Optimal Power Control Research Articles

Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems

This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.

Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks

Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

Multi‐layer and timescale optimal power flow control in capacitive coupling inverter‐based microgrids

AbstractCompared with the traditional solution, the microgrid (MG) employed capacitive coupling inverters (CCIs) with higher reactive power capacity can effectively reduce the probability of low voltage. However, because of the special coupling structure, different power flow control methods will greatly affect the cost and operation efficiency of the CCI. A multi‐layer and timescale optimal power flow control considering inverter‐layer is therefore proposed in this paper. Firstly, the power output characteristics of CCI are analyzed, and the power flow control model of CCI with an energy storage unit is established. Secondly, an optimal control method on different timescales was proposed. By coordinating the active power output and the reserved charging state margin of the energy storage unit, the CCI‐MG reactive power regulation with the most economical and minimum loss was realized, so as to achieve long‐term voltage stability and dynamic satisfaction of load demands. Furthermore, the optimal power flow control of CCI is proposed. The simulation results show that the proposed method can effectively realize the optimal control of active and reactive power of MG, and also improve the voltage stability of AC bus under the economical operation of CCI.

Distributed ADMM power optimal control for standalone hybrid generation systems

With the rapid development and increased demand for renewable energy sources, standalone hybrid generation systems have become an essential energy solution. Power optimization control is thus critical to achieving the efficient operation and stability of this system. The distributed ADMM (alternating direction method of multipliers)-based approach has the full potential to deal with the power optimization problem of standalone hybrid generation systems. This study uses an optimization algorithm with a Gaussian penalty function, ADMM-ρ, to alternately optimize the power reference values of wind, light, and battery-containing power generation subsystems. The local controller regulates the output power of the converter according to this reference value. This ensures that the wind and photovoltaic power generation subsystem work in load-tracking or maximum power-tracking modes so that the optimal operation of hybrid power generation meets the balance of supply and demand while prolonging the service life of the batteries. Simulation experiments show that the distributed ADMM algorithm can reliably address the power optimization challenge of hybrid power generation systems.

Source Network Load Storage Access to Power Wireless Private Network Technology Based on 5G Ultra Dense Networking

We make the source network load storage access power wireless private network, this paper proposes a source network load storage access power wireless private network technology based on 5G ultra dense network. The multiple rotation scheduling and self-organizing learning methods are used to establish the deployment model of the source network load storage access node of the power wireless private network under the 5G communication mode. According to the routing control algorithm design of the 5G ultra dense networking node, combined with the integration analysis of the access load parameters, the source network load storage access model of the 5G ultra dense networking under the dynamic load distributed control mode is established. Through the method of optimal control of reactive power and voltage of distribution network, the transmission link equilibrium structure model of 5G source network load storage access to power wireless private network is constructed. Combined with the coverage analysis of link topology structure and the benefit maximization constraint analysis of production and consumption users, the active and reactive capacity analysis of transaction between production and consumption user groups and multiple production and consumption users is adopted. Combined with the energy storage characteristics analysis and power flow parameter calculation of the source network load storage access power, the 5G ultra dense networking and private network access to the source network load storage access power are realized. The test shows that this method has better power balance dispatching ability and larger output power gain when it is applied to the design of source network load storage access power wireless private network.

Active power optimization control of speed closed-loop engine for energy saving in hydraulic excavator

ABSTRACT Most hydraulic excavators use passive power control to adjust the engine speed according to the load torque to achieve power matching, which has very limited effectiveness in energy saving. In this paper, an active power optimization control of speed closed-loop engine is proposed to improve its tracking performance for the dynamic energy-saving operating points. The inertia movement of the engine during variable speed control is analyzed to determine the kinetic energy requirements for different start and end operating points. An RBF-based neural network controller is designed based on power optimization to actively compensate the strong hysteresis of the engine speed relative to the optimal torque. The simulation and the experimental results show that the proposed control method has a much faster response for the energy-saving operating points, which reduces the energy consumption by 9.28% and 5.56% without adding any energy storage devices to the hydraulic excavator.

Asynchronous distributed optimal power control for fatigue load minimization in wind farms

An asynchronous distributed optimal power control (OPC) scheme is proposed to minimize fatigue load in the wind farms (WFs) in this paper. To better satisfy the grid code, each wind turbine (WT) in the scheme is equipped with an energy storage system (ESS) at the DC link of the WT converter. Based on the WT topology, a WT-ESS control model is established, thus developing an OPC scheme to control the generator torque, pitch angle, and ESS output simultaneously for reducing fatigue load. By formulating the model predicted control (MPC)-based optimal problems, the virtual mechanical power control is proposed to regulate the ESSs output for clarifying the power interactions among WTs and ESSs. In order to achieve more effective control of the above three parameters among multiple WTs-ESSs, an asynchronous distributed alternating direction method of multipliers (AD-ADMM) is developed to simultaneously minimize the variations of thrust force, shaft torque, and flapping moment load of the WTs in a wider range. Considering the impact of communication delays, the proposed AD-ADMM algorithm has the higher time efficiency and system robustness. The convergence of the proposed AD-ADMM algorithm is analyzed, and several cases are used to validate the performance of the OPC scheme.© 2017 Elsevier Inc. All rights reserved.

Optical Power Control for GSNR Optimization Based on C+L-Band Digital Twin Systems

Optical Power Control for GSNR Optimization Based on C+L-Band Digital Twin Systems

Joint Optimization of User Association, Power Control, and Dynamic Spectrum Sharing for Integrated Aerial-Terrestrial Network

Joint Optimization of User Association, Power Control, and Dynamic Spectrum Sharing for Integrated Aerial-Terrestrial Network

Towards Optimal Pilot Spacing and Power Control in Multi-Antenna Systems Operating Over Non-Stationary Rician Aging Channels

Towards Optimal Pilot Spacing and Power Control in Multi-Antenna Systems Operating Over Non-Stationary Rician Aging Channels

Power Is Knowledge: Distributed and Throughput Optimal Power Control in Wireless Networks

Power Is Knowledge: Distributed and Throughput Optimal Power Control in Wireless Networks

Bandwidth Allocation and Power Control Optimization for Multi-UAVs Enabled 6G Network

Bandwidth Allocation and Power Control Optimization for Multi-UAVs Enabled 6G Network

Uplink Power Control Optimization for XR and eMBB Co-Existence in 5G-Advanced Networks

Uplink Power Control Optimization for XR and eMBB Co-Existence in 5G-Advanced Networks

Current source converter-based optimal power extraction and power control of a doubly fed induction generator (DFIG) using backstepping control

An improved backstepping control technique for the operation of a doubly fed induction generator (DFIG) powered by a wind turbine (WT) was developed and discussed. It is derived from a current source converter (CSC). The optimal backstepping control (BSC) settings are determined using the CSC approach, which can improve the drive by providing a more rapid dynamic response, greater precision, and stable performance. The CSC approach control function was constructed using the index criterion integral time square error (ITSE) and integral time absolute error (ITAE). With the simulation technique in the MATLAB/Simulink environment, the proposed BSC-CSC effectiveness is confirmed, and analyse the performance by using the PI and SMC control. The results indicate the goals of this work have been achieved in the form of resilience, improved dynamic system efficiency, lowering the harmonic distortion, optimization of stator power, and successfully addressing the problem of model parameter uncertainty.

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.

Reactive power optimization control for multi-energy system considering source-load uncertainty

Reactive power control in power systems is an important way to ensure stable system operation. However, as the coupling between multi-energy systems increases, the uncertainty of supply and demand in the power system poses new challenges for reactive power control. Therefore, the main objective of this study is to perform reactive power balance control of the grid in a multi-energy system. Firstly, we studied the characteristics of reactive power demand during the energy transport of a multi-energy system. In addition, we studied the reactive power output characteristics of various power sources and their uncertainties. The reactive power balance of the multi-energy system and its uncertainty model are established. Then, we studied the adjustable pressure limits of the energy medium transmission pipelines of the heat supply network and the gas pipeline network. The reactive power demand capacity of the system is determined according to the system's energy condition and safe operation boundary. Finally, we established a reactive power optimization simulation model of the multi-energy system, considering the heat-gas network constraints. The study proposes that the optimal reactive power control method can improve the voltage stability of the multi-energy system.

On Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback

We consider a network of N sensors, tasked with solving binary distributed detection, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite-size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC over orthogonal and Markovian time correlated fading channels. The FC fuses the received symbols and makes a global binary decision. We aim at developing adaptive channel-dependent transmit power control policies such that J-divergence based detection metric is maximized at the FC, subject to total transmit power constraint. Modeling quantized fading channel, energy arrival, and battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors’ transmit powers are functions of the battery states, quantized channel gains, and the arrived energies. We utilize stochastic dynamic programming and Lagrangian approach to find the optimal and sub-optimal power control policies. We demonstrate that our sub-optimal policy provides a close-to-optimal performance with a reduced computational complexity and without imposing signaling overhead on sensors.

Federated Learning Over-the-Air by Retransmissions

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.