Power Control Approach Research Articles

Symmetrical components‐based robust stabilizing control of a grid‐connected inverter under unbalanced voltage sag

AbstractThis study presents an approach for robust current and power control for a three‐phase grid‐connected inverter set up with an L‐filter that operates under an unbalanced AC grid‐voltage source. This control method consists of two paralleled current control for the positive and negative sequences to accomplish one of three selective control targets: a) supplying balanced three‐phase current; b) supplying non‐oscillated real power; and c) supplying non‐oscillated imaginary power to the AC grid for a given reference value utilizing the identical control gains. The real and imaginary power references, together with the measured grid voltages, are used in the computation of the reference current for each control target. In addition, the proposed dual‐current control system has a control law that comprises state feedback to offer stability and an integral effect to get rid of the offset error in the dq‐frame. To reduce settling time under uncertain system conditions, an optimization problem based on linear matrix inequalities is solved to determine a set of robust stabilizing gains. Simulation and experimental results using a TMS320F28377D digital signal processor validate the proposed control performance under three control targets.

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.

Novel resistance control scheme for mitigating current sharing mismatches in parallel dual active bridge converters for DC fast charging stations

Abstract Dual Active Bridge (DAB) converters have gained popularity in electric vehicle charging stations due to their high efficiency and electrical isolation. As the demand for high-powered devices and large-capacity energy storage systems grows, charging systems that integrate multiple interconnected DAB modules are emerging as a promising solution. However, prolonged operation of these modules at high power levels can cause parameter deviations from the initial DAB circuit, resulting in power variations between modules. To overcome parameter deviations, this study presents an enhanced power control approach based on output resistance adjustment, intending to achieve consistent output capacity for multiple DAB modules. In the proposed enhanced power control method, the output resistance of the DAB module is considered to be controllable, and the current-sharing mismatches among DAB modules are fed back to tune the converter output resistance for mitigating current mismatches between modules. Thanks to the proposed control method, each DAB module can operate autonomously and balance the charging current between modules. When one DAB module is suddenly cut out of the system, the other DAB modules still maintain their stability with fully guaranteed load capacity. To demonstrate the feasibility of the enhanced control approach, the small signal model of the DAB system with three modules is derived together with its frequency-amplitude diagram. Then, the effect of virtual resistance on current balancing is comprehensively tested, and the proper control signal with virtual resistance is added to the DAB voltage control loop. The simulation results have demonstrated the reliability of the proposed control method with the ability to balance the charging current between modules and stabilize the system when a single DAB module fails.

Control of DFIG-based wind power generation system under unbalanced grid voltage conditions

Doubly Fed Induction Generator (DFIG)-based wind turbines have become increasingly popular in recent years due to their capacity to operate at varying speeds. Weaknesses in the DFIG system can arise from issues with the power grid due to the stator's direct connection and the excitation converter's power rating limitation. Under situations of unbalanced grid voltage, this study aims to explore the efficacy of the Direct Power Control (DPC) approach in managing wind turbine systems based on DFIG. Throughout the experimental investigation, we evaluated the system in standard and unbalanced grid voltage settings. MATLAB/SIMULINK simulations implement DPC, specifically tailored for a 9 MW DFIG-based wind farm. The results of these simulations show that the changed control method effectively reduces torque oscillations by making it possible to create active and reactive power references for the rotor-side converter. This eliminates the requirement for sequence component excitation, which was previously necessary. Furthermore, the research highlights the intrinsic link between control techniques and grid circumstances, showing this connectivity's crucial role in improving wind energy systems' stability and operational efficiency based on DFIG.

Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board.

Smart Control Strategy for Adaptive Management of Islanded Hybrid Microgrids

This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative methods.

Integrating IoT in WBANs: An energy-efficient and QoS-aware approach for rapid model-driven transmission power control and link adaptation

Wireless Body Area Networks (WBAN) are integral to the application framework of the Internet of Things (IoT) domain, especially in applications demanding efficient connectivity and availability. Our study introduces a rapid, model-based, and sensor-centric approach in WBANs, significantly enhancing energy and time efficiency, crucial for IoT devices constrained by limited power supply and the need for extended operational duration. Our scheme maintains a transmit power threshold table, ensuring objective Quality of Service (QoS) while reducing power consumption in sensor nodes. We achieve this through a novel approach that contrasts the target bit error rate (BER) with the received instantaneous BER, refining path loss and channel models to accurately determine receiver sensitivity. The proposed scheme sets a in receiver sensitivity model, establishing the minimum transmit power needed for desired QoS. This is particularly relevant for IoT applications where maintaining consistent communication quality is paramount. Our performance evaluations show that this approach outperforms existing link adaptation and transmit power control methods in sensor overhead, time efficiency, and energy efficiency, marking a significant advancement in IoT connectivity solutions. By addressing the challenges of radio frequency signal vulnerability to body shadowing and path loss in dynamic conditions, our study not only enhances WBAN performance but also contributes to the stability and adaptability of IoT networks. This aligns with the evolving needs of the IoT domain, where efficient, reliable, and adaptive network solutions are increasingly in demand for a wide range of applications.

GNN-Based Meta-Learning Approach for Adaptive Power Control in Dynamic D2D Communications

GNN-Based Meta-Learning Approach for Adaptive Power Control in Dynamic D2D Communications

Splittable systems in biomedical applications.

Splittable systems have emerged as a powerful approach for the precise spatiotemporal control of biological processes. This concept relies on splitting a functional molecule into inactive fragments, which can be reassembled under specific conditions or stimuli to regain activity. Several binding pairs and orthogonal split fragments are introduced by fusing with other modalities to develop more complex and robust designs. One of the pillars of these splittable systems is modularity, which involves decoupling targeting, activation, and effector functions. Challenges, such as off-target effects and overactivation, can be addressed through precise control. This review provides an overview of the design principles, strategies, and applications of splittable systems across diverse fields including immunotherapy, gene editing, prodrug activation, biosensing, and synthetic biology.

Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

Review on the Electric Spring Connected to the Renewable Energy System to Minimize Harmonics and Voltage Profile Enhancement

The emerging smart grid technology known as Electric Spring (ES) has been employed previously to maintain voltage and power stability in renewable energy-based grids that are minimally regulated or self-contained. This study thoroughly investigated the foundational theories, modeling approaches, practical uses, and restrictions associated with Electric Springs by analyzing existing Scholarly works in this domain Electric Springs implement a direct power control approach. solution for the next-generation single-phase Systems, addressing the limitations of current ES control methods. This solution aims to improve power management in microgrids, where integrating renewable energy sources (RESs) poses challenges due to the unpredictable power output. The challenge involves managing two aspects: the variable Power consumed by ES and the power fed into the grid. The suggested straightforward and accurate signal manipulation method, namely, ANN-based Electric Spring (ES), is set to be deployed. This technique can adeptly manage these aspects in both stable conditions and during transitions in Renewable Energy Sources (RES).

Optimal D2D power for secure D2D communication with random eavesdropper in 5G‐IoT networks

SummaryIn the rapidly evolving landscape of fifth generation Internet of Things (5G‐IoT) networks, Device‐to‐Device (D2D) communication has emerged as a promising paradigm to enhance secrecy transmission rate (STR) and connectivity. However, the security of D2D communications in the presence of eavesdroppers remains a critical challenge. This article investigates the problem of optimizing D2D transmit power to achieve secure D2D communication while considering the presence of random eavesdroppers in 5G‐IoT networks. We propose a novel secrecy‐based power control approach (SRMWPCA) approach to model the random distribution of eavesdroppers in the network, taking into account their varying distances from D2D pairs and deliberately increasing interference at the eavesdropper's link. By leveraging tools from stochastic geometry, we derive an analytical expression for the secrecy transmission probability (STP), which quantifies the probability of eavesdroppers successfully decoding the D2D transmission. In this analysis, we have incorporated practical considerations such as channel fading, path loss, and interference from other devices. To enhance the security of D2D communication, we formulate an optimization problem to determine the optimal transmit power levels for D2D pairs, subject to constraints on the secrecy transmission probability and interference to the cellular network. We propose an efficient algorithm to find the power allocation that maximizes the secrecy outage performance while meeting these constraints. Simulation results demonstrate the effectiveness of the proposed approach in achieving secure D2D communication in 5G‐IoT networks with random eavesdroppers. The performance of the proposed SRMWPCA approach improved by 23.25% and 20.9% compared with standard approaches in terms of the secrecy rate and throughput of the users from malicious attacks.

Co-simulation-based optimal reactive power control in smart distribution network

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

PV Inverter-Based Fair Power Quality Control

Low voltage distribution networks incorporating solar photovoltaic (PV) panels experience overvoltage and voltage unbalance during periods of low load and high PV generation. Resolving overvoltage by active power curtailment (APC) is an effective and cost-efficient solution. However, current APC techniques result in excessive and unfair power curtailment for prosumers at the sensitive parts of the grid that might induce neutral current. In this work, an analytical approach for fair APC and Reactive Power Control is presented for voltage regulation along with neutral current compensation. The desired power quality is maintained by controlling each phase of the PV inverter independently. The proposed algorithm regulates the voltage at the point of common coupling (PCC) within grid limits, eliminates neutral current, and reduces the grid unbalance. Furthermore, the results demonstrate that reducing the neutral current reduces the voltage at the PCC and consequently decreases the power curtailment required for overvoltage regulation.

Point of Common Coupling Voltage Modulated Direct Power Control of Grid-Tied Photovoltaic Inverter for AC Microgrid Application

A direct power control (DPC) approach is proposed in this study for a grid-tied photovoltaic (PV) voltage source inverter (VSI) to regulate active and reactive power flow directly in between utility grid and microgrid (MG) by controlling point of common coupling (PCC) voltage. The proposed PCC voltage modulated (PVM) theory-based DPC method (PVMT-DPC) is composed of nonlinear PVM, nonlinear damping, conventional feedforward, and feedback PI controllers. For grid synchronization rather than employing phase-locked-loop (PLL) technology, in this study, direct power calculation of the PCC voltage and current is adopted. Subsequently, at PCC, the computed real and reactive powers are compared with reference powers in order to generate the VSI’s control signals using sinusoidal pulse width modulation (SPWM). Because of the absence of the PLL and DPC method adoption, the suggested controller has a faster convergence rate compared to traditional VSI’s power controllers. Additionally, it displays nearly zero steady-state power oscillations, which assure that MG’s power quality is improved significantly. To validate the proposed PVMT-DPC method’s performance, real-time simulations are conducted via real-time digital simulator (RTDS) for a variety of cases. The obtained results demonstrate that using the proposed PVMT-DPC approach, PV VSI can track the reference power within 0.055 s where the output power has low steady-state oscillations and output current has lower total harmonic distortion (THD) of 1.68%.

Exceedance control of the false discovery proportion via high precision inversion method of Berk-Jones statistics

Exceedance control of the false discovery proportion (FDP) can provide an interpretable method for addressing the variability in the false discovery proportion estimates. Exceedance control of FDP can be viewed as constructing a confidence interval for FDP and as such inverting a hypothesis test is a viable method for achieving exceedance control. A novel powerful approach for exceedance control is presented based on using a directional Berk-Jones goodness-of-fit statistic. The approach employs a high-precision implementation procedure to accurately compute confidence envelopes for FDP. The procedure is compared against other methods and generalized to include other goodness-of-fit statistics that follow an isotropy condition.

Real-Time Power Control of Doubly Fed Induction Generator Using Dspace Hardware

Numerous studies have been undertaken to evaluate wind energy systems’ active and reactive power control, the energy produced, and their its link to distribution networks. This research makes a novel contribution to the discipline in this setting. The novelty of this work aims to design a new wind emulator and design a power control approach for a doubly fed induction generator (DFIG)-based wind system. A description of the system was provided first. Secondly, the control strategy was described in detail. Then, it was applied to both converters (machine and grid sides). Three stages were used to evaluate the control solution: (1) a MATLAB/Simulink simulation to validate the reference’s persistence (for both real and step wind speeds) and the system’s robustness, (2) implementation in real-time on a dSPACE-DS1104 board linked to an experimental laboratory bench, and (3) overlapped comparison experimental and simulated data to conduct a thorough quantitative and qualitative analysis using the root-mean-square error measures. The simulation and experimental findings demonstrate that the suggested model is valid and presents an excellent correlation between experimental and simulated results regarding wind speed variation.

LQ Optimal Control for Power Tracking Operation of Wind Turbines

In this paper, an approach for active power control of individual wind turbines is presented. State-of-the-art controllers typically employ separate control loops for torque and pitch control. In contrast, we use a multivariable control approach. In detail, active power control is achieved by using reference trajectories for generator speed, generator torque, and pitch angle such that a desired power demand is met if weather conditions allow. Then, a linear quadratic (LQ) optimal controller is used for reference tracking. In an OpenFAST simulation environment, the controller is compared to a state-of-the-art approach. The simulations show a similar active power tracking performance, while the LQ optimal controller results in lower mechanical wear. Moreover, the presented approach exhibits good reference tracking and by improving the reference trajectory generation further performance increases can be expected.

Power Control Approach for PV Panel System Based on PSO and INC Optimization Algorithms

The main purpose of this paper is to obtain optimum power from a Photovoltaic (PV) panel and deliver it to a load system under standard irradiance and temperature weather conditions. Standard Boost DC-DC converters and bidirectional Buck-Boost DC-DC converters work as voltage controlling units for the power provided from the PV panel, which is used to charge the battery and supply suitable voltage signal to AC load. MPPT technique, based on two control algorithms, Particle swarm optimization (PSO) and Incremental Conductance (INC), is used to extract maximum power from the solar cells. Matlab/ Simulink environment is adopted to simulate the proposed PV power system. Simulation results are presented and analyzed based on transient and steady-state performance parameters. The performance results of the PV array system under the Standard Test Conditions (STC) showed that the INC-MPPT control algorithm can provide a