Capacitor Size Research Articles

Novel Topology for Modified Boost Series and Parallel Switching Capacitor DC-DC Converter

Theoretical and experimental work for a novel topology of DC-DC boost switching capacitor converter is introduced in this paper. This new design is an adjustment for boost series and parallel topology developed by Makowski. Thus, a comparison between the two designs presented in this paper aims to highlight the improvement in the conversion rate of the boost converter’s output voltage while using the same number and size of capacitors. Converter analyses for both with and without load are presented. Also, a boost converter with a nonlinear ferroelectric capacitor is presented to further increase the boost converter conversion rate using advantages of the ferroelectric capacitors, such as their big dielectric constant and polarization reversal.

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on Crow Search Algorithm

In power systems, the distribution network is crucial as it serves as the final stage in delivering energy from generation plants to end users. However, reactive power demand from consumers can create challenges such as low power factor, voltage drops, and increased power losses depending on the distribution system used. This study aims to enhance the performance of radial distribution networks (RDNs) by optimizing capacitor placement and sizing based on the Crow Search Algorithm (CSA). Additionally, the study addresses the Optimal Power Flow (OPF) within the network by employing the Backward/Forward Sweep (BFS) method. The study accesses CSA’s effectiveness compared to other metaheuristic optimization techniques through simulation. The simulation results showed that the Voltage Stability Index improved from 0.61 for base case to 0.68 pu with CSA, compared to 0.66 for Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Teacher Learner Based Optimization (TLBO and 0.67 for Invasive Weed Optimization (IWO). CSA also achieved a 30.41 % reduction in active power losses, outperforming PSO, ABC, GA, and TLBO having 26.61 %, and IWO at 24.92 %. Additionally, application of CSA led to a 29.33 % reduction in reactive power losses, compared to 21.91 % for PSO, ABC, GA, and TLBO, 18.2 % for IWO. In the simulated IEEE 33 bus Radial Distribution Network (RDN) topology, the lowest bus voltage at bus 18 increased from 0.8820 in the base case to 0.9080 with CSA, indicating a 32.9 % improvement in voltage deviation. Furthermore, the optimization resulted in a significant reduction of 3.841 pu in capacitor cost, demonstrating CSA’s efficiency in both technical and economic aspects. CSA not only showed favorable results in minimizing power losses and improving voltage profiles but also provided substantial cost savings in capacitor implementation, making it a robust and cost-effective solution for enhancing RDN performance.

Atomic doping of Alumina-based dielectric with high permittivity and breakdown field strength

High specific capacitance anode aluminum foils fabricated by introducing TiO2 to improve the permittivity (εr) of Al2O3 is one of the most effective ways to reduce the size and weight of aluminum electrolytic capacitors (AECs). However, the underlying mechanism by which the introduction of TiO2 causes capacity enhancement remains unclear. Here, a proposition is claimed that TiO2 is not directly responsible for the capacity enhancement but Al-doping TiO2 (AOT) actually. An atomically doping strategy based on atomic thermal diffusion and ionic electromigration is proposed to realize the fabrication of AOT via modulating the thermal and electric fields. Results show that the doping behavior of Al3+ induces the displacement of neighboring Ti4+ ions, which enhances the dipole polarization resulting in an increase of εr (25). Meanwhile, the diffusion of O2 and migration of O2– effectively removes the oxygen vacancy, enhancing the breakdown field strength (> 6 MV·cm−1) of dielectrics. Ultimately, the specific capacitance of anode foils is increased by about 50 % compared to those without AOT. On a brighter note, this work deepens the understanding of the capacity enhancement mechanism and will contribute to further facilitating the miniaturization of AECs.

The League Championship Algorithm (LCA) for optimal placement and sizing of shunt capacitors

The League Championship Algorithm (LCA) for optimal placement and sizing of shunt capacitors

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

Effect of rotation on achieving constant voltage in three-phase self-excited induction generator for small scale wind turbines application

Three-phase Self-Excited Induction Generators (SEIGs) are commonly employed for electricity generation in remote or isolated areas. SEIGs are preferred in such regions due to their ability to create a magnetic field by adding a capacitor to one of their terminals. Nevertheless, a significant challenge in utilizing SEIGs is maintaining a consistent output voltage in the presence of load fluctuations. This study aims to investigate the impact of generator rotation on the SEIG's output voltage and determine the optimal rotation speed required for achieving a stable output voltage. Ensuring stable voltage regulation is crucial to guarantee the proper functioning of all loads connected to the SEIG. Furthermore, operating the SEIG in parallel with other generators is advantageous. The methodology employed in this study involves varying the load supplied by the SEIG at different capacitor values. Unwanted voltage variations occur due to load fluctuations within a generating system or SEIG. Adjustments to the generator's rotation speed are made to uphold a uniform voltage level. The variables considered in this study include the generator's rotation speed, capacitor size, and load fluctuations. Simulation results demonstrate that the SEIG's output voltage is affected by the generator's rotation speed, and maintaining a consistent voltage necessitates appropriate adjustments to capacitor values and generator speed. This research enhances understanding of SEIG characteristics and offers guidance on effective settings for maintaining a stable output voltage at various generator rotation speeds. Future research can focus on practically implementing these findings to enhance the performance of SEIGs in real-world applications

Size-adjustable, foldable, and stretchable capacitor with origami and auxetic structure

Stretchable and flexible capacitors offer significant advantages as energy devices for next-generation electronics. In line with the trend for the aforementioned electronic devices, scientists are exploring the development of energy devices using intrinsically stretchable materials to endow geometrical flexibility. However, these materials typically exhibit a positive Poisson’s ratio, which may not be favorable for long-term mechanical stability. In this study, a size-adjustable capacitor is presented, featuring an auxetic and origami structure designed for foldability and stretchability. The combined structural strategy of auxetic and origami allows the device to be folded and stretched without a deterioration in performance. The size of this auxetic capacitor can be modified into five different stages: 100 % and 50 % folded, original, and 50 % and 100 % stretched. To assess the stability of the device under mechanical deformations and long-term usage, multiple characterization tests were conducted, including cyclic voltammetry and charge−discharge tests, demonstrating stable performance under mechanical deformations. This promising size-adjustable capacitor can find applications in a wide range of devices, including wearables and compact electronics.

A new triple voltage gain seven level switched capacitor-based inverter with minimum voltage stress

Abstract This paper presents, a new seven level triple voltage gain inverter topology is proposed based on switched capacitor technique. The proposed inverter topology has minimum voltage stresses on the switches and balanced capacitor voltages. This paper briefs the operation of proposed topology, voltage stress analysis, capacitor sizing and generation gate signals for the switches. In addition, proposed topology is modeled in industrial based software PLECS with practical switches to study thermal behavior and efficiency analysis. Further, the proposed inverter topology is compared with competitive topologies in the recent literature. The proposed topology has merits like minimum total standing voltage, switching redundancy and inherent polarity generation. Furthermore, MATLAB based simulations and experimental analysis is done to validate the superior performance the proposed topology. The experimental results for the proposed seven-level inverter are presented and discussed in brief by considering different types of loads, amplitude modulation variations and frequency variations.

A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

Due to power losses, a sizeable portion of the electrical power produced is lost, which causes voltage deviation and instability as a result of rising load demand at the radial distribution network (RDN). Shunt capacitor penetration will enhance the RDN’s efficiency by reducing power losses, improving the voltage profile and stability. For the best sizing and allocation of shunt capacitors (SCs) in RDN, a novel human-based metaheuristic heap-based optimizer (HBO) influenced by the ideas of corporate rank hierarchy (CRH) of standard organization is presented. The real power loss, voltage variation, and voltage stability index are all minimized while equality and inequality criteria are met by the HBO optimal allocation of SCs. One, two, and three numbers of SCs were investigated when installing the reactive compensating devices. Using typical IEEE 33-bus and 69-bus RDNs, covering a range of scenarios in the form of single- and multi-objective functions, the effectiveness of the HBO technique was examined. The real power loss for the conventional IEEE 33-bus and 69-bus RDN was reduced by 34.46% and 35.50%, respectively compared to their respective base cases. By contrasting the outcomes of the power loss with well-established and new optimization approaches, the efficacy and superiority of the suggested HBO were demonstrated.

Research on Step-Down Aviation Three-Phase Power Factor Correction based on Machine Learning and Optimization Algorithm

Objectives: The study looks at power quality issues, such as phase imbalance, voltage sags, and power factor issues, in aircraft systems. It highlights how immediate power factor correction steps are required to avoid equipment longevity problems and boost system efficiency, which will ultimately improve the performance of the entire aviation system. Methodology: Using a variety of data sources, real-time data gathering, and machine learning approaches, this study focuses on data collecting and real-time monitoring in aviation systems. Predictive modeling, testing methods for data preparation and analysis, and optimization algorithms for capacitor bank size and placement are all included. Considerations for privacy and security are covered. Result: Salp Swarm Optimization and the Dragonfly Algorithm are used in the study to assess the ideal capacitor sizes and positions, which results in better voltage profiles and lower active power losses. Aviation systems benefit from improved power factor correction through the application of integrated machine learning and optimization, which promotes sustainability and energy efficiency. Conclusion: The study demonstrated the significance of power factor correction in aviation by successfully addressing power quality issues in aircraft electrical systems through the use of optimization algorithms for capacitor sizing and location.

Influence of Load Models and DG Operational Power Factor on Optimization of Shunt Capacitor Placement in Distribution Grids Considering Voltage Stability

The paper proposes a mathematical model of mixed-integer nonlinear programming (MINLP) to optimize the location and size of shunt capacitors in power distribution grids with distributed generation (DG), considering voltage stability constraints. At the same time, the paper also analyzes the influence of the load models, such as voltage-dependence load (ZIP load) and constant power load, together with DG’s operating power factor on the optimal solution. The objective function of the proposed optimization formulation is minimizing the total expenses, including the capital investment of capacitors and the expense incurred by network energy loss. This MINLP model includes constraints in the current and critical loading conditions, such as the system of power flow equations, nodal voltage limits, branch thermal limits, capacitor-related constraints, and restrictions of minimum security level. The proposed optimization model was evaluated on a modified IEEE 33-node distribution grid using the KNITRO commercial solver with the GAMS programming language. The calculation results show that the voltage stability constraints, load models, and operational power factor of the DG units have an essential influence on the optimal position and capacity of the shunt capacitors.

Optimal sizing and placement of capacitors in the isolated microgrid throughout the day considering the demand response program

Reactive power compensation (RPC) is a big problem during power system operation. Parenthetically, capacitor allocation and sizing may be the only convenient solution for RPC of power systems. The loss sensitivity factor (LSF) is applied here for finding the optimum capacitor position. This paper presents quasi-oppositional fast convergence evolutionary programming (QOFCEP), fast convergence evolutionary programming (FCEP), and evolutionary programming (EP) for the optimum location and sizing of shunt capacitors in the isolated microgrid (MG) for minimizing total real power loss throughout the day with and without the demand response program (DRP). The 33-node, 69-node, and 118-node isolated MGs have been studied to authenticate the efficacy of the suggested approach. Each MG includes small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), diesel generators (DGs), and plug-in electric vehicles (PEVs).

A 96 dB DR Second-Order CIFF Delta-Sigma Modulator with Rail-to-Rail Input Voltage Range

A second-order delta-sigma modulator (DSM) is proposed for readout integrated circuits of sensor applications requiring a small area and low-power consumption. The proposed second-order CIFF DSM with the architecture of cascaded-of-integrator feedforward (CIFF) basically consists of two integrators, a 3-bit quantizer, data-weighted averaging (DWA) circuit, and clock generator. The use of the 3-bit quantizer instead of the single-bit quantizer reduces the size of the feedback capacitor in the first integrator. The 3-bit quantizer is designed based on a successive approximation register analog-to-digital converter for small area and low power implementation. Furthermore, the proposed second-order CIFF DSM has a single supply without an additional reference driver while having a wide analog input voltage range with rail to rail. The proposed second-order CIFF DSM, implemented using a 130 nm 1-poly 6-metal CMOS process with a supply of 1.5 V, has an area of 0.096 mm2. It has a sampling frequency of 500 kHz for the implementation of an input bandwidth of 2 kHz and an oversampling ratio of 125. The measured peak signal-to-noise and distortion ratio is approximately 90 dB when the differential analog input signal has a frequency of 353 Hz and an amplitude of 1.2 Vpp. The measured dynamic range is approximately 96.3 dB.

Effect of bias potential and dimension on electrochemical migration of capacitors for implantable devices

Dendrite formation induced by electrochemical migration (ECM) is a common reliability problem occurring on printed circuit boards (PCBs), which significantly threatens the long-term safe operations of current implantable electronic devices (IEDs). Although several factors (i.e., contaminations, humidity, temperature) are proved to be the parameters closely related to ECM susceptibility of capacitors on a PCB under climate environments, further targeted research under other environments still needs to be conducted as ECM is highly environmental-dependent. Herein, the effects of bias potential and pitch dimension on ECM sensitivity are systematically studied using various sizes of capacitors on a test PCB under a human implantation environment. The finite element method first proves that a DC voltage pattern could be regarded as an accelerated test compared to other waveforms. Subsequent chronoamperometry tests using the DC potential further indicate that dendrite formation is closely related to pitch dimension under low bias potential, while under high bias potential electric field is also the dominating factor of dendrite formation for capacitors on a PCB. Benefiting from the electrochemical impedance spectroscopy (EIS) technique, the capacitor reliability under different corrosion states is also evaluated in a detailed manner. This work offers great value both in electronic corrosion mechanisms and future rational design for reliable IEDs.

Capacitor sizing of three‐level neutral point clamped voltage source inverter for electric vehicles: Effects of modulation and motor characteristics

AbstractA three‐level neutral point clamped (3L‐NPC) voltage source inverter (VSI) topology can be advantageous in electric vehicles with a high DC‐link voltage and a high switching frequency. A bulky DC‐link capacitor is not an option; thus, the DC‐link capacitor's sizing considering the traction system characteristics is an important design step. This paper investigates how the DC‐link capacitor size of a 3L‐NPC VSI gets affected by the combination of the interdependent characteristics of an electric drive, such as its power factor, modulation index, current, and fundamental frequency, with the effects of the modulation methods. Five pulse width modulation (PWM) methods, of which three of them have an active neutral point potential control, are compared in terms of their neutral point potential (NPP) oscillations. Then, the size of the DC‐link capacitor is determined for each PWM method so that the NPP ripple is kept under desired limits at all operating conditions. It is shown that both the modulation technique and the electric machine characteristics influence the capacitor size. For example, electric machine design modifications can introduce more than a 30% reduction in capacitor size. Finally, DC‐link and NPP oscillations with different PWM methods are experimentally validated in a scaled‐down 3L‐NPC inverter.

Optimal capacitor bank placement and sizing using particle swarm optimization for power loss minimization in distribution network

This study delves into the issues concerning power quality in distribution systems, which commonly experience substantial losses, voltage fluctuations, and instability in bus voltages. More specifically, this paper centered on optimizing the Distribution Static Compensator (DSTATCOM) to tackle these challenges. To achieve this objective, we employ the Backward/Forward Sweep (BFS) method for load flow analysis and the Particle Swarm Optimization (PSO) algorithm to determine the ideal size and placement of the DSTATCOM. The study evaluates three different scenarios: the first scenario considers a 50-bus system without DSTATCOM, while the second and third scenarios incorporate the use of PSO to identify the optimal location and size of fixed and switched DSTATCOM. The aim is to reduce active and reactive power losses, enhance the voltage profile, and minimize system costs. The DSTATCOM integration significantly improved the distribution system by raising the minimum bus voltage from 0.817 p.u. to 0.95 p.u. Case 1 and Case 2 experienced notable reductions in active and reactive power losses, with percentages ranging from 58.72% to an impressive 96.69%. Economically, both cases demonstrated substantial net savings of 58.85% and 58.71%. The results demonstrate that employing PSO to allocate and size DSTATCOM can significantly decrease distribution system losses.

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.

Optimal Location of Capacitor in a Radial Distribution System Using New Hybrid Method

This paper solves the problem of optimal sizing and locations of capacitors in radial distribution system using a new hybrid method combining a new stability index and a genetic algorithm to improve the loss, voltage and stability planes. The Bus Voltage Stability Index BVSI is used to determine the locations and the genetic algorithm GA to calculate the size. The GA-BVSI analytical-metaheuristic method, compared with existing methods, is more suitable and recommended for operators who are faced with the problem of sizing capacitors to improve the technical performance of their network. Keywords: Optimal sizing and locations; Capacitors; Radial distribution system; New stability index;Hybrid method.

A Multi Objective Multi-Stage Particle Swarm Optimization MOPSO Search Scheme for Power Quality and Loss Reduction on Radial Distribution System

There is an increasing interest in renewable and green energy sources and the integration of Distributed Generation DG into the power grid system. The problem of optimal capacitor allocation in electric distribution systems involves maximizing energy utilization, feeder loss reduction, and power factor correction. The feeder loss can be separated into two parts based on the active and reactive power loss components. The paper presents a novel method for minimizing the loss associated with the reactive component of branch currents by placing shunt capacitor optimally selected banks. This paper presents a novel technique for capacitor sizing using the Multi objective multi-stage Particle Swarm Optimization MOPSO to determine optimal capacitor sizes in a radial distribution system. The main objective functions are: 1. Minimize the feeder current for feeder loss reduction, 2. Minimum voltage deviation at each bus of the distribution system, and 3. Feeder capacity release