Power Control In Networks Research Articles

Review on Power Control in Multi-hop Networks

This paper reviews power control algorithms through interference management in a variety of network types, such as Device-to-Device (D2D) Networks, Unmanned Aerial Vehicles (UAVs), healthcare systems, and Low Power Wide Area Networks (LPWAN). The growing requirement for effective and consistent wireless communications advanced approaches to mitigate interference, ensuring optimal power usage and network performance. We explore the exceptional interference challenges and requirements of each network type. As wireless networks progress, managing interference becomes essential to maximize power usage and maintain network performance. This paper systematically reviews the latest advancements and methodologies that adjust power levels to mitigate interference. Additionally, we analyze these techniques and discuss their impact on spectral efficiency, supported by recent case studies. Our findings aim to guide researchers and practitioners in developing more effective power control and interference management solutions for next-generation wireless networks.

Power-Domain Interference Graph Estimation for Multi-hop BLE Networks

Traditional wisdom for network management allocates network resources separately for the measurement and communication tasks. Heavy measurement tasks may compete limited resources with communication tasks and significantly degrade overall network performance. It is therefore challenging for the interference graph, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, our core insight is to use power as a new dimension for interference graph estimation (IGE) such that IGE can be done simultaneously with the communication tasks using the same frequency-time resources. We propose to marry power-domain IGE with concurrent flooding to achieve simultaneous measurement and communication in BLE networks, where the power linearity prerequisite for power-domain IGE holds naturally true in concurrent flooding. With extensive experiments, we conclude the necessary conditions for the power linearity to hold and analyze several non-linearity issues of power related to hardware imperfections. We design and implement network protocols and power control algorithms for IGE in multi-hop BLE networks and conduct experiments to show that the marriage is mutually beneficial for both IGE and concurrent flooding. Furthermore, we demonstrate the potential of IGE in improving channel map convergence and convergecast in BLE networks.

Sustaining Situational Assessment in Electrical Network Power Control.

The aim of this study was to examine contributions to sustained situational assessment over an extended period in the context of electricity transmission control. The electricity industry is engaged in a period of unprecedented change in the transition to renewable sources of energy. Changes in the nature and function of electricity transmission risks a reduction in situational assessment as network controllers place increased reliance on advanced technology to identify and diagnose changes in the system state. Transmission network controllers from three organisations completed an assessment of their situational assessment on two occasions, one year apart. Multiple regression revealed a statistically significant model in which the variance in Year 2 was predicted by a combination of performance in Year 1, the recency of formal training, and the extent to which controllers perceived their job as exciting. No relationship was evident for years of experience as a network controller. The results suggest that a combination of recent formal training and perceptions of job excitement may have implications in maintaining the capacity for situational assessment over an extended period in the context of electricity network control. The outcomes of the present study suggest that changes in situational assessment can be monitored and that strategies, including formal training and job design, may sustain situational assessment over an extended period in advanced technology settings.

Graph Neural Network for Distributed Beamforming and Power Control in Massive URLLC Networks

Graph Neural Network for Distributed Beamforming and Power Control in Massive URLLC Networks

Distributed energy storage participates in reactive power optimization strategy research of new distribution system

Abstract We studied the reactive power control strategy of distributed energy storage in distribution systems, improved reactive power support capacity, and enhanced system reliability and new energy carrying capacity. Firstly, the principles and methods of reactive power optimization in distribution networks are studied. Then, the principles and mechanisms of distributed energy storage participating in reactive power control in distribution networks are studied. Finally, the genetic algorithm optimization method for reactive power optimization in distribution networks is studied, including parameter setting, target selection, and genetic strategy improvement, providing support for fine control of reactive power and voltage in distribution systems and efficient operation. Through simulation analysis of the IEEE33 node system, it is shown that distributed energy storage can improve the reactive voltage level of the distribution system and promote the consumption of new energy.

A new model of electrical cyber–physical systems considering stochastic communication link failures

This article establishes a model for electrical cyber–physical systems (ECPS), integrating the power grid, power information network and control network. The power grid is modelled based on DC power flow. The power information network enables the exchange of information between the power grid and control network. Stochastic communication link failure is a common problem in real control networks. To address this issue, this article models the control network as a Bernoulli network. The incorporation of an asynchronous gossip algorithm with price penalty factors enables the model to analyse multi-target combined economic emission dispatch problems (CEED) involving line losses in an imperfect control network. The effectiveness and validity of the model are illustrated through simulations.

ESG peer effects and corporate financial distress: An executive social network perspective

AbstractBased on the data of listed firms in China from 2009 to 2020, this study investigates whether environmental, society and governance (ESG) peer effects reduce the risk of corporate financial distress from an executive social network perspective. Using two‐stage least squares method, our empirical results suggest that the ESG peer effects exist in executive social networks, and the ESG peer effects can alleviate corporate financial distress. ESG subcategory analysis shows that the governance peer effect has the most obvious alleviating effect on financial distress. The negative impact of ESG peer effects on corporate financial distress is stronger when firms have high network power, network cohesion and network control in executive social networks. Our conclusions still hold after a series of robustness tests. Our research expands the literature on peer effects from the perspective of social relations, and sheds additional light on the critical role of ESG peer effects in financial risk management.

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

A DNN-orchestrated cognitive radio-based power control scheme for D2D communication

Device-to-device (D2D) communication demonstrates its efficacy across diverse domains, including military, surveillance, public safety, disaster management, content sharing, and local advertising, through its provision of proximity-based connections. Moreover, it elevates data rates, spectrum efficiency, and network throughput with efficient resource allocation while simultaneously reducing latency. However, it encounters challenges related to unregulated high-power consumption, leading to interference in D2D communication. Hence, in this paper, we proposed a deep neural network (DNN) and cognitive radio-driven efficient power control scheme for D2D communication. The proposed scheme operates in two distinct phases: the cognitive radio and the DNN-based phase. In this context, cognitive radio aids in selecting secondary users or cellular mobile equipment (CUE) through energy detection spectrum sensing. Subsequently, the DNN model is employed to optimize the transmission power of CUE in D2D communication. The effectiveness of the proposed scheme is assessed through various metrics, including loss, optimizers, learning rates, and sum rates.

Cyber-Resilient Converter Control System for Doubly Fed Induction Generator-Based Wind Turbine Generators

As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional–integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section.

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

The power flow analysis for electric power network: A tutorial article

The power system network is complex. This makes it more challenging to obtain valuable information because of its hundreds of buses and transmission lines. In addition, solving the steady-state equations of the power system network requires substantial mathematical computations. The power flow analysis is a study of the electrical power system. Engineers and utility companies use it for the design, control, planning, and future improvement of the electrical power network. Power flow analysis calculates the voltage magnitude, phase angles, active and reactive power flows, losses of the power system network under various loads, and generation conditions. This paper is a tutorial article aimed to present the power flow analysis techniques of Gauss-Seidel, Newton-Raphson, and Fast-Decoupled approaches. The Power World Simulator is used to demonstrate the software implementation of the approaches. The results show that Newton Raphson and Fast Decoupled have a faster convergence. The Newton Raphson method is the popular technique used for the analysis. An illustrative example for an IEEE 3-bus system on each technique is also presented in the article.

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

Efficient parallel scheduling with power control and successive interference cancellation in wireless sensor networks

In wireless sensor networks (WSNs), spectrum efficiency and energy efficiency are two challenging issues to be addressed due to the scarcity of spectrum and its limited energy. In this paper, we study efficient parallel scheduling with power control and successive interference cancellation (SIC) aimed at maximizing spectrum and energy efficiency, and formulate the efficiency maximization scheduling (EMS) problem as a mixed integer nonlinear programming (MINIP) problem. Given the high complexity caused by mixed variables, we first analyze the critical variables affecting efficiency to transform the MINIP problem into a combinatorial linear programming (LP) problem, which is further decomposed into two linear minimization problems. Then, a recursive relationship between the optimization variables is explored, through which we propose a two-stage scheme that solves the minimization problems with lower complexity. Finally, distributed implementation of the scheduling scheme is provided to iteratively explore the maximum feasible concurrent links in the networks. Extensive results demonstrate the superior performance of our proposed scheme in improving spectrum and energy efficiency. Specifically, our scheme achieves 12.16% to 66.15% higher spectrum efficiency and 18.84% to 88.20% higher energy efficiency than the existing schemes.

Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks With Time-Delayed and Corrupted Measurements

Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks With Time-Delayed and Corrupted Measurements

A Survey of Combined Heat and Power-Based Unit Commitment Problem: Optimization Algorithms, Case Studies, Challenges, and Future Directions

Combined generation units of heat and power, known as CHP units, are one of the most prominent applications of distributed generations in modern power systems. This concept refers to the simultaneous operation of two or more forms of energy from a simple primary source. Due to the numerous environmental, economic, and technical advantages, the use of this technology in modern power systems is highly emphasized. As a result, various issues of interest in the control, operation, and planning of power networks have experienced significant changes and faced important challenges. In this way, the unit commitment problem (UCP) as one of the fundamental studies in the operation of integrated power, and heat systems have experienced some major conceptual and methodological changes. This work, as a complementary review, details the CHP-based UCP (CHPbUCP) in terms of objective functions, constraints, simulation tools, and applied hardwares. Furthermore, some useful data on case studies are provided for researchers and operators. Finally, the work addresses some challenges and opens new perspectives for future research.

A game-theoretical paradigm for collaborative and distributed power control in wireless networks

A game-theoretical paradigm for collaborative and distributed power control in wireless networks

Learning Decentralized Power Control in Cell-Free Massive MIMO Networks

This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.

Studi Kasus Simulasi Konfigurasi Jaringan Scada Antara Gedung A dan Gedung B Menggunakan Aplikasi Cisco Packet Tracer

The purpose of this article aims to analyze technological developments in the industrial sector. SCADA requires a communication protocol. To increase efficiency in the industrial world, the SCADA system has been widely used in various industrial fields, such as manufacturing, power generation, oil and gas, telecommunications and transportation. Control over the arrangement of an electric power distribution network can be effective and efficient and will be equipped with a SCADA system. Control of the electric power network based on the SCADA system is really necessary considering that the growth of electric power is very high and adds to the increasing complexity of the electric power network system. The SCADA system ((Supervisory Control And Data Acquisition) itself is a system created for data retrieval, storage, analysis and also for controlling a plant/system that has been used remotely.

A Static State Estimation Scheme in Microgrid Utilizing μPMU Measurements

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP.