Magnetic Design Aspects of Coupled-Inductor Topologies for Transient Suppression

Based on the discovery of the surge absorption capability of supercapacitors, a transient protector named supercapacitor-assisted surge absorber (SCASA) was designed and implemented in a commercial device. Despite its simplicity, the circuit topology consisted of a coupled inductor wound around a specially selected magnetic core. This paper elucidates the design aspects of SCASA coupled-inductor topologies with a special focus on the magnetic action of core windings during transient propagation. The non-ideal operation of the SCASA transformer was studied based on a semi-empirical approach with predictions made by using magnetizing and leakage permeances. The toroidal flux distribution through the transformer was also determined for a 6 kV/3 kA combinational surge, and these findings were validated by using a lightning surge simulator. In predicting the possible effects of magnetic saturation, the hysteresis properties of different powdered-iron and ferrite core types were considered to select the optimal design for surge absorption. The test results presented in this research revealed that X-Flux powdered-iron toroid and air-gapped EER ferrite yielded exceptional performance with ∼10% and ∼20% lower load–voltage clamping compared to that of the existing Kool μu design. These prototypes further demonstrated a remarkable surge endurance, withstanding over 250 consecutive transients. This paper also covers details of three-winding design optimizations of SCASA and LTSpice simulations under the IEC 61000/IEEE C62.45 standard transient conditions.</jats:p

Investigating the impact of ferrite magnetic cores on the performance of supercapacitor assisted surge absorber (SCASA) technique

Supercapacitor assisted surge absorber (SCASA) is a patented technique developed by the University of Waikato. One noticeable attribute of this design is the inclusion of a coupledinductor which improves its capability of surge absorption. This paper mainly focuses on investigating the usability of ferrite iron for the core of the coupled-inductor, and attempts to explain how to minimize the effects of a negative voltage peak that arise during SCASA operation. Four ferrite cores with different geometries and material compositions (W-ferrite and J-ferrite) are subjected to 6.6 kV surge hits. Experimental outcomes demanded the need of inserting air-gaps inside these ferrite toroids. High magnetic permeability of ferrite results in a low energy storage capability; this limits their suitability in surge absorption related applications. To overcome the issues of high permeability we modified the cores with thin cuts through the surfaces. Experimental work is facilitated by lightning surge simulators (LSS-6110 and LSS-6230) coupled with the utility main to generate surge waveforms defined by the IEEE C62.41. The analysis of test results encourages us to justify the gappedcore approach, and to further verify, performance of SCASA is empirically compared for both powdered-iron cores and modified ferrite cores using international protocols of UL-1449

Importance of Leakage Magnetic Field and Fringing Flux in Surge Protector Design

Transient surge absorption capability of su percapacitors is practically implemented in a commercial surge protector known as supercapacitor assisted surge absorber (SCASA). It is a low component count high per formance circuit design which utilizes a coupled inductor topology wound to a powdered-iron magnetic core. This paper investigates non-ideal characteristics of the SCASA transformer designed using various air-gapped ferrites such as manually gapped toroids and mass-produced com mercially available EER cores. Emphasis is given to exam ine surge energy losses associated with leakage magnetic field and fringing flux of gapped transformer prototypes. In predicting effects of an air gap in ferrite materials, an analytical approach based on effective-permeance is used with validations based on SCASA inductance properties. Experimental work presented in this paper are carried out using a Noiseken lightning surge simulator adhering to IEC 60038 and IEEE C62.41 standards. In addition, SCASA pro totypes were subjected to surge immunity tests specified by UL-1449 Underwriters’ laboratory procedures, where a 10% reduction of load-voltage was recorded outperforming the present design

Permeance based model for the coupled-inductor utilized in the supercapacitor assisted surge absorber (SCASA) and its experimental validation

Transient-surge absorption capability of small/low cost supercapacitors (SCs) is already published. SCASA is a patented technique that led to the development of a high performance commercial surge protector which adheres to UL-1449 3rd edition test protocols. The commercial implementation comprises a coupled-inductor, two metal oxide varistors (MOVs) and a SC sub-circuit. This paper presents a permeance based model for the coupled-inductor of SCASA topology in predicting its operation under contrasting voltage conditions. In validating the circuit operation with regard to its surge absorption capability versus 50 Hz AC power transfer, a lightning surge simulator (LSS-6230) was utilized. We discuss this comparison based on the standard IEEE C62.41 surge waveforms up to a maximum of 6.6 kV

Optimization of Supercapacitor Assisted Surge Absorber (SCASA) Technique: A New Approach to Improve Surge Endurance Using Air-Gapped Ferrite Cores

SCASA is a patented technique commercialized as a surge protector device (SPD) that adheres to UL-1449 test standards. Apart from the novel use of supercapacitors, SCASA design incorporates a coupled-inductor wound to a specially selected magnetic material of powdered-iron. In this study, we investigate the limitations of the present design under transient operation and elucidate ways to eliminate them with the use of air-gapped ferrite cores. In modelling the operation under 50 Hz AC and transient conditions, a permeance-based approach is used; in addition, non-ideal characteristics of the transformer core are emphasized and discussed with empirical validations. The experimental work was facilitated using a lightning surge simulator coupled with the 230 V AC utility mains; combinational surge-waveforms (6 kV/3 kA) defined by IEEE C62.41 standards were continuously injected into SPD prototypes during destructive testing. Such procedures substantiate the overall surge-endurance capabilities of the different core types under testing. With regard to optimizations, we validated a 95% depletion of a negative-surge effect that would otherwise pass to the load-end, and another 13–16% reduction of the clamping voltage verified the effectiveness of the methods undertaken. In conclusion, SCASA prototypes that utilized air-gapped cores revealed a greater surge endurance with improved load-end characteristics.</jats:p

Supercapacitor assisted surge absorber (SCASA) technique: selection of magnetic components based on permeance

Supercapacitors help building long time constant resistor-capacitor circuits. This property helps them withstand high voltage transient surges and dissipate transient energy in the resistive element of the circuit without exceeding the supercapacitor’s DC voltage rating, which is usually between 2.5 to 4 V. SCASA is a patented technique, which was commercialized within the last five years. Successful implementation of this circuit topology, despite its simplicity, is quite dependent on the selection of the core of the coupled inductor utilized. This paper provides the essential details of the process of selecting the core for the magnetic component required, with a brief comparison of SCASA technique with a traditional surge protector, without any supercapacitors

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time

Studying the effects of thalamic interneurons in a thalamocortical neural mass model

Neural mass models of the thalamocortical circuitry are often used to mimic brain activity during sleep and wakefulness as observed in scalp electroencephalogram (EEG) signals [1]. It is understood that alpha rhythms (8-13 Hz) dominate the EEG power-spectra in the resting-state [2] as well as the period immediately before sleep [3]. Literature review shows that the thalamic interneurons (IN) are often ignored in thalamocortical population models; the emphasis is on the connections between the thalamo cortical relay (TCR) and the thalamic reticular nucleus (TRN). In this work, we look into the effects of the IN cell population on the behaviour of an existing thalamocortical model containing the TCR and TRN cell populations [4]. A schematic of the extended model used in this work is shown in Fig.1. The model equations are solved in Matlab using the Runge-Kutta method of the 4th/5th order. The model shows high sensitivity to the forward and reverse rates of reactions during synaptic transmission as well as on the membrane conductance of the cell populations. The input to the model is a white noise signal simulating conditions of resting state with eyes closed, a condition well known to be associated with dominant alpha band oscillations in EEG e.g. [5]. Thus, the model parameters are calibrated to obtain a set of basal parameter values when the model oscillates with a dominant frequency within the alpha band. The time series plots and the power spectra of the model output are compared with those when the IN cell population is disconnected from the circuit (by setting the inhibitory connectivity parameter from the IN to the TCR to zero). We observe (Fig. 2 inset) a significant difference in time series output of the TRN cell population with and without the IN cell population in the model; this in spite of the IN having no direct connectivity to and from the TRN cell population (Fig. 1). A comparison of the power spectra behaviour of the model output within the delta (1-3.5Hz), theta (3.75-7.5Hz), alpha (7.75-13.5Hz) and beta (13.75-30.5Hz) bands is shown in Fig. 2. Disconnecting the IN cell population shows a significant drop in the alpha band power and the dominant frequency of oscillation now lies within the theta band. An overall ‘slowing’ (left-side shift) of the power spectra is observed with an increase within the delta and theta bands and a decrease in the alpha and beta bands. Such a slowing of EEG is a signature of slow wave sleep in healthy individuals, and this suggests that the IN cell population may be centrally involved in the phase transition to slow wave sleep [6]. It is also characteristic of the waking EEG in Alzheimer’s disease, and may help us to understand the role of the IN cell population in modulating TCR and TRN cell behaviour in pathological brain conditions

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep