Reference Voltage Research Articles

Designing a High-Order Sliding Mode Controller for Photovoltaic- and Battery Energy Storage System-Based DC Microgrids with ANN-MPPT

This paper introduces a robust proportional integral derivative higher-order sliding mode controller (PID-HOSMC) based on a double power reaching law (DPRL) to enhance large-signal stability in DC microgrids. The microgrid integrates a solar photovoltaic (SPV) system, an energy storage system (ESS), and DC loads. Efficient DC-DC converters, including bidirectional and boost converters, are employed to maintain a constant voltage level despite the lower SPV output power. An artificial neural network (ANN) generates the optimal reference voltage for the SPV system. The dynamical model, which incorporates external disturbances, is initially developed and based on this model, and the PID-HOSMC is designed to control output power by generating switching gate pulses. Afterwards, Lyapunov stability theory is used to demonstrate the model’s closed-loop stability, and theoretical analysis indicates that the controller can converge tracking errors to zero within a finite time frame. Finally, a comparative numerical simulation result is presented, demonstrating that the proposed controller exhibits a 58% improvement in settling time and an 82% improvement in overshoot compared to the existing controller. Experimental validation using processor-in-the-loop (PIL) confirms the proposed controller’s performance on a real-time platform.

Investigating the Power Quality of an Electrical Distribution System Stressed by Non-Linear Domestic Appliances

: Power quality has become a matter of growing concern in recent years owing to a daily rise in use of non-linear loads at domestic level. In addition, fast growing technologies like distributed generation and electric vehicles are emerging as part of modern distribution systems. Therefore, it is necessary to evaluate and analyze the power quality issues due to various non-linear home appliances to give a clear picture of the current scenario. So that future technologies can be accommodated in the distribution systems while coping with existing power quality issues. The experimentally developed harmonic models, of various commonly deployed domestic appliances, are used for the simulation of a practical distribution system in Electrical Transient Analyzer Program (ETAP). Experimental results combined with simulation results show an alarmingly high level of harmonic distortion, breaching the recommended standard practices, in both current and voltage at not only point of common coupling (PCC) but also at the consumer’s end. In addition to increase in losses, this also degrades power factor giving rise to distortion power. While discussing other impacts of harmonic distortion, true power factor (PFtrue) and distortion power (D) has also been evaluated at PCC.

Combined operation of UPQC and fuel cell with common DC bus

In this paper analysis results of a cooperation of the unified power quality conditioner with the fuel cell units are presented. A comprehensive control strategy is introduced to extract the compensating signals for the control of the proposed system. The used control strategy can extract the reference currents and voltages of UPQC fast and accurately in the presence of harmonics and/or frequency oscillation. The proposed scheme can compensate voltage sag, voltage interruption and harmonics at the Point of Common Coupling (PCC) on power distribution system. The simulation results prove the efficiency of using the presented method on power quality improvement

Review of methods for a Hybrid Energy System islanding efficient management

Nowadays, when a long time problem happens in the electric net (e.g., voltage falls), Hybrid Energy Systems (HES), those which combine wind turbine generators, solar panels, fuel cells, etc. can be disconnected of it to avoid damages. This kind of system can be used, for example, to support the electrical network for distant sites. The aim of the research described in this paper is to study the possibility that the HES, once it has been disconnected, could be able to provide energy to near loads creating a micro-grid (islanding scenario). Furthermore, the DC/AC inverters used by the HES must find a new voltage and frequency reference after disconnection in order not to perturb the generated electrical signal. When in islanding, it is very important to know the characteristics of the loads to assign priorities to each one in order to connect or disconnect them depending on the available energy. The control of that feature is going to be performed by means of Demand Side Management (DSM), using wireless communications and analyzing different options.

Robust Voltage Vector-Controlled Three-Phase SAPF-based BPMVF and SVM for Power Quality Improvement

The multiplication of nonlinear loads leads to significant degradation of the energy quality, thus the interconnection network is subject to being polluted by the generation of harmonic components and reactive power, which causes a weakening efficiency, especially for the power factor. In three-phase systems, they can cause imbalances by causing excessive currents at the neutral. This research treats the operation of robust voltage-oriented control (VOC) for a shunt active power filter (SAPF). The main benefit of this technique is to guarantee a decoupled control of the active and reactive input currents, as well as the input reference voltage. To sustain the DC voltage, a robust PI-structure-based antiwindup is inserted to ensure active power control. Besides, a robust phase-locked loop (PLL)-based bandpass multivariable filter (BPMVF) is used to improve the network voltage quality. Furthermore, a space vector modulation (SVM) is designed to replace the conventional one. A sinusoidal network current and unitary power factor are achieved with fewer harmonics. The harmonics have been reduced from 27.98% to 1.55% which respects the IEEE 519-1992 standard. Expanded simulation results obtained from the transient and steady-state have demonstrated the high performance of the suggested control scheme.

Assessment of the Performance of Frequency Domain Models Based on Different Reference Points for Linearization

Power electronic-based loads are increasingly used in modern distribution systems. Such appliances inject harmonic currents into the power system and thus, they can have serious impacts on the power quality of the system. Therefore, it is necessary to find suitable frequency domain models of these loads to predict their contribution to the harmonic voltage levels. So far, various modeling approaches have been proposed in the relevant literature. Usually, they are parameterized based on laboratory measurements. In this paper, however, a circuit-based time domain model of a Compact Fluorescent Lamp (CFL) is implemented in MATLAB®/Simulink® and used for the parameter identification of three different frequency domain models. These models usually rely on a linearization of their emission characteristic and therefore, it is required to choose an appropriate reference voltage. As the supply voltage in real distribution systems is already distorted, the commonly made assumption of a sinusoidal reference voltage may increase the inaccuracy of models for strongly non-linear devices. Therefore, different reference points for the model parameterization are specified and their impact on the model performance under typical voltage distortions is studied.

Defect recognition of oil‐pressboard insulation based on frequent arc reignition phenomenon under switching impulse voltage

AbstractIn the past three years, a number of oil‐immersed ultra‐high‐voltage shunt reactors have experienced discharge defects when being put into operation, resulting in an overload of acetylene. However, detecting and identifying these discharge defects caused by switching impulse voltage is challenging under steady‐state conditions. This poses unpredictable and difficult‐to‐assess safety risks for the longterm operation of the equipment and subsequent transient processes. Hence, comprehending the discharge behavior of oil‐pressboard (PB) insulation under switching impulse voltage and devising a method to identify defects becomes crucial. This study focuses on investigating the frequent arc reignition (FAR) pattern exhibited by typical defects under both standard and oscillation switching impulse voltages. The objective is to uncover the mechanism behind FAR and propose a defect recognition strategy suitable for transient processes. The study reveals the FAR process will occur at least once during the breakdown process; the FAR phenomenon is the weakest in the surface defect with a weak vertical electric field. The average recovery voltage percentage and the average discharge interval of the FAR process decrease with increasing impulse amplitude or oscillation frequency. Additionally, the average number of discharges decreases with higher oscillation frequency, while it initially increases and then decreases with increaseing amplitude. Based on the analysis of the number of FAR processes and their variation in terms of amplitude or discharge interval, a method for recognizing oil‐PB defects during switching transient processes is developed and successfully applied to a case study involving acetylene overload in a 1000 kV shunt reactor.

Hexagonal zoning deadbeat model predictive control of induction motor

AbstractDeadbeat model predictive control is known as a precise control method in motor control applications. Recently, finite set deadbeat control is applied to power electronics applications. A voltage‐based cost function is used to select the closest voltage vector to the reference vector by examining all possible states in the cost function. This method needs a high amount of computation when it is applied to multilevel inverters or virtual voltage vectors due to a considerable increase in the number of candidates. Some studies have been done to reduce the number of candidate voltage vectors and computation burden. The methodology in these methods is based on finding the adjacent area and applying the vertex vectors to the cost function. So, these strategies are multi‐step and complex. The hexagonal zoning deadbeat model predictive control is proposed and introduced. The basic idea of the proposed method is that each zone should contain only one vector. In this way, the area identification is equal to finding the optimum voltage selection. Hexagonal zoning is used, which simplifies the process of identifying the area. A simple area identification method is developed based on the reference voltage phase angle. The proposed method can be applied to different inverters with different spatial vector diagrams. Furthermore, the synthesis of the virtual vector is manipulated to reduce the torque and flux ripple. The proposed method is experimentally evaluated by applications to the virtual voltage switching of a two‐level inverter for the induction motor drive in this research.

Normalized Error-Based PI Controller and Its Application to the DC–DC Buck Converter

In this article, the structure of the normalized error-based proportional-integral (PI) controller is presented and its application to the DC–DC buck converter is discussed. The main motivation is to overcome the drawbacks of saturation and the limited range of controller gains offered by the traditional PI controller. Initially, the theoretical structure and advantages of an improved PI controller are shown. Next, the problem of regulation of the step-down DC–DC converter is addressed using the proposed controller. The objective is to keep the load voltage constant even when the converter parameters vary. The averaged state-space model of the converter is presented and a detailed stability analysis based on the Lypunov indirect method is carried out. The results show an improved range of controller parameters when the proposed controller is employed. Finally, some simulation results are shown to illustrate the effect of controller parameter variations on the output response. These results also verify the ability of the proposed controller to handle the changes in the load, input voltage, and reference voltage of the converter. Moreover, a comparative simulation study validates the superior transient response of the proposed normalized error-based PI controller over the traditional PI controller.

Frequency-dependent component of AC-DC transfer difference of a redesigned calorimetric thermal converter

This article presents an analysis of the frequency-dependent component of the AC-DC transfer difference of a calorimetric thermal voltage converter (CTVC) with substantially reduced DC reversal error. It contains an analysis of the effect of this reduction on the AC-DC transfer difference. The analysis was based on the mathematical model of a CTVC waveguide, which was extended by estimation of the residual parameters of the CTVC heater (microwave rod resistor) using the finite element method (FEM). The results of the analysis were validated by laboratory measurements. The article also contains the uncertainty budget of the AC-DC transfer difference of the new CTVC. Despite its high thermal time constant, the CTVC is currently one of the most accurate AC voltage standards in the frequency range from 1 to 100 MHz.

Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.

ANFIS controlled fuel cell powered series active filter for voltage waveform augmentation in the distribution grid

The high penetration of nonlinear and unstable loads into the grid network severely impacts the quality of power delivered by the grid system. Furthermore, it leads to a disruptive situation of maintaining good quality power in the distribution sectors. Consequently, maintaining a good quality of power is very difficult on the power distribution grid. In this proposed work, the Proton exchange membrane Fuel Cell sustained by a three-phase Series Active Filter (PFC-SAF) is employed to enhance the quality of sensitive load voltage in the distribution grid. The generalized bell-shaped membership function based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to generate the reference voltage signals of the PFC-SAF. The proposed system is modelled and analyzed in a Matlab/Simulink environment. The performance of the PFC-SAF is investigated based on various fault conditions and voltage harmonics. From the obtained results, it has been found that the PFC-SAF effectively regulates the voltage in the distribution grid. Also, the total harmonic distortion of the sensitive load voltage is reduced from 20.86 % to 0.55 %.

Modified direct torque control algorithm for regeneration capability of IM driven electric vehicle by using hybrid energy storage system

The direct torque control (DTC) algorithm of a 3-phase voltage source inverter (VSI) has been implemented to drive induction motor (IM) for EV applications. However, the conventional DTC for IM is distressed due to large torque ripple, high current total harmonic distortion, and variable switching frequency. So, in this manuscript, a modified direct torque control (MDTC) algorithm is proposed to tackle the said challenges. A three-level hysteresis controller is proposed in modified DTC to optimize the duty ratio of the fundamental voltage vector to minimize the error between the reference and final voltage vector imposed on IM terminals. To improve the efficiency of the EVs by developing a system that uses the energy produced during regenerative braking. The proposed method recovers electric energy produced by the EV without using any additional power converter. The switching patterns allow power flow in both directions. Later, a hybrid energy storage system (HESS) is integrated to supply and store the energy in an optimal way. Extensive studies have been performed to show the efficacy of the proposed MDTC technique in IM based EV application with HESS. The key findings indicate that the proposed MDTC provides better dynamic and steady-state performance.

A Centralized Shifted Voltage Control Method for Accurate Power Sharing in DC Islanded Microgrids

Due to line impedance mismatch among renewable energy sources (RESs), it is hard to realize accurate power sharing in the DC microgrid system. To solve this issue, a distributed power sharing strategy for adjusting the RES output voltage is developed by adding shifted output voltage into each local controller. Thanks to the shifted voltage, the influence of voltage drop caused by the droop controller is effectively mitigated, so that the DC bus voltage is constantly balanced regardless of the load changes. The proposed method is realized with a centralized approach, and all the required control variable to determine the reference voltage is transmitted through low-bandwidth communication. The controller design and system stability are analyzed in detail with a simplified microgrid model. Small-scale DC microgrid is simulated to verify the effectiveness of the centralized shifted voltage control method.

Improved Feedback Quantizer with Discrete Space Vector.

The use of advanced modulation and control schemes for power converters, such as a Feedback Quantizer and Predictive Control, is widely studied in the literature. This work focuses on improving the closed-loop modulation scheme called Feedback Quantizer, which is applied to a three-phase voltage source inverter. This scheme has the natural behavior of mitigating harmonics at low frequencies, which are detrimental to electrical equipment such as transformers. This modulation scheme also provides good tracking for the voltage reference at the fundamental frequency. On the other hand, the disadvantage of this scheme is that it has a variable switching frequency, creating a harmonic spectrum in frequency dispersion, and it also needs a small sampling time to obtain good results. The proposed scheme to improve the modulation scheme is based on a Discrete Space Vector with virtual vectors to obtain a better approximation of the optimal vectors for use in the algorithm. The proposal improves the conventional scheme at a high sampling time (200 μs), obtaining a THD less than 2% in the load current, decreases the noise created by the conventional scheme, and provides a fixed switching frequency. Experimental tests demonstrate the correct operation of the proposed scheme.

Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.

A Novel Tri-Mode Bidirectional DC–DC Converter for Enhancing Regenerative Braking Efficiency and Speed Control in Electric Vehicles

A bidirectional dc–dc converter is used to match the voltage levels between a low-voltage battery and a high-voltage traction machine in an electric vehicle. Using a conventional bidirectional converter with a standard voltage range, there is a limitation to the fine variation in the electric vehicle speed. During the regenerative braking process, when the speed decreases below a certain value, the generated voltage is insufficient to charge the battery, hence the regenerated energy cannot be stored. This paper proposes a novel bidirectional converter featuring three distinct operational modes: boost, buck and buck-boost. In the normal driving mode, it operates as a boost converter, providing double gain and accommodating a wide voltage range. During regenerative braking, the proposed converter switches to the buck or buck-boost mode based on the control algorithm. This adaptation is intended to either decrease the generated voltage to charge the battery effectively or to raise the voltage if it is insufficient for charging the battery. This configuration provides voltage stress of half the dc link voltage on the switches. This paper provides a comprehensive analysis of the proposed circuit, a detailed description of the control strategy with pulse generation logic for all switches and a mode transition algorithm. The simulation results of a circuit operating at a 1500 W power level are presented and compared with those of a standard bidirectional converter.

Study and Modeling of Nonlinear Dynamic Characteristics of DC-DC Converters with Amnesia Loads

Abstract The chaos phenomenon is a seemingly random irregular motion occurring in a deterministic system, which is an inherent characteristic and prevalent phenomenon of amnesia-loaded DC-DC converter nonlinear systems. Nonlinear dynamics and chaos theory guide the text, and we create an amnesia-loaded DC-DC converter system model using an amnesia resistor. We analyze the stability of the system using Filippov theory, and analyze the nonlinear dynamic characteristic behavior of the amnesia-loaded DC-DC converter using the time-domain waveform diagram, bifurcation diagram, and phase-track diagram. This paper talks about the slope compensation method and the parametric resonance perturbation method for control analysis. These methods are used to stop the chaotic behavior that happens in the amnesia-loaded DCDC converter. The system maintains a stable state when the reference current is less than 4.732A and the supply voltage exceeds about 17.966V, maintaining an amplitude of the maximum eigenvalue below 1. When the load resistance is 50Ω, the capacitance is 43 uF, the inductance is 2.8 mH, and the reference voltage is 2.05 volts, the amnesia-loaded DC-DC converter suffers from obvious chaos. To restore the system to a steady state by applying the ramp compensation method, the slope of the ramp signal will need to increase as the reference current increases. Loading amnesia onto the DC-DC converter achieves a stable cycle 1 state when the perturbation phase’s fluctuation range falls between 4.35 and 6.15. A nonlinear dynamic analysis of the amnesia-loaded DC-DC converter helps find the stable parameters of the amnesia-loaded DC-DC converter and improve the design of the converter.

Ultra-low power magneto-elastic analog-to-digital converter based on magnetic tunnel junctions and bicomponent multiferroic nanomagnet

<sec>In recent years, the utilization of artificial intelligence and big data has led to the rise of compute-in-memory signal processing as the primary method for ADC design. Spintronic memory devices, which have non-volatile and low static power consumption characteristics, are particularly suitable for the design of low-power, high-bandwidth compute-in-memory ADCs.</sec><sec>In this paper, a 3-bit magneto-elastic analog-to-digital converter (MEADC) is proposed, which comprises eight magnetic tunnel junctions (MTJs), where the MTJ free layer is a bicomponent multiferroic nanomagnet. The bicomponent multiferroic nanomagnet can attain deterministic magnetization switching under zero-field condition by regulating the strain-mediated voltage. It has been discovered that there is a linear correlation between the thickness of the piezoelectric layer and the critical flip voltage in a bicomponent multiferroic nanomagnet of a given size and material. Using this principle, the thickness of the piezoelectric layer is adjusted to allow the MEADC to have eight different voltage switching thresholds. This can make the analog signal converted into a combination of different magnetization states of eight multiferroic MTJ. A latch comparator and an independent read circuit are designed to detect the MTJ’s resistance state output a digital signal. Monte Carlo simulations indicate that the MEADC can achieve a 100% success rate of writing at room temperature. Additionally, the read circuit and write circuit are separated from each other, thus the same reference voltage can be set for each MTJ and result in higher readability. Micromagnetic simulation and numerical analysis demonstrate that the MEADC can operate at a maximum frequency of 250 MHz, and the energy consumption of a single conversion is only 20 aJ. Compared with the magnetic analog-to-digital converter based on the Racetrack technology, the energy consumption is reduced by 1000 times, and the sampling rate is increased by 10 times. The MEADC proposed in this paper offers an essential technical support for the spintronics-based compute-in-memory integrated circuit architecture.</sec>

Reinforcement Learning-Based Integrated Control to Improve the Efficiency of DC Microgrids

The hierarchical control of the DC microgrid regulates the terminal voltages of the interfacing converter to achieve proportional load sharing and good voltage regulation at the DC bus. In doing so, the difference of the voltage at different nodes increases which results in higher circulating current and leads to higher losses. In this paper, a Reinforcement Learning Based Integrated Control (RLIC) is proposed which will minimize the circulating current and power losses in the transmission. The proposed RLIC consists of a primary and secondary controller. The primary controller is a robust sliding mode controller which receives the voltage references from secondary controller and regulates the terminal voltage and source current accordingly. The secondary control consists of a proportional integral control (PI), and a Deep Neural Network (DNN) surrogate model with implementation of Q-Learning as reinforcement method. A novel DNN based surrogate model uses the droop value from the PI controller and estimates the power loss and the local and global loading difference for a particular node, for a set of operating condition. This surrogate model is used by the Q-Learning based reinforcement technique which adjusts the droop constants and provide the voltage reference to primary controller to maintain load sharing and reduce the power losses, there-by leading to an improved overall efficiency. The proposed control structure is verified to improve efficiency while maintaining the load sharing and bus voltage regulation.