Switched-capacitor Research Articles

Design and investigation of a novel variable reactance-based capacitive RF-MEMS switch with multifrequency operation for mmWave applications

Design and investigation of a novel variable reactance-based capacitive RF-MEMS switch with multifrequency operation for mmWave applications

Single Source Switched Capacitor Based Multilevel Inverter With Reduced Number of Components

Multilevel inverters find extensive use across various applications owing to their capability to generate voltage waveforms of superior quality. thereby minimizing harmonic distortion. Traditional multilevel inverters require a substantial number of power semiconductor devices and passive components, resulting in heightened costs, intricacy, and physical dimensions. These multilevel inverters play a pivotal role in the electric vehicle sector, serving as a critical component in EV power trains for converting Convert DC power from batteries into AC power to operate electric motors.. The research methodology involves the following steps: Design and configuration of a multilevel inverter utilizing switched capacitor technology from a single source.. Design proposed topologies for multilevel inverter design. Design a circuit according topologies in MATLAB. Comparative analysis with existing multilevel inverter designs. Evaluation of the inverter's performance in terms of harmonic distortion, output voltage quality, and efficiency. Rather than employing a multilevel inverter with fewer switches that operates from a single source the highest possible levels are preferred. This paper introduces an innovative 9-level multilevel inverter based on a single source and switched capacitors, which significantly reduces the number of switches required, utilizing only three capacitors and a single DC source. This design approach serves to streamline the inverter's intricacy, decrease costs, and minimize its physical footprint. Furthermore, it elevates the voltage levels by up to 96%, enhancing the overall performance and efficiency of the inverter. Additionally, this technology can be applied in electric vehicles, integrated with renewable energy sources, and utilized in industrial settings. The novelty of this research lies in the utilization of a single-source switched capacitor configuration to achieve multilevel inverter functionality. This approach reduces the number of components required, leading to potential cost savings and improved system reliability. The novel features of this study include: Integration of switched capacitors in a single-source multilevel inverter design. Achieving multilevel inverter functionality with fewer components. Improved performance and efficiency compared to traditional multilevel inverter designs.

A switched‐capacitor‐based active‐interleaved‐network with high voltage gain

SummaryA high voltage gain characteristic is required in fuel cell vehicles to achieve voltage matching between low voltage fuel cell and high voltage DC‐link bus. In this paper, a non‐isolated high step‐up dc‐dc converter based on switched‐capacitor (SC) and active‐interleaved‐network (AIN) is proposed. The main advantages of the converter are the high voltage gain without an extremely high duty cycle, automatic current sharing of inductors, reduced input current ripple, and lower device voltage stress when compared to the topologies with the same output voltage. Detailed descriptions of the converter including operation principle and dynamic response analysis are presented in this manuscript. To verify the correctness of the theoretical analysis and feasibility of the proposed converter, a 500 W prototype with 20 V input and 200 V output is built.

A Switched-Capacitor Multilevel Inverter With Modified Pulsewidth Modulation and Active DC-Link Capacitor Voltage Balancing

Although switched-capacitor (SC) multilevel inverters (MLIs) offer self-voltage balancing of flying capacitors and voltage gain higher than unity, the advantages come at the cost of high current stress and power loss in the SC circuit and DC source. Moreover, the voltage balancing is often restricted to a limited range of modulation index. Another problem of MLIs with neutral-point clamped (NPC) front end is with DC-link capacitor voltage balancing under unbalanced load conditions, imploring sensor-based closed-loop control. This paper proposes a unity gain five-level active-NPC SC-MLI with inrush charging current attenuation and actively balanced DC-link capacitor voltages under all load conditions and over the entire range of modulation index and power factor. The modified pulse-width modulation results in most switches operating at a low switching frequency, minimizing switching losses in the SC circuit. The proposed MLI attains a maximum efficiency of 98.04% at an output power of 510 W. Experiments on a 2 kVA laboratory prototype validate the theoretical analysis. Results from transformerless grid-connected solar PV system show that the proposed MLI can inject power into the grid with a unity power factor under conditions of varying irradiance and provide the grid with reactive power as well.

Design and Simulation of Switched Capacitor for Voltage Boost Converter for EV

The concept focuses on using voltage boost converters specifically made for electric vehicle (EV) drives to simulate and design switching capacitors. In the past, standard boost and non-boost inverters have been used to assess the efficiency of electric vehicles. On the other hand, this proposal suggests using voltage boost converters to greatly increase the vehicle's speed and efficiency. The main goal is to maximise the converter topology's design parameters in order to achieve higher voltage-boosting capabilities and improve the vehicle's overall performance. The research intends to address issues with power delivery and power conversion efficiency through careful investigation and simulation. This research aims to push the boundaries of EV technology and aid in the creation of more effective and potent electric vehicles by utilising sophisticated switching capacitor techniques. The project's approach entails a thorough analysis of current converter topologies and design parameters, followed by in-depth simulation studies carried out with the use of suitable software programmes like Matlab. To maximise the efficiency of voltage-boosting, several parameters, including switching frequencies, capacitor values, and circuit designs, will be rigorously examined and optimised. The relevance of this notion is that it has the potential to introduce revolutionary voltage boost converter designs that are specifically tuned to the needs of electric vehicle drives, hence revolutionising the efficiency benchmarks of EVs. By conducting thorough testing and analysis, this research hopes to open the door for the electric car sector to widely embrace cutting-edge power conversion technology, which will ultimately result in more environmentally friendly and sustainable transportation options.

Local outlier factor based condition monitoring for sub-module capacitors in modular multilevel converters

Modular multilevel converters (MMC) as a key technology for future power grids is rapidly developing. The failure rate of sub-module (SM) capacitors is only lower than that of power devices in MMC, so accurate evaluation of the reliability of capacitors is crucial for the safe operation of MMC. Both the increase of equivalent series resistance (ESR) and decrease of equivalent series capacitance (ESC) are features for capacitor deteriorating. ESC or ESR of the capacitor reaching to limited values indicates that the end of its lifespan and it must be replaced. Most of contemporary researches only focus on ESC or ESR. This article considering the characteristics of both ESC and ESR, proposes a method for monitoring the status of SM capacitors based on machine learning algorithm: local outlier factor (LOF). By utilizing the existing SM switching function and capacitor voltage signals within a fundamental frequency cycle in the control system of MMC, the proposed strategy can not only monitor the faulty capacitors, but also can sort the degrees of abnormal states of capacitors, and the strategy is applicable to MMC with single and multiple types of SM topologies. The proposed method is verified by the hybrid MMC model on Matlab/Simulink.

Nine level switched capacitor inverter with level shifted pulse width modulation approach

This article proposes a nine-level switched capacitor inverter (NLSCI) with a minimum number of switches. In recent years, switching capacitor (SC) multilevel inverters (MLIs) have become one of the most common inverter topologies. These proposed nine level switched capacitor inverter (NLSCI) do not deserve any external control unit for capacitor control. Since, the charging and discharging of the capacitors are controlled by the on and off states of switches. Furthermore, by employing fewer switches and DC voltage sources, the suggested design produces a greater amount of resultant voltage. Additionally, pulse width modulation (PWM) is recommended as a method to enhance output quality and power level quality. The switched-capacitor two-output multilevel inverter (SCMLI) structure's viability and effectiveness have been demonstrated using MATLAB simulation.

A flexible islanding identification technique for PV based grid-tied systems

The phenomenon of islanding, which occurs in grid integrated distributed generators (DGs), poses significant challenges and potential risks for customers, utility providers, and operational personnel. Detecting islanding quickly and accurately is crucial. The issues related to the conventional islanding methods paved the way in developing a hybrid islanding identification scheme. The developed method achieves accurate results within a reduced time span of 30 ms. The hybrid technique combines the passive method of sliding window based moving average (SWMA) for faster detection and the active method of asymmetrical bilateral reactive power perturbation (ABRPP) for improved precision. The developed hybrid method is simulated and tested using MATLAB/Simulink 2023a. It undergoes testing under various load conditions, including small and large power mismatches between DG generation and local load demand, as well as different load quality factors (QF). Additionally, the hybrid algorithm demonstrates no nuisance tripping when subjected to different load switching conditions such as capacitor switching, motor load switching, and nonlinear load switching. A comparison with existing islanding detection methods confirms the accuracy and speed of the proposed method in identifying islanding conditions.

CSA-based harmonic elimination controlled reduced switch multilevel inverter for standalone PV system

A standalone photovoltaic (PV) system has emerged as a clean energy solution for application in remote areas. To establish such a system a suitable converter is required for voltage boosting as well as to produce high-quality output as required by the load. To achieve this, multilevel inverters (MLIs) are the proven substitute in PV systems. A conventional MLI uses multiple sources and switches that increase the cost and complexity. To provide a complete stand-alone system solution that addresses this issue, reduced component switched capacitor (SC) based seven-level MLI is disclosed in this paper. A new crow search algorithm (CSA) based selective harmonic elimination (SHE) method is employed to estimate the switching angles. The dominant harmonic orders are removed from the 7-level SC-MLI output while the desired firing angles for the switches are evaluated. Using only seven switches, the circuit boosts the voltage to 1.5 times with the aid of self-balanced capacitors. Therefore, the circuit does not require an additional control circuit for voltage balancing. The overall system is designed in MATLAB/Simulink environment to test under different operating conditions. An experimental prototype of the 7-level SCMLI is also designed to validate the CSA-based SHE control and MLI performance in real time.

Uncertainty Quantification of Capacitor Switching Transient Location Using Machine Learning

Identification of capacitor switching transient location provides valuable insight into the state of the associated equipment. Machine learning (ML) models, and convolutional neural networks (CNNs) in particular, have demonstrated remarkable performance in signal location. However, ML models are data driven whose predictions are affected by noise in data and may also suffer from large extrapolation errors when applied to new conditions. Uncertainty quantification (UQ) is necessary to ensure model trustworthiness and avoid overconfident predictions in extrapolation. In this work, we propose a novel UQ method, called PI3NN, to quantify prediction uncertainty of ML models and integrate the method with CNNs for transient source location. PI3NN calculates Prediction Intervals by training 3 Neural Networks and uses root-finding methods to determine the interval precisely. Additionally, PI3NN can identify out-of-distribution (OOD) data in a nonstationary condition to avoid overconfident prediction. Results indicate that with PI3NN, transient signals are not only correctly identified, but when said signals are subject to corruptions characteristic of an actual power monitoring system (e.g non-ideal sensors), the model recognizes when it is uncertain about its predictions, effectively letting the user know when to accept or discard the results.

Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach

This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

Kinetics of N- to M-Polar Switching in Ferroelectric Al1-xScxN Capacitors.

Ferroelectric wurtzite-type aluminum scandium nitride (Al1-xScxN) presents unique properties that can enhance the performance of non-volatile memory technologies. The realization of the full potential of Al1-xScxN requires a comprehensive understanding of the mechanism of polarization reversal and domain structure dynamics involved in the ferroelectric switching process. In this work, transient current integration measurements performed by a pulse switching method are combined with domain imaging by piezoresponse force microscopy (PFM) to investigate the kinetics of domain nucleation and wall motion during polarization reversal in Al0.85Sc0.15N capacitors. In the studied electric field range (from 4.4 to 5.6MVcm-1), ferroelectric switching proceeds via domain nucleation and wall movement. The currently available phenomenological models are shown to not fully capture all the details of the complex dynamics of polarization reversal in Al0.85Sc0.15N. PFM reveals a non-linear increase of both domain nucleation rate and lateral wall velocity during the switching process, as well as the dependency of the domain pattern on the polarization reversal direction. A continuously faster N- to M-polar switching upon cycling is reported and ascribed to an increasing number of M-polar nucleation sites and density of domain walls.

A compact and reconfigurable C-band DGS-based power divider using RF MEMS capacitive switch

This article presents a reconfigurable and compact power divider design that utilizes a combination of defect structures in the ground plane (DGS) and capacitance loading using a unique RF MEMS switch on the sapphire substrate. The combination of these two techniques results in a reduced-size reconfigurable power divider, wherein the length of the quarter-wavelength transmission line is reduced to λ/16 from λ/4, and the higher-order harmonics are suppressed up to 40 GHz. The RF MEMS capacitive switch is designed, and its DC characteristics are simulated in coventorware. The RF characteristics of the designed power divider are simulated in Ansys HFSS 19.2. The power divider utilizing RF MEMS technology has been fabricated through surface micromachining, requiring only four masks for fabrication. According to measurement findings, insertion loss and return loss are 3.57 dB and 24.6 dB (at 4.4 GHz) and 3.82 dB and 22.06 dB (at 5.4 GHz) when the switch is on and off, respectively. The total area of the design is 3.1 mm2 (21.3% of the conventional power divider). The proposed design can suppress harmonics up to the 8th order by 10 dB. These features render it a viable option for employment in Radio Frequency Integrated Circuits (RFICs).

Universal Adaptive Differential Wavelet Energy-Based Protection Scheme for an AC Microgrid

A protection engineer’s top priorities are the detection of faults and the recognition of faulty phases. Modern distribution networks are interconnected; thus, any breakdown poses a hazard to the entire system. One of the challenges with microgrids is developing an effective protection scheme. In this paper, a new scheme has been proposed that effectively detects faults in the grid-connected, as well as off-grid modes also radial and looped topology of the microgrid. The scheme can adapt to the system changes and even detects the high resistance faults in a low-voltage distributed feeder. Moreover, various non-fault events such as load switching, capacitor switching, DG outage, section cutoff, and external faults have also been considered for evaluation. The performance of the scheme has also been tested for changes in fault inception angles, fault types, fault locations, simultaneous, evolving, and composite faults. The suggested method has also been tested using real-time data from the OPAL-RT simulator. The results demonstrate that the suggested scheme is suitable for real-time practical applications for the protection of microgrid feeders. Further, a comparative study has been done to prove that the proposed scheme is better than several existing protection schemes considered in this paper.

Dual Timescales Voltages Regulation in Distribution Systems Using Data-Driven and Physics-Based Optimization

A large number of electric vehicles (EVs), distributed solar and/or wind turbine generators (WTGs) connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming (QP) for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators (DGs) and static VAR compensators (SVCs) is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process (MDP) is developed, in which the charge/discharge power of energy storage systems (ESSs), statuses/ratios of switchable capacitors reactors (SCRs), and voltage regulators (VRs) are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

A Reduced Component Count Self-Balance Quadruple Boost Seventeen-Level Switched Capacitor Inverter

A Reduced Component Count Self-Balance Quadruple Boost Seventeen-Level Switched Capacitor Inverter

Capacitor Switching Techniques

Capacitance switching applications involve not only interrupting capacitive currents, but also the energizing of capacitor banks, cables and overhead lines. The interruption of a capacitive current can cause dielectric problems for the switching device. The high inrush currents can cause damage to the capacitors of the capacitors bank and to weld the contacts of the switch together. The problems of the capacitive inrush currents and ways to reduce the magnitude of the inrush current, have been chosen for analysis in this paper.

Investigation of PWM methods for a 9 level boost inverter using CD-type carriers

The article suggests a new boost inverter topology that uses two switching capacitors and a single dc source to produce an output voltage waveform with nine levels. There are no sensors or other electronics required because the capacitor voltages balance themselves. An input dc boost converter is not necessary when using an inverter because its output voltage is frequently two times greater than its input voltage, especially when the inverter is powered by a natural source. The new modulation techniques that have been proposed for CD-type carrier waves have better efficiency, higher RMS voltage, and less harmonic distortion (THD).The efficacy of the suggested carriers has been confirmed by phase disposition (PD), alternate phase opposition disposition (APOD), and phase opposition disposition (POD) investigations. The effectiveness of each technique described under 9LBI has been evaluated using a MATLAB/Simulink configuration. The operation and dynamic performance of the suggested architecture have been modelled using MATLAB/ SIMULINK.

A PVT-Robust and 73.9 mHz High-Pass Corner Instrumentation Amplifier with an SCF-SCR-PR Hybrid Feedback Resistor

Analog front-end (AFE) circuits play an important role in the acquisition of physiological signals with low-level amplitudes (from tens of μV to tens of mV) and broadband low-frequency ranges (from sub-Hz to several hundred Hz). Possessing a high input impedance, an instrumentation amplifier (IA) accurately amplifies signals with low amplitude and low frequency, making it suitable for AFE circuits. This work demonstrates a capacitively coupled IA whose feedback resistance is realized by the proposed hybrid resistor, consisting of a switched-capacitor low-pass filter, a switched-capacitor resistor, and a continuous-time low-pass filter. The capacitively coupled IA achieves tera-ohm (TΩ) resistance and is insensitive to process, voltage, and temperature (PVT) variations. The simulation results show that the proposed IA illustrates a high-pass corner of 73.9 mHz, and the change of its high-pass corner with temperature is 0.05 mHz/°C. With the variation in the PVT corners, the difference between the maximum and minimum values of the high-pass corner of the proposed capacitively coupled IA is only 0.06 Hz. The design was implemented in a 130 nm standard CMOS process. The AFE with the proposed capacitively coupled IA achieves a 53.9 dB signal-to-noise and distortion ratio (SNDR) and 69.5 dB total harmonic distortion (THD).

A fault recognition method for transmission systems based on independent component analysis and convolutional neural networks

Accurate recognition of transmission line (TL) faults plays a vital role in protection systems, avoiding misoperations and false trips. Several studies in this area have revealed the limitations of existing algorithms, particularly when considering more complex electrical power systems (EPS), where some expected operations (capacitor and load switching, for example) can be misinterpreted as fault events. This work addresses the critical issue of fault recognition (FR) in TLs with a special focus on distinguishing faults from other types of disturbances. The primary objective of this study is to introduce an approach based on Convolutional Neural Networks (CNN) and Continuous Wavelet Transform (CWT) for FR in EPS. This method relies only on voltage signals acquired with a reasonable sampling frequency. The proposed method is enhanced with an Independent Component Analysis (ICA)-based preprocessing, making it robust and versatile in handling different types of disturbances, such as adjacent line faults, load switching and capacitor switching. The evaluation of the proposed method is done considering two different EPS, achieving an accuracy up to 99 %, showing the robustness of the method against different scenarios such as fault events and switching operations.