Deep Reinforcement Learning Based Joint Cooperation Clustering and Downlink Power Control for Cell-Free Massive MIMO

In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power grids.To tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase.This unpredictability can lead to unsafe control measures.To mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL framework.This module ensures that the MARL agents carry out voltage control actions safely.The experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%V.out)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,respectively.Additionally,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems.

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

Robust Nonlinear Current Sensorless Control of the Boost Converter with Constant Power Load

The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. A PWM-based current-sensorless robust sliding mode controller is developed that requires only the measurement of the output voltage. An extended state observer is developed to estimate a lumped uncertainty signal that comprises the uncertain load power and the input voltage, the converter parasitics, the component uncertainties and the estimation of the derivative of the output voltage needed in the implementation of the controller. A linear sliding surface is used to derive the controller, which is simple in its design and yet exhibits excellent features in terms of robustness to external disturbances, parameter uncertainties, and parasitics despite the absence of the inductor’s current feedback. The robustness of the controller is validated by computer simulations.

A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems

In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

Distributed robust power control in two-tier vehicle networks under uncertain channel environments

This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks,where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications.However,considering complex channel fading and high-speed vehicle movement,the cer-tainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks.Rather than the perfect channel state information assumption,the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains.Moreover,interference management is a major challenge in reusing communications,especially in uncertain channel environments.Power control is an effective way to realize interference management,and optimal power allocation can ensure that interference of the user meets the communication requirements.In this study,the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives.Since both of these schemes are non-convex and intractable,the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones.Furthermore,a novel distributed robust power control al-gorithm was developed to determine the optimal solutions.The performance of the algorithm was evaluated through numerical simulations,and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.

An Adaptive Wireless Power Sharing Control for Multiterminal HVDC

Power sharing among multiterminal high voltage direct current terminals(MT-HVDC)is mainly developed based on a priority or sequential manners,which uses to prevent the problem of overloading due to a predefined controller coefficient.Furthermore,fixed power sharing control also suffers from an inability to identify power availability at a rectification station.There is a need for a controller that ensures an efficient power sharing among the MT-HVDC terminals,prevents the possibility of overloading,and utilizes the available power sharing.A new adaptive wireless control for active power sharing among multiterminal(MT-HVDC)systems,including power availability and power management policy,is proposed in this paper.The proposed control strategy solves these issues and,this proposed controller strategy is a generic method that can be applied for unlimited number of converter stations.The rational of this proposed controller is to increase the system reliability by avoiding the necessity of fast communication links.The test system in this paper consists of four converter stations based on three phase-two AC voltage levels.The proposed control strategy for a multiterminal HVDC system is conducted in the power systems computer aided design/electromagnetic transient design and control(PSCAD/EMTDC)simulation environment.The simulation results significantly show the flexibility and usefulness of the proposed power sharing control provided by the new adaptive wireless method.

Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller

There are issues with flexible DC transmission system such as a lack of control freedom over power flow.In order to tackle these issues,a DC power flow controller(DCPFC)is incorporated into a multi-terminal,flexible DC power grid.In recent years,a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability.This work proposes a model predictive control(MPC)strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance.Initially,the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed,and the relationship between each regulated variable and control variable is determined;The power flow controller is then discretized,and the cost function and weight factor are built with numerous control objectives.Sub module sorting method and nearest level approximation modulation regulate the power flow controller;Lastly,theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy.Finally,it is demonstrated that the control strategy possesses the benefits of robust dynamic performance,multiobjective control,and a simple structure.

基于Simulink的E-Power自卸车发动机控制方法

E-Power作为一种新兴混动技术,能够将发动机运行与车辆驱动解耦,实现能量的高效利用。文章介绍一种E-Power自卸车发动机控制方法,该方案充分考虑发电机的工作效率和电池充放电功率限值的条件,通过Simulink模型搭建发动机控制策略。实车CHTC-D循环试验结果表明,按照该控制方法进行发动机驱动,可以平稳控制发动机起停,起到节油效果。

PCTRAN Westinghouse AP1000 Power Control of Pressurized Water Reactor Using Simulink of MATLAB

This paper introduces the simulation, and controls using Simulink of MATLAB for PCTRAN (Personal Computer Transient Analysis) of the power control system (PWR) type pressurized water reactor of PWR WESTINGHOUSE AP1000. The power controller model produces mathematical model description in nonlinear relation form in Simulink of MATLAB which is an important and popular program used at most universities for education. The power controller is described by a block diagram in this paper and some details introduce to clearly understand the work function. The results of action control compared with the PCTRAN programme in modes of automatic and manual control.

Adaptive and distance-driven power control scheme in mobile ad hoc networks

In order to save the energy and reduce the latency of the end-to-end transmission in mobile ad hoc networks an adaptive and distance-driven power control ADPC scheme is proposed by means of distance research in random geometrics. Through mathematical proof the optimal number of relay nodes and the optimal location of each node for data transmission can be obtained when a distance is given.In the ADPC first the source node computes the optimal number and the sites of the relay nodes between the source and the destination nodes.Then it searches feasible relay nodes around the optimal virtual relay-sites and selects one link with the minimal total transmission energy consumption for data transmission.Simulation results show that the ADPC can reduce both the energy dissipation and the end-to-end latency of the transmission.

An Uplink Power Control Scheme in CDMA Mobile Communication System

A brief introduction of principles and algorithm realization of uplink power control in CDMA mobile communication system based on IS 95 are given, and then the blocking probability and Erlang capacity under the condition of perfect and imperfect uplink power control are presented and analyzed. Finally the uplink power control algorithms are simulated, and the optimum uplink power control algorithm that maximizes system Erlang capacity is acquired.

Distributed Power Control for Energy Conservation in Hybrid Cellular Network with Device-to-Device Communication

Device-to-Device(D2D) communication has been proposed as a promising implementation of green communication to benefit the existed cellular network.In order to limit cross-tier interference while explore the gain of short-range communication,we devise a series of distributed power control(DPC) schemes for energy conservation(EC)and enhancement of radio resource utilization in the hybrid system.Firstly,a constrained opportunistic power control model is built up to take advantage of the interference avoidance methodology in the presence of service requirement and power constraint.Then,biasing scheme and admission control are added to evade ineffective power consumption and maintain the feasibility of the system.Upon feasibility,a non-cooperative game is further formulated to exploit the profit in EC with minor influence on spectral efficiency(SE).The convergence of the DPC schemes is validated and their performance is confirmed via simulation results.

Mobile Jammer-Aided Secure UAV Communications via Trajectory Design and Power Control

To further promote the achievable average secrecy rate for UAV-ground communications, a UAV-aided mobile jamming strategy was proposed in this paper. Specifically, an additional cooperative UAV is employed as a mobile jammer to transmit the jamming signal to help keep the source UAV closer to the ground destination, thus establishing more favorable legitimate link and enhancing the secrecy performance. We aimed to maximize the achievable secrecy rate by jointly optimizing the trajectories and transmit power of both source UAV and jammer UAV. To solve the considered non-convex optimization problem, we presented a block coordinate descent based iterative algorithm to address a sequence of approximated convex problems for the optimized parameter block by block to find a local optimal solution. Numerical results verify that the proposed algorithm can achieve significant secrecy rate gain compared to all the benchmark schemes.

Review on grid-forming converter control methods in high-proportion renewable energy power systems

Grid-forming(GFM)converters can provide inertia support for power grids through control technology,stabilize voltage and frequency,and improve system stability,unlike traditional grid-following(GFL)converters.Therefore,in future“double high”power systems,research on the control technology of GFM converters will become an urgent demand.In this paper,we first introduce the basic principle of GFM control and then present five currently used control strategies for GFM converters:droop control,power synchronization control(PSC),virtual synchronous machine control(VSM),direct power control(DPC),and virtual oscillator control(VOC).These five strategies can independently establish voltage phasors to provide inertia to the system.Among these,droop control is the most widely used strategy.PSC and VSM are strategies that simulate the mechanical characteristics of synchronous generators;thus,they are more accurate than droop control.DPC regulates the active power and reactive power directly,with no inner current controller,and VOC is a novel method under study using an oscillator circuit to realize synchronization.Finally,we highlight key technologies and research directions to be addressed in the future.

A Combining Call Admission Control and Power Control Scheme for D2D Communications Underlaying Cellular Networks

As device-to-device(D2D) communications usually reuses the resource of cellular networks, call admission control(CAC) and power control are crucial problems. However in most power control schemes, total data rates or throughput are regarded as optimization criterion. In this paper, a combining call admission control(CAC) and power control scheme under guaranteeing QoS of every user equipment(UE) is proposed. First, a simple CAC scheme is introduced. Then based on the CAC scheme, a combining call admission control and power control scheme is proposed. Next, the performance of the proposed scheme is evaluated. Finally, maximum DUE pair number and average transmitting power is calculated. Simulation results show that D2 D communications with the proposed combining call admission control and power control scheme can effectively improve the maximum DUE pair number under the premise of meeting necessary QoS.

Distributed power control algorithm based on game theory for wireless sensor networks

Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless sensor networks is restrictive energy, this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime. The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.

Power Control for Fading Channels in Cognitive Radio Networks with Outage-Probability Minimization

Cognitive radio allows Secondary Users （SUs） to dynamically use the spectrum resource licensed to Prirmry Users （PUs）, and significantly improves the efficiency of spectrum utilization and is viewed as a promising technology. In cognitive radio networks, the problem of power control is an important issue. In this paper, we mainly focus on the problem of power control for fading channels in cognitive radio networks. The spectrum sharing underlay scenario is considered, where SUs are allowed to coexist with PUs on the condition that the outage probability of PUs is below the maximum outage probability threshold limitation due to the interference caused by SUs. Moreover, besides the outage probability threshold which is defined to protect the performance of PUs, we also consider the maximum transmit power constraints for each SU. With such a setup, we emphasize the problem of power control to minimize the outage probability of each SU in fading channels. Then, based on the statistical information of the fading channel, the closed expression for outage probability is given in fading channels. The Dual-Iteration Power Control （DIPC） algorithm is also proposed to minimize the outage probability based on Perron-Frobenius theory and gradient descent method under the constraint condition. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.

DS-CDMA system outer loop power control and improvement for multi-service

When a new user accesses the CDMA system, the load will change drastically, and therefore, the advanced outer loop power control （OLPC） technology has to be adopted to enrich the target signal interference ratio （Silt） and improve the system performance. The existing problems about DS-CDMA outer loop power control for multi-service are introduced and the power control theoretical model is analyzed. System simulation is adopted on how to obtain the theoretical performance and parameter optimization of the power control algorithm. The OLPC algorithm is improved and the performance comparisons between the old algorithm and the improved algorithm are given. The results show good performance of the improved OLPC algorithm and prove the validity of the improved method for multi-service.