Stand-alone Mode Research Articles

Development of constant switching frequency model predictive control for DFIG-based wind energy conversion system in standalone and grid connected operation

This paper presents a constant switching frequency (CSF) model predictive control (MPC) for a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) in standalone and grid-connected operations. The system comprises a cascade connection of a rotor side converter (RSC) and a grid side converter (GSC) with a common DC-link. A battery energy storage system (BESS) is also connected to the common DC-link via a bi-directional dc–dc converter (BDDC). The RSC is controlled through CSF-model predictive current control (CSF-MPCC) scheme in both standalone mode (SAM) and grid-connected mode (GCM) of operations. The GSC control employs the CSF-MPCC scheme in grid-connected operation, whereas CSF-model predictive voltage control (CSF-MPVC) scheme is implemented in standalone operation. Moreover, the power balance is achieved by controlling the BDDC using MPCC to discharge or charge the BESS in response to surplus or deficient power generation. The effectiveness of the proposed control approach for DFIG-based WECS in both SAM and GCM is verified through MATLAB-based simulation and experimental results under different dynamic conditions like variable wind velocity changing from 12 m/s to 8 m/s, different grid demands from 3 kW to 1.5 kW and load change of 4 kW to 6.5 kW. Moreover, the robustness of the proposed control is corroborated by parametric sensitivity, time delay, and grid unbalance, along with its low-voltage ride-through (LVRT) capability during an LLLG fault.

Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation

Software Defined Networking (SDN) has revolutionized network administration by providing centralized management through software, enabling traffic adjustment independent of the data plane. Despite the benefits, SDN networks are prone to security threats from external sources, thus necessitating the implementation of security measures. Unfortunately, most existing efforts have been just a simple mapping of earlier solutions into the SDN environments. This paper addresses the problem of SDN security based on deep learning in a purely native SDN environment, where a Deep Learning intrusion detection module is tailored to a native SDN environment. In particular, we propose a hybrid Deep AutoEncoder with a Random Forest classifier model (DAERF) to enhance intrusion detection performance in a native SDN environment. The proposed model is incorporated into a novel adaptive framework for attack mitigation in SDN environments. The proposed framework consists of a three-layer protection mechanism for detecting and preventing attacks. It is based on entropy-based detection, hybrid machine learning in the control layer and proactive services monitoring in the application layer. Experimental results have shown that our DEARF proposed autoencoder model achieved anomaly detection rates in excess of 98% in stand-alone mode as well as when incorporated within the framework, making it highly solution for next generation SDN networks.

Intelligent learning approach for transition control and protection of solar PV integrated electric vehicle charging station

Recently, the control of distributed generations (DGs) with electric vehicles (EVs) parking lot charging systems has received much attention for supporting emergency local loads during grid outages. To achieve optimal transition control of grid-connected (GC) and standalone (SA) modes, an intelligent learning-based algorithm is implemented for optimal handling of the disturbance in the system by injecting a fault condition over certain cycles during a weak/strong grid operation. Here, a fuzzy Q-learning (FQL) based algorithm is implemented for detecting and monitoring the distributed grid condition during different scenarios and a seamless transitioning control in GC and SA modes of operation. In this context, the implementation of designed FQL algorithm enables dynamic adjustments of control signals in response to real-time uncertainties in two major applications (grid synchronization and islanding detection mode). The FQL algorithm focuses on real-time decision-making in response to sudden fault scenarios, addressing the problem under various modes of operation. To validate the proposed FQL controller accuracy, a real-time hardware-in-loop (HIL) simulation and their experimental validation is carried out of a 16kWp grid integrated PV sub-system with different load profiles. The performance evaluation of the FQL controller is investigated under linear and nonlinear local load scenarios.

Robust control for improving performance of grid-synchronized solar PV-BES-wind based microgrid

An integration of hybrid renewable energy systems (HRESs) to the grid results in power quality issues at the point of interconnection. This paper proposes an approach to investigate and mitigate power quality issues and uncertainties associated with integration of a HRES that includes a solar photovoltaic (PV) array, a wind generator, and battery energy storage (BES). The system functions in grid tied mode, standalone mode and seamless mode transition between these modes. Primary objective of proposed control approach is to ensure stable and reliable operation while mitigating effects of power quality issues. To achieve this objective, proposed control approach employs advanced control strategies, including generalized fourth order filter (GFOF) for grid tied mode and phase angle estimation. An off-grid mode uncertainty and disturbances are overcome by proportional resonant based control. GFOF addresses multiple power quality issues associated with grid-connected mode, including robust performance during voltage unbalance, harmonics suppression, DC offset removal, weak grid conditions, and evaluation of unit templates. GFOF extracts fundamental load currents and voltages for evaluation of reference grid currents and phase angle. This precisely estimates frequency and phase angle for seamless synchronization. It provides adaptive mode transition. Power quality of currents is enhanced in both islanded and grid-connected modes through utilization of GFOF control, ensuring that total harmonic distortions (THDs) of grid currents remain below 5% (simulated THD is 1.77% and experimental THD is 3.80%), as specified by the IEEE-1547and 519 standards. A comparative analysis has justified efficacy of control. Simulated and experimental results confirm effectiveness of proposed controllers in various scenarios, demonstrating their ability to provide a favourable solution for controlling unfavourable grid conditions with technical strength and precision.

Nonlinear control of two-stage single-phase standalone photovoltaic system.

This paper presents a single-phase Photovoltaic (PV) inverter with its superior and robust control in a standalone mode. Initially, modeling and layout of the Buck-Boost DC-DC converter by adopting a non-linear Robust Integral Back-stepping controller (RIBSC) is provided. The controller makes use of a reference voltage generated through the regression plane so that the operating point corresponding to the maximum power point (MPP) could be achieved through the converter under changing climatic conditions. The other main purpose of the Buck-Boost converter is to act like a transformer and produce an increased voltage at the inverter input whenever desired. By not using a transformer makes the circuit size more compact and cost-effective. The proposed RIBSC is applied to an H-bridge inverter with an LC filter to produce the sinusoidal wave in the presence of variations in the output to minimize the difference between the output voltage and the reference voltage. Lyapunov stability criterion has been used to verify the stability and finite-time convergence of the overall system. The overall system is simulated in MATLAB/Simulink to test the system performance with different loads, varying climatic conditions and inverter reference voltages. The proposed methodology is compared with a back-stepping controller and Proportional Integral Derivative (PID) controller under rapidly varying climatic conditions. Results demonstrated that the proposed technique yielded a tracking time of 0.01s, a total harmonic distortion of 9.71% and a root means square error of 0.3998 in the case of resistive load thus showing superior control performance compared to the state-of-the-art control techniques.

Narrowband IoT

Narrowband Internet of things (NBIoT) is a low energy and low resource consuming version of IoT. As its name suggests, it needs a narrow bandwidth for its operations. Its energy consumption is also very low, and thus it is suitable for low energy applications. It is compatible with all types of cellular communication infrastructure such as 2G, 3G, 4G and 5G. It is also possible to deploy NBIoT in the standalone mode where cellular networks are not available. It can cover a large area with a very small amount of power. So, it is a popular low power wide area (LPWA) technology. Due to its LPWA features, it is popular for the connected living applications at home and workplace surroundings. Its LPWA features make it a popular green technology for digital transformation. In this article, the authors provide the main characteristics of NBIoT, its standards, its potentials, and applications in different domains.

Novel and Robust Forward Kinematic Algorithm for Real-Time Control of General Six-Degree-of-Freedom Parallel Robot for Tele-Manipulation and Tele-Navigation

Abstract The forward kinematics (FK) of a 6-6 universal-prismatic-spherical (UPS) structure of a parallel robot is highly nonlinear, coupled, and has a one-to-many nature of mapping. There exists no close form solution to a forward kinematic problem (FKP), and real-time kinematic control is extremely difficult. This paper presents the implementation of time efficient and robust solution for FKP using a trajectory modifier algorithm along with a Newton Raphson (NR) method. One micrometer in translation and 0.001 deg in orientation accuracy with an average pose computation time of 2.3 ms are achieved. The novel algorithm is elaborated and the detailed performance parameters are tabulated. The paper presents trajectory following experiments to show robust, real-time FK solution and efficient kinematic control on both standalone and master–slave modes to be used for robot-assisted neurosurgery. The neuro-registration using the FK solutions in real time in a tele-manipulation mode is demonstrated.

Comparison of Discharge Performance of 12V/150Ah Gel and AGM Sealed Lead acid Batteries in Stand-alone PV-based Systems in UAE

This paper investigates the discharge performance of two major lead-acid batteries, namely 12V/150Ah Gel and AGM sealed lead-acid batteries, when operating in stand-alone (off grid) mode in photovoltaic (PV) based systems in United Arab Emirates (UAE). At harsh environmental conditions such as in UAE where the temperature can go to extremely high level in the summer, the battery performance and replacement cost increases the concerns about the reliability and economic feasibility of those batteries. This paper compares the discharge characteristics of those batteries through discharge tests performed on four similar batteries (two of each type). Experimental results of the discharge tests followed by a comparison between the two batteries are presented in this paper.

Design of a wind-PV system integrated with a hybrid energy storage system considering economic and reliability assessment

Hybrid energy systems (HESs) have garnered significant attention as a sustainable solution to meet the world's growing energy demands while minimizing environmental impact. Achieving cost-effective and resilient HES solutions requires considering economic and reliability factors for optimal design. This research delves into the optimization and design of a wind-PV system integrated with a hybrid energy storage system using the Multi-Objective African Vultures Optimization Algorithm (MOAVOA) in both standalone and grid-connected modes. A comprehensive case study is conducted in Tabuk, Saudi Arabia, focusing on a microgrid and incorporating wind speed, solar radiation, and load data. The effectiveness of MOAVOA is verified using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) methods in achieving the Pareto front of optimal system designs. Then, the mean, entropy, and criteria importance using the inter-criteria correlation (CRITIC) methods are utilized to assign weights to each objective in the obtained Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is employed to select the optimal system design from the Pareto front. The results underscore the significance of the optimization approach in assessing the cost, dependability, and ideal configurations of the system. Furthermore, they highlight the diverse outcomes produced by different optimization techniques. While the entropy method yields more cost-effective designs, the mean and CRITIC methods produce comparable results. However, the mean and CRITIC approaches exhibit higher levels of reliability. For instance, in the grid-connected mode, the entropy weighting method yields an optimal system design with a total cost of M$ 42.2 and a reliability index of 91.73 %, while using the mean method, the selected system configuration has a total cost of M$ 42.98 and a reliability index of 92.89 %. The study emphasizes the benefits of diversifying renewable resources by considering different scenarios involving wind and solar generation. For example, in the wind-PV grid-connected system, the total cost is 22.65 % less than in the PV-only grid-connected system with a higher system reliability. The findings provide valuable guidance for system designers in selecting optimal optimization techniques and promoting the integration of renewable energy sources in hybrid energy systems. By presenting a comprehensive framework that incorporates economic and reliability assessments through the utilization of MOAVOA, this study contributes to the field of HES design.

Enhancing Grid-Forming Converters Control in Hybrid AC/DC Microgrids Using Bidirectional Virtual Inertia Support

This paper presents a new grid-forming strategy for hybrid AC/DC microgrids using bidirectional virtual inertia support designed to address weak grid conditions. The stability of hybrid AC/DC microgrids heavily relies on the AC mains frequency and the DC-link voltage, and deviations in these factors can lead to undesirable outcomes such as load curtailments and power system congestions and blackouts. This paper introduces a unique approach that leverages bidirectional virtual inertia support to enhance the stability and reliability of hybrid AC/DC microgrids under weak grid conditions. The proposed strategy employs virtual inertia as a buffer to mitigate rapid changes in DC-link voltage and AC frequency, thereby enhancing system stability margins. This strategy significantly contributes to a more stable and reliable grid operation by reducing voltage and frequency fluctuations. A standard hybrid AC/DC microgrid configuration is used to implement the bidirectional virtual inertia support, where a bidirectional interlinking converter control is adjusted to deliver inertia support to both the AC and DC subgrids. This converter utilizes the DC grid voltage and AC grid frequency as inputs, effectively managing active power balance and implementing auxiliary functions. Extensive simulations are conducted under weak grid conditions and standalone mode to validate the effectiveness of the proposed strategy. The simulation results demonstrate a remarkable improvement in frequency nadir, rate-of-change-of-frequency (RoCoF), and DC bus voltage deviation in the hybrid AC/DC microgrids. The bidirectional virtual inertia support substantially reduces voltage and frequency fluctuations, enhancing the microgrid stability and resilience. There is an improvement of over 45% and 25% in the frequency deviation and voltage deviation, respectively, achieved through implementing the proposed control strategy.

The Frequency Regulation Strategy for Grid‐Forming Wind Turbine Generator and Energy Storage System Hybrid System in Grid‐Connected and Stand‐Alone Modes

This paper proposes a coordinated frequency regulation strategy for grid‐forming (GFM) type‐4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid‐connected (GC) and stand‐alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series‐connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady‐state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small‐signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis.

Performance Investigation of PV Battery Integrated Parallel operated Inverter in Standalone Mode

Performance Investigation of PV Battery Integrated Parallel operated Inverter in Standalone Mode

POSSIBILITIES OF IMPROVING THE VOICE SERVICES QUALITY IN 5G NETWORKS

Background. The introduction of fifth-generation (5G) networks creates new opportunities for fast and continuous data exchange, but there are still some problems with the quality of voice services in such networks. With the rapid development of technology and the further spread of 5G, there is a need to understand the impact of key aspects of 5G on voice quality. This requires research that can systematically analyse the features of 5G networks that affect the quality of voice services.
 Objective. Identification of ways to improve the quality of voice services in 5G networks. Assessment of key indicators of voice service quality in 5G networks. Determination of the best option for the gradual transition to the Standalone mode and the use of VoNR technology in the fifth generation networks.
 Methods. Analysis of factors affecting the quality of voice services in fifth-generation networks. Analysis of well-known publications on the implementation of 5G networks. Comparison of the implementation of Non-Standalone and Standalone modes in the 5G network. Testing of the modern EVS codec, which provides an opportunity to improve the customer experience.
 Results. Confirmation that 5G networks can significantly improve the quality of voice services compared to previous mobile communication technologies such as 4G and 3G. Certain factors that may affect the quality of voice services and require additional attention when planning and deploying 5G networks are identified. The optimal steps for the transition to the Standalone mode and the use of VoNR technology in fifth-generation networks are determined. The main differences in the QoS architecture between LTE and 5G are identified, and the purpose of DRB flows for separating traffic types and services is established.
 Conclusions. It has been confirmed that 5G networks can significantly improve the quality of voice services compared to previous technologies such as 4G and 3G. This is possible due to the broadband capabilities of 5G networks, improved data transmission, low latency, the use of an advanced EVS codec and reduced response time. However, certain factors, such as network coverage, optimisation level and traffic characteristics, can affect the quality of voice services and require additional attention when planning and deploying 5G networks. The QoS management architecture consists of QoS flows, which allow separating packet assignment to flows (managed by the CN) from the assignment of DRB flows (managed by the RAN). As 5G networks are being rolled out gradually, it is important to properly integrate the 5G domain into the existing telecoms provider's network. The transition from Non-Standalone to Dual connectivity is a necessary step for the implementation of VoNR technology in Standalone mode. Using the modern EVS codec allows not only improving the customer experience, but also introducing new voice services.

Online joint localization without user interactions

Wi-Fi fingerprinting has been a popular indoor localization due to the widespread layout of indoor WLAN. However, the signal fluctuations in the complex environments make it difficult to maintain high accuracy localization for the received signal strength (RSS) fingerprinting. Various positioning solutions have emerged to address this challenge, either working in stand-alone mode or in collaborative mode. In the former case, the user only utilizes his own RSS observation to request location service, while the latter usually requires information transfer between users. Considering the spatial correlation of wireless signal distribution, we propose an online joint localization scheme (JointLoc) that does not require direct interaction between users. The fact that the signals observed by users in physical proximity characterize the surroundings is used by JointLoc to identify neighboring users for joint localization. Besides this, JointLoc further integrates a novel subset-based localization scheme, thus the influence of anomalous RSS signals is eliminated before making the final location decision. We have fully evaluated the performance of JointLoc in two RSS datasets collected in real environments. Compared with conventional algorithms and the latest ones, results show that JointLoc is robust against signal fluctuations, and achieves good localization accuracy.

Local and Remote Causes of the Equatorial Pacific Cold Sea Surface Temperature Bias in the Kiel Climate Model

Abstract We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP), and the southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12%, and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds causes the cold SST bias, while the surface heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region. The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP, and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.

Control and Management Approach of Photovoltaic-Battery-Ultra-Capacitor Energy System in Standalone and Grid-Connected Modes

This paper deals with energy management for the photovoltaic-battery-ultra-capacitor energy system for a standalone and grid-connected. The developed energy system can operate in an isolated or connected mode according to the required power. Indeed, the photovoltaic generator is coupled with the DC bus voltage via a DC–DC power converter, to provide the maximum power from the photovoltaic panels. An energy storage system presented by batteries and ultracapacitors is employed to regulate the DC bus voltage through both DC–DC bidirectional power converters. The DC bus is connected to the AC bus through a three-phase power inverter to control the operating modes and ensure the energy demands. In effect, an energy management algorithm is developed to control the energy flow in the system for a standalone and grid-connected. Hence, the output voltage DC bus is controlled by the energy storage system using a Lyapunov approach to ensure high performance under load and irradiance profile variations. In this context, simulations have been carried out to prove the effectiveness of the proposed photovoltaic system. The developed energy system has been tested in standalone and grid-connected modes with the following scenarios: PV generator supplies the load, PV generator power is limited due to less solar lighting, the electric grid supplies the high load power, the surplus energy of the PV generator is injected into the electric grid, and standalone mode operation.

Ride-hailing and transit accessibility considering the trade-off between time and money

Ride-hailing services can expand access to opportunities in urban areas, but out-of-pocket costs may limit its benefits for low-income individuals. This paper examines how ride-hailing shapes spatial and socioeconomic differences in access to opportunities while accounting for the trade-off between travel time and monetary costs. Using one year of aggregate Uber trip data for Rio de Janeiro in 2019 and a new multi-objective routing algorithm, we analyze the potential for ride-hailing services to improve employment accessibility when used as a standalone transportation mode and in conjunction with transit as a first-mile connection. We find that, compared to transit, standalone ride-hailing can significantly expand accessibility as a standalone mode for short trips, and as a first-mile feeder to transit in trips longer than 30 min. However, the accessibility benefits of ride-hailing accrue mostly to high-income groups due to affordability barriers. These findings suggest that policy efforts to integrate rideshare with transit are likely not going to benefit low-income communities without some form of subsidized fare discounts to alleviate affordability barriers. The paper also highlights how accounting for trade-offs between travel-time and monetary costs can importantly influence the results of transportation accessibility and equity studies.

Decentralized Control for Multiple Network-Forming Inverters in a Solar PV-BESS Based Standalone Microgrid System

—This research paper focuses on decentralized control of an AC microgrid in standalone mode. The microgrid includes three solar PV arrays accompanied by three battery energy storage systems (BESS), utilizing renewable distributed energy resources (DERs). The connection between each DER system and the microgrid is established through a network-forming three-phase voltage source inverter (VSI). The microgrid’s operation is regulated using modified droop control, incorporating virtual impedance and feed-forward derivative in primary control, along with a phase-locked loop (PLL) system for inverter synchronization. A master-slave control configuration is employed between the VSIs to mitigate voltage amplitude and frequency deviations induced by primary control. These enhancements ensure smooth power distribution and maintain voltage amplitude and frequency within specified limits across different operational scenarios. Control operations are performed in a stationary frame ( frame) using Clarke’s transformation, and PR controllers regulate VSI’s output voltage and current. The research paper shows the effectiveness of controlled microgrid operations using MATLAB/Simulink software in various conditions including intermittent PV system behavior, load dynamics, and DER connection/disconnection challenges. Simulation results show that the proposed approach enhances stability and performance of the microgrid system, especially in managing power fluctuations and maintaining a constant frequency response.

Histogram cube: towards lightweight interactive spatiotemporal aggregation of big earth observation data

ABSTRACTIn the era of Earth Observation (EO) big data, interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns. However, existing methods are inefficient and complex. Their interactive performance greatly depends on large-scale computing resources, especially data cube infrastructure. In this study, from a green computing perspective, we propose a lightweight data cube model based on the preaggregation concept, in which the frequency histogram of EO data is employed as a specific measure. The cube space was divided into lattice pyramids by the Google S2 grid system, and histogram statistics of the EO data were injected into in-memory cuboids. Therefore, exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes. We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation. The experiments showed that the proposed model is smaller, faster and consumes less energy than ArcGIS Pro and XCube, and facilitates green computing strategies involving a cube infrastructure. Due to the standalone mode, larger dataset will result in longer cube building time with indexing latency. The efficiency of the approach comes at the expense of accuracy, and the inherent uncertainties were examined in this paper.

Design of continuous-duty high-power converter for multi-purpose welding applications

One large-current high-power full-bridge DC-DC converter is often used to cater several multi-characteristic welding loads. The load could be a welding arc or a conductive molten flux. The contour of operating point (voltage-current) of each load type is different. The converter often operates under complex light-load conditions. The design of such high power continuous-duty converter needs to meet three objectives – desired control performance, high across-the-load efficiency, and superior thermal management. To address the thermal issues, this article proposes to use parallel-connected converter modules. It helps distribute the power loss over larger component base; increased surface area would assist the heat removal process. For high operating efficiency, this article proposes superior component engineering in each module. And, for high across the load efficiency, this article proposes to enable or disable an efficient converter module based on the current demand. It means each converter module is equipped to feed the connected load in stand-alone mode. For reduction of power loss, this article elaborates the role of each critical component. The distribution of heat loss would be optimally shared if the current sharing by converter modules is equal, but it needs to be compatible to the dynamic behavior of multi-input process loads. This research proposes and practically validates that single PWM-controller driven parallel-connected converter-modules are not only compatible to process dynamics, the idea would achieve proper current sharing among similar components.