Power Control Algorithm Research Articles

Connection Quality Prediction and Nonlinear Control of Power Grid Communication Wireless Network

Learn about effective and uncontrolled gambling on wireless networks. Firstly, according to the control purpose, considering the reliability requirement of microgrid for network communication, the characteristics of wireless sensor network communication quality of service in microgrid, the influence of wireless connection reliability, and the mathematical standard of wireless communication are determined. Then, according to the estimation of the occurrence time of wireless communication link reliability, the reliability of wireless sensor network communication link in a smart grid is improved based on a fuzzy nonlinear control algorithm. Finally, the security and reliability of the control system described in this paper are measured by simulation. The experimental results show that the reliability of the wireless communication estimation time estimation algorithm and the power control algorithm based on fuzzy control can estimate the reliability, shortsightedness, and accuracy of good connections in real time. By adjusting the output power of the node, the connection quality and reliability can be controlled stably.

A Novel DC-Power Control Method for Cascaded Converter under DC Voltage-Imbalance Condition

This paper proposes an interphase power control algorithm (IPCA) and submodule power control algorithm (SPCA) based on state transition PWM (ST-PWM) for cascaded H-bridge inverter with unbalanced DC sources. In this method, the correction value helps control the interphase power distribution and submodule power distribution by directly modifying the duty cycle of each switching state. In order to achieve linear modulation, an in-depth analysis of ST-PWM is conducted. Combining ST-PWM and IPCA, balanced AC currents and accurate power control on DC side can be achieved with an unbalanced DC source. The proposed strategies are simulated using MATLAB/Simulink and tested on a five-level laboratory prototype. Simulation and experimental results demonstrate the advantage and effectiveness of the proposed algorithm.

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System.

A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

Effective capacity optimization for UDN

For the ultra-dense distributed network (UDN), the intensive deployment leads to the collision problem of the unlicensed spectrum access due to the random contention access mechanism of the unlicensed spectrum, which is difficult to improve the user quality of service (QoS). Here, the wireless access process of the licensed assisted access (LAA) network is modelled as a four-state semi-Markov model. Then, the effective capacity (EC) of the UDN based on the random access is derived, and the network capacity limit under the QoS requirements in the unlicensed spectrum is given. Based on the obtained EC, two power control algorithms are proposed to maximize the EC and effective energy efficiency (EEE) respectively under the user QoS requirements. Finally, numerical results verify the accuracy of the proposed theory, and the capacity-delay domain of the UDN is given. The simulation results show that the proposed algorithms improve the EC and the EEE compared with existing classical schemes.

The Single-Phase Voltage and Power Control Algorithm of a 4-Leg Type CVCF Inverter for an Off-Grid Micro-Grid System

In general, severe load imbalances in small AC micro-grid systems can degrade their operational performance and their maintenance. This is because the unbalanced load in the micro-grid affects the energy flow and the voltage regulation functions of each phase. In order to solve the voltage imbalance problem, several algorithms for the 3-phase 4-leg CVCF inverter have been proposed, but the control algorithms are not enough to operate the 4-leg CVCF inverter in a stable manner. Therefore, this paper proposes a single-phase voltage and power control algorithm for the 3-phase 4-leg CVCF inverter based on a dq control in order to improve the voltage imbalance problem caused by a severely unbalanced load, where the single phase voltage control algorithm is composed of an αβ-dq and a dq-αβ transformer, a voltage and a current controller, and an off-set controller and a PWM, and the single-phase power control algorithm is also composed of an αβ-dq and a dq-αβ transformer, an active/reactive power and a current controller, and an off-set controller and a PWM. Additionally, this paper performs modeling of the single-phase voltage and the power controller for a 4-leg CVCF inverter using the Matlab/Simulink S/W. From the simulation results, it is confirmed that the transient stability of the proposed single voltage control algorithm can be improved compared to the conventional control algorithm, and voltage control can also be maintained in a stable manner under extremely unbalanced conditions. Further, it is confirmed that 3-phase currents of the proposed single-phase power control algorithm are controlled in a stable manner under extremely unbalanced conditions.

Half sine shock on random control method for multi-axial vibration testing

Control method for the multi-axial half sine shock on random vibration test is studied in this article. From the Fourier spectrum analysis, it is found that the power of the half sine shock is mainly concentrated within a specific frequency band, which is only related to the sine frequency of the half sine shock. Compared with the broadband random signal, the frequency band occupies a small range. Therefore, a mixed signal separation method is proposed for the shock on random vibration control test. Firstly, the Fourier spectrum is divided into two parts, and then the Empirical Wavelet Transform (EWT) is used to extract the shock component from the mixed signal. Afterward, the random component is obtained by subtracting the shock component from the mixed signal. According to the deviations between each component and the specified references, the amplitudes of the shock component and the Power Spectral Densities (PSDs) of the random component are controlled by the amplitude correction algorithm and the matrix power control algorithm, respectively. An experiment with tri-axial shaker is implemented and the results show that the proposed method is feasible for the multi-axial half sine shock on broadband random vibration control test.

Weighted Sum Rate Maximization for Two-Way Wireless Powered Interference Channels

Interference is a detrimental factor for information decoding, but it can be a helpful resource for energy harvesting in a wireless powered network. Considering a two-way wireless powered interference channel, in this study, we present a simultaneous wireless information and power transfer (SWIPT)-then-wireless information transfer (WIT) protocol. In this protocol, transmitters perform SWIPT, and receivers receive both information and energy simultaneously in the forward link (FL). Then, the receivers perform WIT using the harvested energy in the backward link (BL). To maximize the weighted sum rate of the FL and BL, we jointly optimize the transmit power and energy harvesting (EH) ratio according to time switching and power splitting policies. To solve this non-convex optimization problem, we propose an iterative power control and energy harvesting algorithm based on dual methods that reduce computational complexity. The results show that the proposed algorithm finds the optimal transmit power and EH ratio suitably and thus maximizes the weighted sum rate. Consequently, the proposed SWIPT-then-WIT protocol expands the achievable rate region significantly compared with conventional schemes that do not adaptively control the transmit power and EH ratio.

Maximum Power Control Algorithm for Power Take-Off System Based on Hydraulic System for Floating Wave Energy Converters

In this study, a hydraulic system generator power converter was modeled to verify the performance of a hydraulic-based power take-off (PTO) system. Moreover, the characteristics and output performance of the PTO system were analyzed with various load control algorithms applied for maximum power control. The simulation performance was verified through a comparison with actual sea test results. Unlike previous studies on hydraulic-based PTO system control for input power performance, the performance of a hydraulic-based PTO system was analyzed through electrical load control in this study. The electrical load control was analyzed by applying a speed control algorithm based on the perturb and observe algorithm and an optimal torque control algorithm. A load control algorithm suitable for maximum power control of the PTO system was proposed by analyzing the characteristics and power generation performance of the system according to the control variables of each algorithm. The proposed optimal torque control algorithm proved to be suitable for maximum power control of the considered PTO system.

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 certainty 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 algorithm 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.

Optimization and Power Management of Solar PV-based Integrated Energy System for Distributed Green Hydrogen Production

Photovoltaic-based integrated energy systems act as a possible modern technological solution for clean and affordable green hydrogen production. However, research attempts are required from the scientific community to develop power management, control, optimization algorithms, and new techniques for these integrated energy systems effective and economical operation. The integrated energy system considered in this work consists of a solar photovoltaic, battery, grid and proton exchange membrane (PEM) electrolyzer. PEM electrolyzer with a power rating of 100 kW is modelled as a controlled current sink to interfere with the DC bus directly. This work proposes a power management and control algorithm for the photovoltaic-based integrated energy system considering different parameters like availability of solar photovoltaic power, DC link voltage, battery state of charge (SOC), tariff and availability of grid power. Effective power-sharing among the different power sources increases system reliability and stability. Optimization is performed for optimal sizing and costing to achieve economical operation such that these photovoltaic-based integrated energy systems become affordable and are encouraged for wide use. The photovoltaic system operates at the maximum power point through a neural network-based control strategy; in addition to this, stacking in neural network topologies is also discussed for improving system accuracy in tracking the reference voltage for MPPT operation. The DC link voltage is controlled by interfacing the battery with the DC link via a bidirectional buck-boost power converter. The contribution of the work is to highlight the role of renewable energy sources for continuous green hydrogen production. Green hydrogen will be a critical driving force for the future transportation system, fuel cell-based power generation, and industries utilizing hydrogen in direct or indirect forms-that will help transition towards a carbon-free society. Optimization and time-domain simulation results indicated the economic and technical feasibility of the proposed photovoltaic-based integrated energy system for green hydrogen production with the best optimal configuration producing hydrogen at the cost of $ 4.806/kg and total net present cost of $749904.

Coevolutionary Framework-Based Constrained Multi-Objective Optimization for Optimal Carbon-Energy Combined Flow of a Power Grid

A new constrained multi-objective optimization coevolutionary algorithm (CCMO) based on the NSGA-II algorithm is proposed to cope with the efficient optimization of multiple objectives containing constraints in the optimal combined carbon-energy flow (OCECF). The algorithm improves the convergence of the population by evolving a new auxiliary population that shares effective information with the original population for weak cooperation, offering significant performance advantages. Applying the algorithm for reactive power control on two different-sized IEEE benchmark systems (IEEE-57 and IEEE-300 bus systems), respectively, minimizes carbon emissions and voltage deviations in the grid. Simulation results show that the CCMO algorithm has significant advantages in terms of the convergence speed and Pareto front.

Lower boundary based nonlinear model predictive control of transmission power for smart grid WSNs

In the application of data collection and communication in smart grid based on wireless sensor networks (WSNs), communication reliability is the key technical index of WSNs. Theoretically, adjusting the transmission power can control the signal-to-noise ratio (SNR), thereby improving the reliability of wireless communication. However, wireless signal is random changes caused by external device interference or environmental factors. Hence, we study a control of transmission power to meet reliability requirements while maximizing the overall efficiency of the network. Considering the random characteristics of wireless links, we introduce the predictive lower boundary with probability guarantees, and establish a nonlinear state-space model that chooses the SNR as the state variable. Subsequently, we adopt the lower boundary based nonlinear model predictive control (LB-NMPC) algorithm to solve the optimal transmission power, and creatively prove the feasibility and stability. Furthermore, we compare the proposed LB-NMPC algorithm with the adaptive transmission power control (ATPC) algorithm, the potential feedback control (PFC) algorithm and the confidence interval based model predictive control (CI-MPC) algorithm by simulation, and the comparative results show that our LB-NMPC algorithm has lower transmission power and less bad controls. Finally, we also test the LB-NMPC algorithm in real-world indoor substation and outdoor substation to verify its practicability and accuracy.

Distance Based Power Control for D2D Communication in LTE-Advanced Networks

Device to device communication systems places a very important role in handling both voice and data services efficiently in LTE-A networks. Power control techniques that we adopt for efficient D2D communication becomes a challenging issue. D2D and cellular signals under the same network may result in In-band Emission Interference (IEI). This degrades discovery range which result in loss of energy efficiency, throughput and hence increases the power requirement in both uplink and downlink transmissions. In this paper, a power control algorithm is introduced which greatly helps in saving power in LTE-Advanced networks for D2D communication. Power calculation mechanisms for D2D users and cellular users are shown separately. Based on mode selection, transmit and receive power requirements are evaluated to obtain the preset Quality of Service (QoS) parameters. The simulation results presented in the proposed method demonstrates the effectiveness of power consumption in LTE-Advanced networks for device to device communication.

Deep Reinforcement Learning-Based Resource Allocation for Satellite Internet of Things with Diverse QoS Guarantee.

Large-scale terminals’ various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT’s normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps.

A novel sliding mode control and modified PSO-modified P&O algorithms for peak power control of PV

Photovoltaic (PV) energy is a renewable source which provides clean energy in power systems. Because of low efficiency of energy conversion in PV systems, harvesting maximum power from these systems is important. Many papers have been focused on this challenge to obtain the maximum power from PV array. Sliding mode control is an efficient control method to achieve maximum power. Although, due to non-linear curve of P–V and I–V of PV array in one side and uncertain PV generated power due to climate and temperature conditions, different evolutionary algorithms must be applied for accurate maximum power point tracking. Perturbation and observation (P&O) is a well-known method in this field but it has some defects in case of shadow-condition multiple maxima. In this paper, considering analysis of two sliding mode control structures, modified particle swarm optimization (PSO) algorithm has been used in combination with modified P&O method called modified PSO-modified P&O (MPSO-MP&O) to compensate the problem of P&O tracking in shadow conditions. The proposed method selects the global maximum point by searching all maximum points in many iterations. Also, the achievement stability of the maximum point is interesting in different temperature conditions and sudden changes of physical parameters of PV system. Simulation results show the efficiency of the proposed method performance in obtaining the maximum point faster with more PV power compared to other efficient methods in this field.

Secure Transmission in Cell-Free Massive MIMO With Low-Resolution DACs Over Rician Fading Channels

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal K$ </tex-math></inline-formula> -factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

Active Cable Sheath Current-Based Protection Scheme for an Offshore HVAC Wind Transmission System

Due to the complex sending terminal structure of offshore wind transmission systems, conventional on-line monitoring-based protection cannot work well. The electromagnetic transient faults are also difficult to locate due to a short time span with fast characteristics that are affected by various power electronic devices and control algorithms. To solve the aforementioned problems, a new offshore wind transmission line protection scheme based on active detection of submarine cable sheath current is proposed. This method uses a normalized coding cooperation to realize the risk levels and failure locations of the transient defects, and then the sheath currents become a characteristic input data source, which can build a clear system protection boundary. Case studies using MATLAB and ATP simulations are carried out, where three types of transient faults represented by sheath currents are studied, i.e., loss of electrical continuity of grounding device, short circuit of segmented metal sheath of cable joint, and water immersion of junction box. Testing results illustrated that the proposed method could achieve fast fault detection and precise fault location of the electromagnetic transients. Moreover, compared with conventional wind farm protection techniques, it only needs to add few signal injection modules with high sampling frequency into the submarine optical fibers.

An efficient resource optimization scheme for D2D communication

With the rapid development of wireless technologies, wireless access networks have entered their Fifth-Generation (5G) system phase. The heterogeneous and complex nature of a 5G system, with its numerous technological scenarios, poses significant challenges to wireless resource management, making radio resource optimization an important aspect of Device-to-Device (D2D) communication in such systems. Cellular D2D communication can improve spectrum efficiency, increase system capacity, and reduce base station communication burdens by sharing authorized cell resources; however, can also cause serious interference. Therefore, research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance. This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network. The proposed algorithm uses the fuzzy clustering method, which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users. An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency. Simulation results show that these proposed algorithms can effectively improve system throughput, reduce co-channel interference, and enhance energy efficiency.

Decentralized Multi-Agent Power Control in Wireless Networks With Frequency Reuse

Many of the existing optimization-based transmit power control algorithms suffer from high computational complexity and require instantaneous global channel state information (CSI), both of which hinder their practical implementation. In this paper, we consider a wireless network with multiple transmitter-receiver pairs, where each transmitter only has access to its local CSI fed back from its intended receiver and does not require local CSI exchange with its neighboring transmitters. In such a network scenario, we propose a deep reinforcement learning based decentralized multi-agent power control (DEC-MAPC) algorithm for sum-rate maximization, where each transmitter acts as an intelligent agent. By leveraging the value decomposition technique, we establish a nonlinear mapping from the local reward of each agent to the global reward. Such a design allows each agent to independently control its transmit power based on its local CSI while enabling global collaboration among the agents. The proposed algorithm is scalable to large-scale networks as only local CSI is required, and is robust to the channel and interference variations via interacting with the environment. Simulation results show that the proposed DEC-MAPC algorithm with local CSI achieves comparable sum-rate performance with the centralized optimization algorithms with global CSI, while significantly reducing the computational complexity.

Toward a Web-Based Digital Twin Thermal Power Plant

As a crucial part of cyber-physical systems, a digital twin can process data, visualize processes, and send commands to the control system, which can be used for the research on thermal power plants that are vital for providing energy for manufacturing and industry, and also daily consumptions. This article introduces the methodologies and techniques toward a web-based digital twin thermal power plant. To implement a web-based digital twin thermal power plant, the architecture, modeling, control algorithm, rule model, and physical-digital twin control are explored. The potential functionalities of the web-based digital twin including real-time monitoring, visualization and interactions, and provided services for physical thermal plants and universities are also presented. A case study has been provided to illustrate the web-based digital twin power plant. The research in this article can provide potential solutions for web-based digital twin research and education.