Integrated voltage regulator (IVR) based power delivery is becoming a necessity in high-performance computing systems. Embedded inductors are key components in such power delivery architectures. In this paper, novel package-embedded inductors using the dual-core spiral topology are designed and demonstrated for 48V-to-1V and 12V-to-1V power conversion applications. This topology consists of a spiral-shaped conductor winding layer sandwiched between dielectric and magnetic core layers. The conductor and dielectric layers separate the cores and act as an air gap for the magnetic flux of the spiral inductor. The air gap causes an increase in the saturation current performance of the inductor. In addition to the single spiral winding, two advanced spiral inductor configurations, namely two spiral windings in series and parallel, are explored. To enable design, monomial analytical expressions for inductance calculations are provided. To evaluate the performance of these designs, three inductors are fabricated with two different magnetic core materials and air gaps. For the large-signal characterization of the fabricated inductors, a current probe-based small-signal resistance scaling methodology is presented. Overall, packageembedded dual-core spiral inductors with a performance of 115 nH inductance with 5 A saturation current and 300 nH inductance with 3.5 A saturation current, occupying a 9 mm 2 area and having 75% to 80% efficiency, are demonstrated.

This document presents a simple method that allows the use of time-domain simulation to extract an AC small signal model of the power stage of a power converter. Such extraction can be important for the simulation of feedback loop gain and the stability of complex power converter topologies, such as resonant converters or converters with complex switch control. With the presented approach, a single simulation run in the time domain is sufficient to extract a small signal model, resulting in a time-efficient extraction process. The method is based on the step response simulation, where the input control signal source (duty cycle, switching frequency, etc.) is applied as a step function. The output parameter of the converter (output voltage, current, etc.) is recorded during the time-domain simulation and then transformed into a SPICE netlist using Laplace transform statements. The Laplace transform in s-domain is supported by most SPICE simulators, such as LTspice, PSpice, etc. The presented algorithm and model are automatically generated using a developed open-source software tool. Thus, the free software LTspice, along with the presented open-source tool, can be used as a free alternative to commercial SPICE programs like PSpice, PADS, Simetrix, MATLAB, etc.

Converting renewable electricity into green ammonia has emerged as a new solution for cross-sector decarbonization and an important source of flexibility for power systems. Power-to-Ammonia (PtA) plants are an integration of various electro-chemical-mechanical processes and multiple storage facilities. Optimal sizing of these modules is an important but challenging optimization problem at the engineering stage. It is a two-stage decision-making program, involving both discrete and continuous variables at two stages, and requiring a large number of scenarios to accurately capture the stochasticity in renewable energy generation. We formulate this problem as a general stochastic mixed-integer programming (SMIP). We use the Benders Dual Decomposition (BDD) method as an algorithmic framework for this problem, which enables scenario decomposition and computation parallelization. To reduce the duality gap of the BDD method, we derive a new type of optimality cut, the Strengthened Lagrangian Cut (SLC), based on dissimilarity random scenario bundling. We prove that the SLC is tighter than the Lagrangian cut, and the resulting master problem provides tighter lower bound and high-quality feasible solutions for upper bound calculations. We develop an Improved Benders Dual Decomposition (IBDD) method based on SLCs. Numerical results show that the traditional BDD method can produce acceptable results for the PV-powered PtA case but not for the wind-powered PtA case. In comparison, the IBDD method produces satisfactory results for both cases and outperforms the BDD method. The IBDD method is demonstrated to be capable of solving large-scale SMIPs (containing more than 95,000 mixed-integer variables and 141,000 constraints in the extensive form) by achieving low relative gaps (< 1%) within manageable computational time (< 1200s) on a standard Laptop.

As the volume of end-of-life lithium-ion batteries (LIB) used in electric vehicles increases, so does the demand for recycling to recover valuable active materials as secondary resources, reducing the need for primary raw materials and minimizing supply dependencies. In addition to pyro- and hydrometallurgy recycling processes, an increasingly examined approach is the direct recycling route, which involves disassembling battery systems into modules, modules to cells, and ultimately cells to electrodes. To minimize passive materials such as various metals from the housing, joints, arresters, plastics, foams, and adhesives in the subsequent crushed mass, the introduction of module disassembly processes into the existing LIB recycling process chain is promising for the resulting increase in recyclate purity. This study presents the conceptualization of a LIB module disassembly process chain which aims to elevate the depth of disassembly of LIB during early recycling stages. A reverse engineering approach is used to recover individual cells from two battery modules of one specific automotive manufacturer by removing their casing through milling and disconnecting the cells from the arresters. The experimental results are analyzed for influences on the disassembly possibilities for individual components as well as the interactions within the system, enabling the elaboration of a priority graph and a disassembly plan. In order to allow for scalable recycling process chains suitable for very high quantities, a concept for a fully automated and variant-compatible robot application based on the experiment is derived. Furthermore, the depth of disassembly is compared with the corresponding reduction of passive materials in the crushed mass. The findings indicate that several module components can be disassembled, resulting in cell recovery and a reduction of metal and plastic in the crushed mass by about 17 %, highlighting the importance of robotic disassembly in enhancing material separation efficiency during LIB recycling.

The rapid expansion of generative artificial intelligence (AI) technologies is projected to have a significant effect on electricity use in the global data center sector. Some earlier research has proposed using data centers for load-balancing the future power grid to allow higher integration of variable renewables. In this paper, we review the expected future electricity consumption of AI and evaluate AI data center’s potential roles in a clean energy system. Our work finds that the levelized cost of computing (LCOC) favors higher load factors and shows relatively low sensitivity to electricity price levels. Under our baseline cost assumptions, a baseload electricity price of $125/MWh benefits load factors higher than 64%, depending on market price conditions and variations. The findings reveal that a boom in AI energy use could drive significant baseload power demand in the future power system.

Transformer isolated, multi-output power supplies (MOPS) are useful as gate drivers auxiliary supply in multi switch conversion systems such as motor drive inverters. This work investigated the use of series resonant capacitors at the secondaries of the transformer to compensate the leakage inductance and explored the issues of cross regulation and parasitic oscillation at light load. Analytical, simulation and experimental studies, conducted in this work, showed that good overall performance can be obtained by the proposed choice of the components of the MOPS. Synopsis Transformer isolated, multi-output auxiliary power supplies (MOPS) are useful as gate drivers supply in multi switch conversion systems such as motor drive inverters. Systems based on WBG devices require low interwinding capacitance of the MOPS isolation transformers. This gives rise to large leakage inductance of the transformer which in turn results in poor load stability and cross regulation. This work investigated the use of series resonant capacitors at the secondaries of the transformer to compensate the leakage inductance and explored the issues of cross regulation and parasitic oscillation at light load. The effects of the non-idealities were investigated analytically, by simulation and experimentally. The results of these studies were used to develop recommendations for considerably improving the performance of the MOPS by proper choice of the resonant capacitors, the rectifying diodes and an LDO which dissipate rather low power.

This study presents a comprehensive analysis of three bipolar-junction-transistor (BJT) amplifier topologies common-emitter, common-collector, and common-base amplifiers-under transient conditions across low and high frequencies. Utilizing MATLAB-Simulink simulations, the response of each amplifier topology to varying frequency inputs is investigated, with a particular emphasis on state space modeling. Beginning with low-frequency models characterized by specific voltage or current sources, the study delves into the steady-state dynamics of output variables, employing state space equations to elucidate amplifier behavior. Notably, the common-emitter amplifier topology exhibits high gains, as corroborated by established literature, underlining its potential for high-performance applications. Conversely, the common-collector and common-base amplifier topologies demonstrate lower gains, reflecting distinct design characteristics. Transitioning to high-frequency operation, the impact of Miller capacitance on gain reduction is explored, especially pronounced in the common-emitter amplifier topology. Despite this, all amplifier configurations remain functional, with state space models validated through meticulous simulations. Simulation parameters are meticulously adjusted to accurately represent real-world scenarios, enhancing the reliability of the findings. These insights underscore the importance of comprehending amplifier behavior across different frequency domains, with state space modeling offering a powerful framework for analysis. By providing detailed insights into BJT amplifier performance under transient conditions, this study facilitates informed decisions regarding amplifier configuration selection and design, tailored to specific application requirements and frequency considerations.

Stability analysis of power electronicsdominated grids is challenging due to the need of manufacturers to protect the intellectual property of converters control systems. Black-box dq impedance models have been proposed to overcome this challenge in grid stability analysis. However, such models are limited to small-signal analyses and are accurate only around a predefined and pre-computed equilibrium point. Conversely, the recently proposed nonlinear Differential-Algebraic Equations (DAE) models of converter-dominated grids can be used for stability analyses in every operating conditions and additionally for generalized power flow analysis and transient time-domain simulations. Despite these advantages, nonlinear state-space converters models require the full knowledge of their control system. To overcome this limitation, this paper proposes a nonlinear grey-box modeling strategy which uses structured perturbations to represent the converters' control system uncertainties. The stability boundaries and control loops sensitivity are analyzed in a multi-vendor converterdominated grid at different operating points through robust control theory. Hardware-In-the-Loop results are provided.

Integrating inverter-based resources into power systems increases their complexity, and makes their operation more challenging. The conventional approach for simulating power systems neglects transients of the power grid, using steady-state phasors and considering only fundamental frequency values. However, to accurately predict the dynamic behavior of systems with high shares of inverter based resources, electromagnetic transient simulations (EMTSs) are necessary. The primary limitation of classical time-domain EMTSs is their frequent timeconsuming nature, making them impractical for larger systems. In this work, we explore the use of dynamic phasors (DPs) as a technique to accelerate EMTSs. For this purpose, we first adapt the power grid and inverter model to DP simulation. Subsequently, we assess the effectiveness of this approach through three numerical examples, demonstrating a reduction in the simulation times by a factor of 4 to 40.

The model complexity in grid-related applications can be reduced by the concentration of distribution network constraints (DNC), which removes the original need for power flow calculations. To conquer the "curse of dimensionality" inherent in existing computational concentration methods, this letter proposes an analytical concentration scheme that utilizes Gaussian elimination and theoretical inner approximation. Based on the 16-dimensional numerical studies, this new scheme proves effective, which eventually enhances the applicability of physical constraint awareness in concrete grid management practices.

Design-oriented modelling of switched reluctance machines is gaining more and more attention as it provides an attractive solution to the considerable computational burden associated to preliminary design stages. This work proposes a novel analytical model to determine the unsaturated inductance values, particularly in non-overlap conditions, based on a twofold improvement of the permeance method: the use of elliptic flux tubes, and the ability to self-tailor the flux tube shapes to any machine geometry. Finally, the accuracy of the proposed model is proven against finite element analysis applied to four designs, along with experimental results of a physical prototype. Selftailoring is also assessed and its implementation in an automated design routine is analysed.

Manufacturing process deviations pose significant challenges in GaN manufacturing especially when modern technologies demand extreme chip densities. More than a thousand of each of three distinct GaN-based flip-chips were manufactured in our highly controlled cleanroom. By integrating Monte Carlo and finite element methods in the simulations which relies on the theoretical models, the results were validated by comparing the voltage measurements of the three thousand manufactured chips. Remarkably, validation was successful considering the replication of voltage deviations affecting the three designs equivalently in each specific wafer. In addition, comparing the three designs, Chip A emerged as the optimal choice for low current resistivity. Looking ahead, our theoretical modeling and simulation hold promise for high-accuracy predictions in high-volume GaN-based chip manufacturing, enhancing reliability and performance.

In many papers about commutation torque ripple of Brushless DC motor, the winding resistance is neglected to match the current slew of incoming and outgoing phase, which lead to torque ripple under heavier load and low speed .The approach I adopt to solve the problem is to maintain neutral voltage constant during the commutation interval. Simulation and experiment result show the proposed algorithm suppress the ripple under heavier load and low speed.

In the past decades when capacitive loads were less common. As a result, electrical networks only experienced electrical wave distortions on inductive loads, such as industrial pumps, refrigeration units, injection compressors, and electric motors; phase angle issues in the power triangle accounted for the majority of these issues. The classic, adding power factor capacitors were seem to be capable of solving majority of such issues. [1-3]. However, harmonic distortions on capacitive loads were later produced by excessive use of computers, energy-saving light bulbs, mobile chargers, and routers; These distortions had a direct influence on transformer stations and significantly affected electrical network performance. At present times, the public and private electric power generation and distribution companies around the world have recently become aware of these problems and have imposed heavy fines on this type of due to the fact that it reduces the efficiency of state-level distribution stations while simultaneously undermining the principle of equitable power distribution.

With more inverter based generations the nature of voltage variation during grid events changes requiring new approach to network voltage support. This paper develops an optimal control strategy for grid-forming inverter-based resources (GFM-IBRs) to provide the maximum dynamic voltage support (DVS) to the grid considering the current limitation and thus offering an effective Low Voltage Ride-Through (LVRT) capability. In this paper we look at different current limitation control methods that have been proposed in the literature. A new active current priority and reactive current priority, have been investigated and compared with the existing Optimal dynamic Voltage Support. The results suggest that prioritizing the fault current, does not always give the optimal support if X/R ratio is not taken in to consideration. Despite the IBR reaching its maximum current limit, oDVS (optimal-dynamic voltage support) has been found to be the optimal solution making the inverter provide maximum voltage support at the Point of Common Coupling (PCC) while keeping the phase angle at its optimal value.

This research intends to use machine learning techniques to estimate the heating load class of residential buildings, allowing for the design of more energy-efficient structures. The dataset utilized in the analysis comprises of seven hundred residential building examples, each with variables such as relative compactness, surface area, roof area, overall height, orientation, glazing area, and glazing area distribution. The target variable, heating load class, is classified as low, medium, or high. The analysis begins with a thorough overview of the major variables, which includes summary statistics, correlation analysis, and visuals. Decision tree models are built in two ways: using the complete dataset and adopting a 10-fold cross-validation strategy. Additionally, a Neural Net Multi-Layer Perceptron (NN-MLP) model is run and compared to the decision tree models. The results show that all models have high accuracy rates, with the NN-MLP model having the best at 99.57%. Comparative research indicates that the NN-MLP model outperforms decision tree approaches in forecasting heating load classes. However, both models perform well in accurately categorizing heating load classes. Overall, this work emphasizes the effectiveness of machine learning algorithms in forecasting heating load classes, as well as their potential for improving the energy efficiency of residential structures.

This research deals with the effect of capacitive and inductive harmonics on the efficiency of electrical and electronic devices used in installing a giant screen owned by Faden Spectrum Company that was installed on one of the huge towers in the city of Al-Khobar in the Kingdom of Saudi Arabia, with an area of 2000 m2, and it consists of 2000 cabinets containing 2000 electrical cells, each cell having an area of 1 m2.The problem was solved -Significantly - by linear grounding, that provided creative also solutions for four similar screens of relatively smaller size, which may allow this type of grounding to be generalized to all electrical networks with similar problems.The engineering team, under our supervision, worked to reduce the annoying noise coming from the main feed box (MDB), which was increasing steadily with increasing lighting intensity, to become 90 dB at 60% brightness and 155 dB at 100% brightness, by building an independent linear grounding.It deals to grounding the zero point of the transformer and this applies to the screen feeding system as a whole, provided that the old grounding remains as grounding against touch currents to protect individuals from electrification.Our engineers succeeded in reducing the noise caused by harmonics from 95dB to 55dB at 50% brightness (which is the normal operating rate for this type of screen at noon).The apparent amperage on neutral has also been reduced to 438A instead of 811A at the same brightness rate.This solution is reduced the size of the Active Harmonic Filter to less than half, saving a lot of money when the customer wants to reach typical values by used the THD Filtering.

Accurate state-of-health (SOH) estimation is crucial for the reliable operation of lithium-ion batteries in sustainable energy systems. While existing methods often require complex feature engineering or focus on specific voltage ranges, this paper presents a novel and practical approach using readily available discharge data from Battery Management Systems (BMS). We introduce a hybrid feature set that combines the strengths of incremental capacity (IC) curves and temperature generation factors, capturing critical aspects of battery degradation. By employing a two-layer feedforward Artificial Neural Network (ANN) and analyzing the complete discharge cycle, our method was tested two different dataset, achieves an average root mean square error (RMSE) of 0.38% across two diverse battery degradation datasets with minimal computational cost. This approach offers a promising solution for real-world on-board SOH estimation, enabling efficient battery management and ensuring the longevity of energy storage systems.