be regarded as the gold standard diagnostic technique, providing detailed information on the microstructures of the macula for planning the surgical procedure and comparing improvements after surgery in the follow-up period. Intraoperative use of OCT is a novel application to support surgeons during macular surgery. The aim of this study was to examine the diagnostic precision of a microscope-integrated intraoperative spectral-domain OCT (i-OCT) device and compare imaging results to a stand-alone spectral-domain OCT (SD-OCT) device. Methods: This prospective study included 41 eyes of 41 patients scheduled for pars plana vitrectomy with membrane peeling due to an idiopathic epiretinal membrane (ERM). Intraoperative imaging with the i-OCT device was performed at the beginning of the surgery and compared to preoperative SD-OCT images. Results: Preoperative and intraoperative SD-OCT evaluations showed high intraobserver and interobserver reproducibility for the presence of ERM, lamellar macular hole, and vitreomacular traction. For intraretinal cystoid changes, intraobserver and interobserver reproducibility for both OCTs was rather poor, mainly due to microcystic changes. Conclusions: Intraoperative spectral-domain OCT offers high reproducibility regarding the visibility of ERM, lamellar macular holes, and vitreomacular traction. Microcystic changes cause discrepancies in interpretation, often simply diagnosed as retinal thickening.

Purpose: The aim of our study using an intraoperative optical coherence tomography (iOCT) device was to assess the rate of neurosensory elevation with resulting subfoveal and extrafoveal hyporeflective zones during membrane peeling and to evaluate a possible influence on postoperative outcomes. Procedures: This study included patients scheduled for pars plana vitrectomy with membrane peeling due to idiopathic epiretinal membrane. All patients underwent 23-G pars plana vitrectomy with iOCT-guided membrane peeling and were scheduled for follow-up examinations at 1 day, 5 days, and 3 months after surgery. Results: Among the 171 patients included, subfoveal and extrafoveal hyporeflective zones could be detected in 12 patients (7%). Follow-up at 3 months after surgery was available for 149 patients with a median improvement in best corrected visual acuity of +2 lines (IQR: +1 to +4 lines). There were no significant differences in outcomes between patients with and those without subfoveal/extrafoveal hyporeflective zones concerning visual acuity, macular thickness, occurrence of intraretinal cystoid changes, and postoperative hyporeflective zones. Conclusions: No significant differences in postoperative outcomes were observed between patients with and those without subfoveal/extrafoveal hyporeflective zones.

Background Treatment modalities in iris melanoma include excision, plaque radiotherapy, photon or proton beam therapy and enucleation. In extensive tumours and diffuse seeding, radiotherapy remains as an alternative to enucleation. Methods This study is a retrospective, consecutive, interventional, single-institutional case series. 54 patients with a diffuse and non-resectable iris melanoma diagnosed from September 1998 to June 2012 were included. A 68-megaelectron volt proton beam was used to treat the anterior segment with a total dose of 4×12.5 cobalt grey equivalent. The cases were evaluated for local tumour control, eye retention, functional outcome and local complications after treatment. Results During a mean follow-up of 62.7 months (median 54.8 months, range 5.5–159.6 months), local tumour control was achieved in 96.3% of the patients. Cataract and glaucoma were the main complications developing after irradiation in 42.6% and 55.6%, respectively. In 34 of 44 patients (77.3%) who underwent cataract removal, a visual acuity of 20/40 or better following surgery was preserved. Enucleation was performed in three patients. The reason was suspected tumour recurrence in one and glaucoma in two. Hepatic metastasis occurred in one patient. Conclusions As an alternative to enucleation, whole anterior segment fractionated proton beam radiotherapy offered excellent local tumour control in diffuse iris melanoma. Given the limited alternatives, the rate of complications appears acceptable and visual function could be preserved in the majority of the patients during follow-up.

BACKGROUND AND OBJECTIVE: The aim of this study was to examine the quality of intraoperative visualization of the posterior hyaloid, epiretinal membrane (ERM), inner limiting membrane (ILM), and hyporeflective subfoveal zone with a commercially available, microscope-integrated spectral-domain OCT setup (mi-SD-OCT) (Rescan 700; Carl Zeiss Meditec AG, Germany). PATIENTS AND METHODS: Twenty patients prospectively scheduled for pars plana vitrectomy with membrane peeling due to an idiopathic ERM were included. Standard 23-gauge, three-port pars plana vitrectomy with membrane peeling and staining of the ERM with a trypan blue-based chromovitrectomy dye was performed in all cases. Intraoperative SD-OCT was performed before and after peeling and visualization of the posterior hyaloid, ERM, ILM, and presence of subfoveal hyporeflective zones were examined. OCT follow-ups were performed 2 days and 3 months after surgery. The study was approved by the local ethics committee of the city of Vienna. RESULTS: Successful intraoperative visualization of ERM by mi-SD-OCT was possible in all cases. The posterior hyaloid and ILM could not be seen in the mi-SD-OCT scans, whereas an intraoperative subfoveal hyporeflective zone presented in 35% of cases. In 12.5% an independent subfoveal hyporeflective zone presented postoperatively. Visual acuity improved in 93.8% of patients after surgery. CONCLUSION: mi-SD-OCT appears to be a valuable tool for intraoperative visualization of the ERM and offers immediate visualization of retinal anatomy during peeling. Therefore, it adds to the understanding of intraoperative traumatic changes due to the peeling procedure. [ Ophthalmic Surg Lasers Imaging Retina . 2016;47:328–332.]

PURPOSE The aim of this study was to correlate different biometric dimensions of the eye as measured from ocular magnetic resonance imaging (MRI) scans to predict the lens diameter. METHODS High-resolution ocular MRI scans of 100 eyes of 100 patients were reviewed. Various anatomical variables of the eye such as the axial length, the globe diameter, and the lens dimensions were measured. Also, the distances between the ciliary sulcus and angle-to-angle were measured. A partial least square (PLS) regression model was built to analyze which variables influence the model regarding the lens dimensions. RESULTS Sixty-two eyes of 62 patients were included in the final analysis. The lens diameter ratio (horizontal to vertical) was 0.93 (SD: 0.04; 0.83-1.00). The partial least square regression showed a significant connection (P < 0.001) between the horizontal and vertical diameter. The partial least square regression model that included the globe diameter and the axis length resulted in the best prediction for the horizontal lens diameter. Similar to the horizontal lens diameter, globe diameter was the best predictor for the vertical lens diameter followed by the distance of the ciliary sulcus. White-to-white distance, distance of the ciliary sulcus, and axial eye length were found to have a high influence on the angle-to-angle distance. CONCLUSIONS The introduced models may serve as tools to predict the capsular bag biometry in a preoperative setting for cataract surgery or lens refilling procedures.

L j (t) is a martingale , as stated in Definition 2.2.8, under the T j forward measure \({\mathbb{Q}^{{T_j}}}\), where we use the shorthand notation L j (t) for L(t, T j−1, T j ), j=1,...,N. We assume that the differential of L j (t) follows the following driftless SDE: $${{d}}{{{L}}_j}{{(t) = }}{\sigma _{j}}{(t)}{{L}_{j}}{(t)d}{{W}^{j}}{(t)\; for\; t } \le {{T}_{{{j - 1}}}}$$ (3.1)

In this chapter, two calibration possibilities for the LMM are presented, where the first is the direct calibration to market data and the second is a Rebonato's popular linear exponential parametric function, see Brigo and Mercurio [2006]. To calibrate the LMM directly to market data, the volatility curve has to be "bootstraped", as L j (t) is modeled and therefore each caplet on its own.

This chapter presents the necessary foundations to understand the mathematical, financial and computational aspects behind this model. In the first section we start with simple interest rate necessities and go on to financial Derivatives which are necessary to understand to correctly calibrate and use the model for pricing. The section 2.2 starts with the most important aspects in stochastic calculus which is the key step to understand and work with stochastic differential equations. One additional topic in this section is the no-arbitrage Pricing which are a prerequisite to understand modern option pricing theory. The final section in this chapter gives the reader an overview about the computational aspects which are important to build this model.

The first Figure 5.1 shows the market cap volatility structure with the characteristic hump at the beginning where cubic spline interpolation has been used between the market volatility points. The dashed line is the stripped caplet volatility which is used to calibrate the libor market model for pricing caps.

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach., {"references":["Y. Le Cun, J. S. Denker, S. A. Solla, \"Optimal Brain Damage,\" in\nD. S. Touretzky, \"Advances in Neural Information Processing Systems,\"\nMorgan Kaufmann, 1990, pp. 598-605.","B. Hassibi, D. G. Stork, G. J. Wolff, \"Optimal Brain Surgeon and\nGeneral Network Pruning,\" IEEE International Conference on Neural\nNetworks, vol. 1, pp. 293-299, April 1993.","R. Reed, \"Pruning Algorithms - A Survey,\" IEEE Transactions on\nNeural Networks, vol. 4, no. 5, pp. 740-747, September 1993.","M. Attik, L. Bougrain, F. Alexandre, \"Optimal Brain Surgeon Variants\nFor Feature Selection,\" in IEEE Proceedings of the International Joint\nConference on Neural Networks, pp. 1371-1374, 2004.","O. De Jes'us, M. Hagan, \"Backpropagation Algorithms Through Time\nfor a General Class of Recurrent Network,\" IEEE Int. Joint Conf. Neural\nNetwork, Washington, 2001, pp. 2638-2643.","O. De Jes'us, M. Hagan, \"Forward Perturbation Algorithm For a General\nClass of Recurrent Network,\" IEEE Int. Joint Conf. Neural Network,\nWashington, 2001, pp. 2626-2631.","O. De Jes'us, \"Training General Dynamic Neural Networks,\" Ph.D.\ndissertation, Oklahoma State University, Stillwater, OK, 2002.","O. De Jes'us, M. Hagan, \"Backpropagation Algorithms for a Broad Class\nof Dynamic Networks,\" IEEE Transactions on Neural Networks, vol. 18,\nno. 1, pp. 14-27, January 2007.","M. Hagan, B. M. Mohammed, \"Training Feedforward Networks with\nthe Marquardt Algorithm,\" IEEE Transactions on Neural Networks, vol.\n5, no. 6, pp. 989-993, November 1994.\n[10] L.S.H. Ngia, J. Sj¨oberg, \"Efficient Training of Neural Nets for Nonlinear\nAdaptive Filtering Using a Recursive Levenberg-Marquardt Algorithm,\"\nIEEE Transactions on Signal Processing, vol. 48, no. 7, pp. 1915-1927,\nJuly 2000.\n[11] P.J. Werbos, \"Backpropagation Through Time: What it is and how to to\nit,\" Proc. IEEE, vol. 78, no. 10, pp. 1550-1560, 1990.\n[12] R.J. Williams, D. Zipser, \"A Learning Algorithm for Continually Running\nFully Recurrent Neural Networks,\" Neural Computing, vol. 1, pp.\n270-280, 1989.\n[13] O. Nelles, Nonlinear System Identification. Berlin Heidelberg New York:\nSpringer-Verlag, 2001.\n[14] D. Schr¨oder, Elektrische Antriebe - Regelung von Antriebssystemen.\n2nd edn., Berlin Heidelberg New York: Springer-Verlag, 2001.\n[15] K. S. Narendra, K. Parthasarathy, \"Identification and Control of Dynamical\nSystems Using Neural Networks,\" IEEE Transactions on Neural\nNetworks, vol. 1, no. 1, pp. 4-27, November 1990."]}