Proposed Power Control Research Articles

A novel power control scheme for distributed DFIG based on cooperation of hybrid energy storage system and grid-side converter

Due to the uncertainty of wind energy, the wind power is difficult to be dispatched and may cause the voltage fluctuations for distributed network. Therefore, a novel power control scheme is proposed to manage the output power of the distributed doubly-fed induction generator (DFIG) by cooperating the hybrid energy storage system (HESS). In the proposed scheme, the grid-side converter of DFIG is controlled to manage overall output power of the DFIG/HESS hybrid system in accordance to the power dispatching command. The HESS is utilized to deal with the power difference between DFIG generation power and the power dispatching command. The HESS is embedded in the DC-link bus of DFIG and is composed of superconducting magnetic energy storage and batteries. Additionally, in order to avoid HESS from overcharging and over-discharging, the pitch angle control and power dispatching command are adjusted by considering the state of charge (SOC) of HESS. Finally, the simulation model of distributed DFIG and HESS is built on the real-time digital simulation (RTDS) platform, and the simulation results validate the effectiveness and reliability of the proposed power control scheme.

Optimizing job shop scheduling with speed‐adjustable machines and peak power constraints: A mathematical model and heuristic solutions

AbstractThis paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power‐flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi‐objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small‐sized instances. We also proposed a multi‐objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade‐off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4–6% increase), while at the same time allowing for a small reduction in energy consumption.

Location-based Power Control Mechanism for D2D Communication Underlaying a Cellular System

The paper presents a location-based approach to controlling the power of device-to-device (D2D) underlay of a frequency reuse-1 cellular system. The system allows for direct communication to share uplink resources with cellular users. As a result, both D2D and cellular users are experiencing additional interferences in the system. By controlling the output power of the devices, these interferences can be mitigated and the performance of the network can be improved in terms of better spectral and energy efficiency. The proposed location-based target signal-to-interference ratio power control scheme for D2D communications utilizes information about users' locations to estimate the interference level experienced by the receiver of the direct link. Based on this estimation, an appropriate transmit power can be determined. The performance of the proposed power control solution is investigated via system level simulations.

Unsupervised Deep Learning for Power Control of Cell-Free Massive MIMO Systems

The problem of power control for the uplink and the downlink in cell-free massive multiple-input multiple-output (CF mMIMO) systems is considered in this paper. In order to achieve a balance between the conflicting requirements of good performance levels and of low computational complexity, we employ unsupervised learning based on deep learning (DL). The proposed power control strategies can work using as input the large scale fading coefficients only and are capable to obtain very satisfactory performance levels, as compared to conventional, highly complex, optimization methods and to heuristic methodologies. Moreover, they can be used also in a user centric system wherein each mobile station is served by a subset of the active access points.

Federated Learning-Based Cell-Free Massive MIMO System for Privacy-Preserving

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.

Interval type-2 fuzzy logic control for a space nuclear reactor core power system

The uncertain environment and delayed communications bring great challenges for the space nuclear reactor core power control. The imprecise knowledge about nonlinear and time varying parameters of the reactor make the problem further difficult. In this work, an anti-interference method is proposed for the core power control of space reactors. A new core power controller is designed to compensate the power deviation caused by model uncertainties and external disturbances. To further approximate nonlinear mapping functions between the control drum angle position and the deviation of the reactor core power, an interval type-2 fuzzy logic system is employed to deduce the correction of control drum angle position for its general approximation ability. Moreover, in order to solve control difficulties caused by physical constraints of the control drum and high stability requirements of the core power control system, an optimized iteration algorithm is proposed to ensure convergence to the optimal solution. A 1MWe space nuclear reactor is used as a reference object to carry out the research and verification of the newly proposed power control scheme. The numerical results verify the feasibility of the novel controller and show that load tracking performance of the system can be realized effectively.

An MPC based active and reactive power coordinated control strategy of PMSG wind turbines to enhance the support capability

As wind turbines are constantly replacing traditional units, it is becoming a consensus that wind turbines should participate in the grid support that was only responsible by traditional units in the past. In order to enhance the grid support capabilities (including active power support and reactive power support) of permanent magnet synchronous generator (PMSG) based wind turbines, this paper constructs an active and reactive power coordinated control strategy. Compared with the current active and reactive power coordinated control strategy of PMSG wind turbines, the method of the proposed one innovatively considers the climbing coordinated restriction between active and reactive power, flexible prioritization arrangement between active and reactive power, the accurate amplitude and climbing constraints of grid-side converters’ output voltage, and the model predictive control (MPC) technique. The simulation results verify that the proposed power control strategy can make PMSG wind turbines achieve excellent power output performance and thus better meet the requirements of power grid support.

Development and Validation of Control Algorithm for Variable Speed Fixed Pitch Small Wind Turbine

In this study, a power control algorithm of a variable-speed fixed-pitch horizontal-axis lift-type 20 kW small wind turbine (SWT) was proposed and verified through dynamic simulations. The power control algorithm proposed in this study consists of algorithms for Region II to track the maximum power coefficient, for Region II-1/2 to maintain the rated rotor speed, and for Region III to maintain the rated power. To verify the proposed power control algorithm, simulations were performed at the rated wind speed and above the rated wind speed, to which turbulence intensity based on the IEC regulation’s normal turbulence model was applied. As a result, it was confirmed that the proposed controller operates properly in the whole three regions including Regions II, II-1/2, and III. The controller performance was then compared with the variable-speed variable-pitch power controller. Although the performance of the proposed controller was considered good for the target VSVP wind turbine, it was lower than that of the conventional controller applied to the same wind turbine. Compared to the VSVP wind turbine, the VSFP wind turbine with the proposed controller was found to have higher mean loads on the blade and the tower but the fatigue loads in terms of Damage Equivalent Load (DEL) were found to be reduced.

Optimized Power Control for Over-the-Air Federated Averaging With Data Privacy Guarantee

This paper investigates the privacy-preserving power control problem for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over-the-air federated edge learning</i> (Air-FEEL), which features “one-shot” aggregation and significantly reduced communication latency. In Air-FEEL, the inevitable random perturbation encountered in the aggregation process due to the corruption by channel fading and noise poses a fundamental trade-off between the privacy and accuracy, as larger perturbation may harm the accuracy but benefit the privacy, and vice versa. Therefore, power control in Air-FEEL, as the main approach to regulate the said random perturbation, needs to be judiciously designed for balancing the accuracy-privacy trade-off. This, however, is a largely uncharted area. To bridge the research gap, we first analyze the convergence behavior (in terms of the optimality gap) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differential privacy</i> (DP) performance of Air-FEEL with respect to the power control policy at different iterations. Then, to achieve the maximized training accuracy under given DP guarantee requirement, we minimize the optimality gap by jointly optimizing the power control at edge devices and the denoising factors at the edge server, subject to a set of power constraints at individual edge devices and the DP constraint. Experimental results validate that the proposed power control policy for Air-FEEL achieves a better privacy-accuracy trade-off compared with the benchmarks.

IOT-based power control systems framework for healthcare applications

Battery-powered EGC systems are improved with different IoT mechanisms in particular for the treatment of cardiovascular problems. To extend the battery capacity of IoT devices in the ECG monitoring system, an energy-saving technique was adopted using the VLSI design have been given in this study. The proposed power control computer automatically switches between two modes of transmission, i.e., greater style of power and reduced power, style, according to the battery intensity. A light approach stressing slope augmentation with a real-time segmentation algorithm was proposed for QRS complex assessment of pulse rate, guaranteeing optimum energy consumption during low battery capacity because on-node localized computation was a source of energy. Proper power transmission mode was chosen to efficiently achieve the objectives of the Wireless Body Area Network (WBAN) device, gathering sufficient ECG data from a specific patient and will increase in the charging level of the entire system with pulse rate consistency. The MIT-BIH arrhythmias dataset was used to assess the sensitivity. The design was suited for real-time ECG surveillance systems because it has been developed in a Spartan6 FPGA with a maximum frequency of 269 MHz and energy consumption of 0.7 MW.

Ambient Backscatter-Based Power Control Strategies for Spectrum Sharing Networks

Prior works on power control strategy need to estimate all users’ channel state information (CSI), which consumes both spectrum and time. Two efficient and low-complexity power control strategies, namely quality of experience (QoE) based power control scheme and distance based power control strategy, are proposed for an ambient backscatter based spectrum-sharing network. In the proposed power control schemes, there is no need to estimate all users’ CSI. In the QoE-based power control strategy, only the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> primary users with the highest priority feedback their CSI. We introduce an attenuation factor to reduce the transmit power to satisfy the interference threshold on primary users. Then, considering the short transmission distance of secondary backscatter communication and randomly distributed primary users, we propose the distance-based power control scheme, in which only the users within the backscatter distance need to feedback their CSI. Closed-form expressions for the outage probabilities of primary and secondary systems are derived over the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> channels under the proposed two power control strategies. The system energy efficiency are also analyzed. Simulation results are conducted to verify the analytical results. Finally, outage performance and energy efficiency of the proposed schemes are compared with that of the existing nearest- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> users based power control strategy. It is shown that the proposed power control strategies have lower outage probabilities and higher energy efficiency.

Mean Field Game-Theoretic Framework for Distributed Power Control in Hybrid NOMA

The steady expansion of the number of wireless devices and the ubiquity of the networks give rise to various interesting challenges for the future sixth generation (6G) of wireless communication systems. Particularly, the operators have to handle massive connectivity among Machine Type Devices (MTDs) and increasing demand for eMMB through limited spectrum resources. Non-Orthogonal Multiple Access (NOMA) has been spotlighted as an emerging technology to meet the above-mentioned challenges. In this paper, we consider a densely deployed network in which users are divided into NOMA coalitions. Firstly, we model the power allocation problem as a differential game. Then, we extend the formulated game using a Mean Field Game (MFG) theoretic framework by considering the effect of the collective behavior of devices. Furthermore, we derive a distributed power control algorithm that enables the users to appropriately regulate their transmit power according to brief information received from the BS. Indeed, the analysis of the proposed approach is governed by the two fundamental Hamilton- Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Numerical results are presented to analyze the equilibrium behaviors of the proposed power control algorithm and to demonstrate the effectiveness of the formulated MFG compared to existing works in the literature.

Downlink Achievable Rate of D2D Underlaid Cell-Free Massive MIMO Systems With Low-Resolution DACs

Device-to-device (D2D) communication coexisted with a cell-free massive multiple-input multiple-output (MIMO) system will be one of the pieces of the B5G/6G jigsaw puzzle. In this article, a D2D underlaid cell-free massive MIMO system with low-resolution digital-to-analog converters is studied, where multiantenna D2D user equipments (DUEs) communicate pairwise with each other in the same time-frequency resources as cell-free with equipments (CFUEs). Assuming imperfect channel state information is available at access points (APs), the downlink closed-form achievable rates for both CFUEs and multiantenna DUEs are derived, which enable us to give insights into the impacts of the key design parameters (e.g., the AP number, the DUE density, and its antenna number) on achievable rate. After that, a power control scheme is developed to maximize the average sum rate of CFUEs and DUEs, and we show that the proposed power control problem can be effectively solved by using the path-following algorithm. Finally, numerical simulations are provided to highlight the system performance and verify the effectiveness of the proposed algorithm.

An improved virtual synchronous generator power control strategy considering time-varying characteristics of SOC

Virtual synchronous generator (VSG) is proposed to solve the lack of inertia of the power system. However, as the core of the VSG providing inertia, the dynamic characteristics of energy storages tend to be ignored in the VSG. In this paper, the dynamic characteristics of energy storages are first considered into the VSG control. Based on this concept, an improved VSG power control strategy considering time-varying characteristics of state of charge (SOC) is proposed in this paper. Firstly, the dynamic characteristics are analyzed in terms of external and output characteristics. Then, Radial Basis Function (RBF) neural network is used to learn data features and handle nonlinear relationships between port voltage and output power of energy storages. The dynamic response of the VSG is improved since the dynamic indexes of the power are optimized during transients. Finally, simulation and experimental results validate the efficacy of the proposed power control strategy.Index Terms—Energy storage, VSG, SOC, RBF neural network, Power control.

A Holistic Power Management Strategy of Microgrids Based on Model Predictive Control and Particle Swarm Optimization

Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy.

Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach

This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).

Security performance analysis for cell-free massive multiple-input multiple-output system with multi-antenna access points deployment in presence of active eavesdropping

This article investigates the influence of multiple-antenna deployment at access points on physical layer security under cell-free massive multiple-input multiple-output systems. To embody the concept of network multiple-input multiple-output, cell-free massive multiple-input multiple-output employs a great deal of geographically distributed access points, which jointly serve multiple users in the same frequency-time resource. Assuming a malicious active eavesdropper having knowledge of instantaneous channel state information, we derive the asymptotic closed-form expression of the achievable secrecy rate, which is used to evaluate network security performance. A distributed power allocation scheme is developed based on channel fading degrees of different users. All of access points distribute a larger proportion of local transmission power to the users with better channel conditions. We verified that the security performance of the multi-antenna access points outperforms that of the single one. Compared to equal power allocation, the proposed power control algorithm can further boost the network security performance.

Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

Decoupled fractional-order harmonic filter for power quality enhancement of grid-connected DG units

The global expansion of power electronics interfaced distributed generator (DG) units has led to the incrementation of nonlinear loads in the distribution systems, which may impose negative effects on the power quality. To maintain the desired power quality while actively compensating the harmonics in the presence of frequency deviations, this work proposes a decoupled closed-loop fractional-order current controller that incorporates the harmonic alleviation with the DG power generation function. Accordingly, an optimal fractional-order proportional–integral–derivative (FOPID) is designed to control the DG unit fundamental current, while compensating the local nonlinear load harmonic current (LHC) effects. A noteworthy contribution of this paper is that the optimal control scheme delivers the desired performance without the need for LHC detection and effectively reduces the operational cost. Another contribution is the implementation of an optimal FOPI-based control scheme in the outer power control loop to extract the fundamental current reference with no phase-locked loop (PLL) components. This offers comprehensive solutions with several improvements, such as eliminating the power tracking steady-state error under fundamental current tracking errors and preventing harmonic disturbances from being imposed on the harmonic control by the power control part. The feasibility and effectiveness of the proposed power control scheme are validated in comparison with the conventional PI approach.

An Electrolytic Capacitor-Less PV Micro-Inverter Based on CLL Resonant Conversion With a Power Control Scheme Using Resonant Circuit Voltage Control Loops

A power control scheme with maximum power point tracking based on solely voltage feedback control loops is proposed in this paper for a dc/ac isolated high frequency PV micro-inverter. The presented power circuit topology consists of an integrated continuous conduction mode (CCM) boost converter with an asymmetrical pulse-witch modulation (APWM) controlled CLL step-up resonant converter and a half-bridge grid-side inverter. The presented topology is capable of achieving CCM input current and a wide range of zero voltage switching (ZVS) operation for the front-end stage. The proposed power control scheme, as well as the developed maximum power point tracking (MPPT) control technique utilize the resonant circuit’s resonant capacitor voltage and the APWM input voltage of the resonant circuit only to achieve MPPT and to control the active power of the overall system. By doing so, a large electrolytic input filtering capacitor is not needed. This allows small size film capacitors to be used and improves the system lifetime expectancy. The theoretical analysis and the detailed descriptions of the proposed power control scheme for the presented PV micro-inverter are provided. Simulation and hardware results on a 220 W with 120 Vac, 60 Hz output hardware prototype are presented to demonstrate the performance of the proposed PV inverter system.