DC Bus Research Articles

Voltage stability control strategy for DC microgrid based on adaptive virtual DC motor

The large-scale integration of distributed energy sources and power electronic devices results in the DC microgrid exhibiting significant low inertia and weak damping characteristics. This, in turn, leads to inevitable fluctuations in the DC bus voltage, which endanger the stable operation of the DC system. Energy storage devices can provide equivalent inertia. To enhance the inertia and response speed of the DC bus interface converter, this paper proposes a power allocation parameter adaptive virtual DC motor control strategy based on a hybrid energy storage unit. The strategy introduces power allocation control to regulate the energy storage converter on the basis of virtual inertia parameter adaptive control, thereby enabling the energy storage converter to simulate the inertia and damping characteristics of a DC generator. The small-signal stability of the system is analyzed by establishing a small-signal model of the photovoltaic energy storage system and utilizing the impedance ratio criterion. Finally, the proposed control strategy is validated through simulation. The results demonstrate that the strategy effectively mitigates the fluctuations in bus voltage under varying photovoltaic power and sudden load changes, ensures the power distribution in the hybrid energy storage system, and enhances the dynamic response of the system.

PWM Strategy in OVM and Six-Step Regions for Open-End Winding PMSM Fed by a Dual Inverter With a Common DC Bus

PWM Strategy in OVM and Six-Step Regions for Open-End Winding PMSM Fed by a Dual Inverter With a Common DC Bus

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

Active damping control based bus voltage resonance suppression in electrolytic capacitorless PMSM application

Abstract The structure of electrolytic capacitorless in automotive electronic applications has the advantages of lower cost and better reliability. However, motor drive systems using this structure may experience unstable bus voltage. To this end, this paper proposes an active damping control strategy based on virtual impedance. By modelling the electric drive system and conducting stability analysis, the cause of bus voltage resonance due to the use of small capacitors is revealed. Based on the extracted DC bus voltage harmonic signal, additional voltage vectors are added in the d-axis and q-axis voltage vectors, eliminating the impedance mismatch problem in the drive system caused by the reduction of bus capacitance, effectively suppressing the voltage oscillation on the machine side of the drive system. Simulation data shows that under the condition of using small-capacity ceramic capacitors, the method proposed in this paper achieves a harmonic suppression capability similar to that of large-capacity electrolytic capacitors, effectively ensuring the application safety of automotive drivers with electrolytic capacitorless architecture.

Theoretical and simulation analysis of DC bus current fluctuation control strategies of modular multilevel converter under AC side fault conditions

Modular Multilevel Converters (MMCs) play a crucial role in high-voltage DC (HVDC) systems due to their adaptable control and swift response capabilities. However, AC side faults could introduce asymmetric double-line frequency components into the MMC's circulating current, jeopardizing DC bus stability. Although strategies to mitigate DC side fluctuations are well-established, a detailed comparison of their operational impacts remains unexplored. The operational effects of MMCs under AC side faults are firstly examined in this study, including issues related to grid-connected current asymmetry and DC bus current fluctuations. Subsequently, two targeted control schemes are devised based on the goals of suppressing the double-line frequency components and only the zero-sequence components of circulating current. Through simulation, these approaches are contrasted, delineating their advantages and disadvantages in terms of circulating current suppression, submodules(SMs) capacitance voltage fluctuation, and other performance metrics, which could provide a foundational reference for designing and selecting MMC control strategies under AC side faults.

A high-frequency reduced-order model for parameters optimization of AC/DC distribution systems considering random disturbances

Accompanying the rapid development of AC/DC distribution systems, along with the random disturbances introduced by a significant number of distributed generations and loads, the difficulty of system stability analysis has increased. The phenomenon of DC bus voltage instability has become severe, affecting the safe and stable operation of power systems. This paper focuses on the research of multi-terminal AC/DC distribution systems. A reduced-order method for multi-terminal distribution systems is proposed. Through parameter equivalent transformation, the system model is simplified while maintaining its dynamic characteristics. Parameter sensitivity analysis is utilized to identify key parameters influencing the system’s dynamic behavior, achieving an analytical expression of the system’s dynamic characteristics. And stability domain analysis is applied to delineate the variations in system dynamic behavior and responses to random disturbances under different parameter settings. Then a multi-parameter optimization method is designed to enhance system stability while achieving fast system response. Finally, theoretical analysis is validated through software simulation and hardware-in-the-loop experiments.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid

This study relates to the enhancement in performance of a Hybrid Energy Storage System (HESS) over a DC microgrid, particularly toward a fast-accurate controller of the DC bus voltage. Conventional control methods are particularly difficult to implement in order to reduce start-up time with minimum overshoot, resulting in a significant disturbance of the DC bus voltage. To circumvent these problems, we propose an innovative hybrid controller method that combines the Integral Super-Twisting Algorithm (ISTA) controller technique with adaptive gain in regulating DC bus voltage and current. The capacity of ISTA to greatly enhance the system's dynamic responsiveness, guaranteeing steady operation with effective output voltage control, is by far its greatest benefit. Comparative testing found that the conventional PI controller had a rise time of 0.0666 s, a settling time of 0.165 s, and a 17 % overshoot with a steady-state error of 0.02 %. The proposed ISTA controller had a rise time of 0.0016 s, a settling time of 0.0667 s, zero percentage overshoot, and a minimal steady-state error of only 0.0003 %. The above-mentioned issues mostly focused on better sharing power in HESS and a more effective controller of the DC bus voltage. Furthermore, we evaluate the practical feasibility of the suggested controller through hardware-in-the-loop testing. This research lays a foundation for advancing renewable energy integration in DC microgrid, offering a promising avenue for sustainable and resilient power systems. Finally, the effectiveness and applicability of the proposed ISTA controller for DC microgrid use are proven with Controller Hardware-in-the-Loop (C-HIL) simulations.

Active Disturbance Rejection Control Combined with Improved Model Predictive Control for Large-Capacity Hybrid Energy Storage Systems in DC Microgrids

In DC microgrids, a large-capacity hybrid energy storage system (HESS) is introduced to eliminate variable fluctuations of distributed source powers and load powers. Aiming at improving disturbance immunity and decreasing adjustment time, this paper proposes active disturbance rejection control (ADRC) combined with improved MPC for n + 1 parallel converters of large-capacity hybrid energy storage systems. ADRC is utilized in outer voltage control loops, and improved MPC is employed in inner current control loops of n battery converters. Droop control is adopted to obtain power distribution between n battery converters, and a DC bus voltage compensator is used to compensate voltage deviations and maintain constant DC bus voltage. The low-pass filter (LPF) is adopted to obtain high-frequency power as the reference for the supercapacitor converter, ADRC is also utilized in the outer power control loop, and MPC is employed in the inner current control loop. Compared with traditional observers, the voltage expansion state observer of the proposed ADRC control is independent of the system model and parameters and consequently has strong disturbance immunity, and significantly reduces voltage overshoots during power fluctuations. The MPC-based inner current control loops of n + 1 converters accelerate current response speed and significantly decrease switching losses. Simulation and experimental results indicate that utilizing the proposed control strategies, large-capacity HESS has stronger anti-interference ability, shorter regulation time, smaller switching loss, and simultaneously maintains the stability of the DC bus voltage.

Dual Second Order Generalized Integrator – Phase Locked Loop technique applied to a distorted grid-connected solar energy based on a Z-Source Inverter

In this paper, a Dual Second Order Generalized Integrator_Phase Locked Loop (DSOGI_PLL) technique applied to the Photovoltaic (PV) system supplying a Z-Source five-level Inverter (ZSI) is presented. The ZSI assures the increasing voltage of the PV system and provides the desired output DC at the input of the five-level Neutral Point Clamped Inverter (5L_NPCI), without any control which reduces the complexity of the overall system, this is due to the impedance network in its structure. We use Z-Source in order to eliminate the controlled DC bus as well as the use of the DC-DC Boost converter as an intermediary. A DSOGI technique is employed to control and optimize the energy quality, especially in distorted grid conditions and allows one to obtain a very low value of Total Harmonic Distortion (THD) which means lower peak currents, and higher efficiency. Low THD is an essential feature in power systems that international standards such as IEC 61000-3-2, and IEEE-519 set limits on the harmonic currents of various classes of power equipment connected to a distorted three-phase grid. Compared to the classic structure of inverters, the NPC five-level inverter presents a very high performance.

A Detailed Analysis and Gain Derivation of Reconfigurable Voltage Rectifier-Based LLC Converter

In this paper, a complete analysis of an LLC resonant converter with a customized rectifier structure is presented. The converter is intended for wide, low-input, high-output voltage DC bus applications. The performance of the converter is assessed using comprehensive time-domain and fundamental harmonic approximation (FHA), which demonstrates its capacity to operate across an ample range of voltages by precisely adjusting the rectifier structure. The converter’s capability is illustrated by deriving and discussing detailed mode operation, steady-state analysis, and DC gain equations. In order to verify the theoretical analysis, a prototype with a power output of 250 watts is constructed and subjected to testing. The results of the testing demonstrate that the converter is both feasible and effective. The experimental findings illustrate its capacity to manage vast voltage ranges while upholding high efficiency. In addition, the converter utilizes a frequency switching modulation (FSM) to connect with a photovoltaic (PV) panel and control the high output voltage. This demonstrates its adaptability in renewable energy applications. The validation is in accordance with theoretical predictions, demonstrating the converter’s high-efficiency performance and versatility.

Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system

In order to extract the maximum photovoltaic (PV) power under stochastic weather conditions, a maximum power point tracking (MPPT) technique is required. PV modules behave nonlinearly in unpredictable weather conditions, and the effectiveness of many control strategies under such conditions especially conventional ones—decreases significantly. It is essential to maximize the use of PV power in the system; thus, MPPT algorithms have been developed to ensure that grid integrated PV systems work best at the maximum power point regardless of weather conditions. This research presents a novel HLO-ANN (Horned Lizard optimized artificial neural network) MPPT technique for grid-integrated solar photovoltaic systems which is the improved version of the ANN-MPPT, developed using a novel HLO technique, which effectively optimizes weights of the neural network. The irradiance and temperature datasets from the NASA website have been utilized to train the neural network. The proposed methods have been evaluated by comparing their simulation results to other ANN optimized MPPT techniques: The results showed that the proposed method outperforms the other methods in terms of robustness, DC bus voltage regulation, tracking speed, convergence to the minimum error value, and tracking efficiency in maximizing the harvested power from the PV systems. Furthermore, the EN50530 MPPT efficiency test was carried out during both fast and slow-varying irradiance levels to examine the effectiveness of proposed algorithms.

Design of DC bus voltage high dynamic performance control for single-phase converters

Design of DC bus voltage high dynamic performance control for single-phase converters

DUAL LOOP VOLTAGE DROOP REGULATED CONTROLLER FOR DC MICROGRID USING HYBRID PSO AND GGO ALGORITHMS

Abstract DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance & settling time for output voltage has been mitigated more than 31ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.

Control of Solar Photovoltaic Integrated Universal Active Filter Using Artificial Intelligence Technique

This paper organizes, a different technique based on adaptive filtering is proposed for the control of three phase worldwide active power filter with a solar photovoltaic array integrated at its DC bus. Two adaptive filters along with a zero crossing detection method are used to remove the magnitude of fundamental active component of distorted load currents, which is then used in estimation of reference signal for the shunt active filter. This technique enables extraction of active component of all three phases with reduced mathematical computation. The series active filter control is based on synchronous reference frame theory and it regulates load voltage and maintains it in-phase with voltage at point of common coupling under conditions of voltage sag and swell. In this paper An artificial intelligence technique fuzzy logic controller is used. The performance of the system is evaluated on an MATLAB/SIMULINK software under various dynamic conditions such as sag and swell in voltage at point of common coupling, load unbalancing and change in solar irradiation intensity.

MITIGATION OF POWER QUALITY PROBLEMS IN STEEL PLANTS USING STATIC SYNCHRONOUS COMPENSATOR

This paper focuses on mitigating various power quality issues from electric arc furnaces (EAF) in steel plants, including voltage flicker, sag, swell, and harmonics. Arc furnaces are recognized as one of the industry's most nonlinear electrically polluting loads. The proposed solution in this article to address all power quality issues caused by EAF is implementing a flexible power supply strategy known as a static synchronous compensator (STATCOM) for injecting reactive energy into the electrical grid. In this study, an experimental model of EAF is utilized based on real measurements of the variation of arc resistance and arc reactance during the melting phase. The pulse width modulation (PWM) technique is employed for current tracking feedback control to generate the required reactive current. A fuzzy logic controller (FLC) is employed for the DC bus.

Design and Experimental Testing of Extended-Range Power Supply System for 15 Horsepower Electric Tractor

Electric tractors have many advantages, including high torque, excellent controllability, energy efficiency, a simple structure, and an electric interface for expansion. However, a significant limitation lies in their endurance. This study presents the design of an extended-range power supply system to ensure continuous endurance for an electric tractor. The objective is to provide a continuous power source for our self-developed electric tractor while preserving the benefits of electric propulsion. Extended-range power systems utilize a primary mover, typically an oil-fueled internal combustion engine, to drive the generator for electricity generation, and the generated AC-form electricity is subsequently converted into stable DC bus voltage by a power electronic converter. The hardware and control design of an extended-range power supply system are finalized and validated through experimental trials. The results demonstrate the system’s capability to sustain stable DC bus voltage amidst disruptions such as sudden load shifts and fluctuations in the prime mover’s speed. Even with a 50% sudden load change, the voltage drop is within 12% and can recover to ±3% within 4 s. The extended-range can be used alone without a battery to power the electric tractor, or it can used in parallel with other extended ranges or batteries for power sharing thanks to the droop control ability.

Rule‐based energy management system for autonomous voltage stabilization in standalone DC microgrid

AbstractThis paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios.

Testing Algorithms for Controlling the Distributed Power Supply System of a Railway Signal Box

Trends in the use of renewable energy sources to power buildings do not bypass objects for which maintaining a power supply is critical. This also applies to railway signal boxes. The aim of the research work was to test the multisource power supply system for a railway signal box with power electronic converter systems and a DC bus, built as part of the research project. The assumption for powering the railway signal box building was to use renewable sources, energy storage devices, and a 3 kV DC traction network as the second required power supply grid. Both power grids were connected by power electronic converters, and the power values of the converters were set based on the calculated power balance values using the values measured at the system nodes and the set constraints. The tests primarily tested the response of the power supply system to changes in load power and power generated by the photovoltaic source, as well as the charge level of the energy storage devices. The correctness of the control algorithm’s operation was assessed based on the recorded power values in the power supply system nodes. The tests were carried out for 60 scenarios that covered all normal and emergency operating conditions. During the tests, delays in response to changes in the power supplied to the converters and the values of circular power flow between the power grid connections were recorded. The recorded delays ranged from 2 to about 50 s and the circular power flows did not exceed 1500 W. Based on the results of the tests, it was found necessary to improve the power measurement system in the power supply system nodes and to improve the quality of communication and the transmission time of measurement data transmission time.

Optimal design of hybrid green energy powered reduced switch converter based shunt active power filter using horse herd algorithm

Renewable energy sources are playing a leading role in today's world. However, integrating these sources into the distribution network through power electronic devices can lead to power quality (PQ) challenges. This work addresses PQ issues by utilizing a shunt active power filter in combination with an Energy Storage System (ESS), a Wind Energy Generation System (WEGS), and a Solar Energy System. While most previous research has relied on complex methods like the synchronous reference frame (SRF) and active-reactive power (pq) approaches, this work proposes a simplified approach by using a neural network (NN) for generating reference signals, along with the design of a five-level reduced switch voltage source converter. The gain values of the proportional-integral controller (PIC), as well as the parameters for the shunt filter, boost, and buck-boost converters in the WEGS and ESS, are optimally selected using the horse herd optimization algorithm. Additionally, the weights and biases for the neural network (NN) are also determined using this method. The proposed system aims to achieve three key objectives: (1) stabilizing the voltage across the DC bus capacitor; (2) reducing total harmonic distortion (THD) and improving the power factor; and (3) ensuring superior performance under varying demand and PV irradiation conditions. The system's effectiveness is evaluated through three different testing scenarios, with results compared against those obtained using the genetic algorithm, biogeography-based optimization (BBO), as well as conventional SRF and pq methods with PIC. The results clearly demonstrate that the proposed method achieves THD values of 3.69%, 3.76%, and 4.0%, which are lower than those of the other techniques and well within IEEE standards. The method was developed using MATLAB/Simulink version 2022b.