Optimal Power Control Research Articles

Advanced Control And Development of Hydro and Diesel Generator Hybrid Power System Models for Renewable Energy Microgrids

The Nigerian power problem resulted to incessant and erratic supply of electricity and this has destroyed many industrial processes in the country. It has reduced productivity and has increased unemployment rate in the country to over 50million (this figure is over 70% of Nigerian youths). This has led many of the youths in the country to crime. It has led to the deaths of many innocent people in the country. As of 2016, the electricity energy consumption in the world from the world fact book revealed that the average power per capita (watts per person) in the United States is 1,377 Watts. In Canada, it is 1704 Watts per person and in South Africa; it is 445 Watts per person and in Australia, average power per capita (watts per person) is as high as 1,112 Watts. Whereas, the average electricity consumed in watts per person in Nigeria is just 14 Watts. Unfortunately, this has put the country in a rank of 189 out of 219 countries estimated. In this research work, a Hybrid Electric Power System (HEPS) which comprises Hydro Electric Power Plant (HEPP) and Diesel Generator (DG) was modelled and a control algorithm was established to improve the performance of the system. Hybrid power system mathematical and Simulink models were developed. The output power of the developed Simulink model was be optimized using optimum power point optimization techniques and control algorithms. Simulink models of the two components of the Hybrid Electric Power System were produced using MATLAB/Simulink software. The develop Simulink models was interconnected and final model was developed. The results obtained revealed that the problems associated with conventional methods of power generation was overcomed by the development of this renewable and non-renewable energy resources Hybrid Electric Power System (HEPS) models.

Optimization Control Strategy for Large Doubly-Fed Induction Generator Wind Farm Based on Grouped Wind Turbine

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

Assessment of Daily Cost of Reactive Power Procurement by Smart Inverters

The reactive power control mechanisms at the smart inverters will affect the voltage profile, active power losses and the cost of reactive power procurement in a different way. Therefore, this paper presents an assessment of the cost–benefit relationship obtained by enabling nine different reactive power control mechanisms at the smart inverters. The first eight reactive power control mechanisms are available in the literature and include the IEEE 1547−2018 standard requirements. The ninth control mechanism is an optimum reactive power control proposed in this paper. It is formulated to minimise the active power losses of the network and ensure the bus voltages and the reactive power of the smart inverter are within their allowable limits. The Vestfold and Telemark distribution network was implemented in DIgSILENT PowerFactory and used to evaluate the reactive power control mechanisms. The reactive power prices were taken from the default payment rate document of the National Grid. Simulation results demonstrate that the optimal reactive power control mechanism provides the best cost–benefit for the daily steady-state operation of the network.

차압 계측에 의한 압축기 토크 연산

Since the engine of an automotive determines the output of the engine torque by compensating a torque corresponding to the compressor torque to drive the refrigerant compressor for an air conditioner, accurately calculating the torque of the air conditioner compressor can improve fuel economy and quietness through optimal engine power control. In this study, a torque calculation based on differential pressure which is a function of refrigerant mass flow rate is proposed by replacing the conventional torque calculation method based on evaporator load condition. Reliability of torque calculation was confirmed through evaluation under an automotive operating condition. In test results, torque calculation error under differential pressure was improved from 0.3~1.0 N·m depending on the condition compared to the previous method.

Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

Quantum-Driven Energy-Efficiency Optimization for Next-Generation Communications Systems

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.

RETRACTED: FPGA-based adaptive linear-line approximation and implementation for optimum photovoltaic device power control

Photovoltaic technology is one of the greatest attractions among the various renewable energy sources, coupled with the availability of atmospheric temperature in quantity and without emissions of greenhouse gases due to its poor PV power. Therefore, networking opportunities lie in applying electrical power generation and transmission methods to produce voltage level with favorable rates for effective application. The photovoltaic panel’s suitable methodology is important for analyzing, controlling, and monitoring a PV generator’s design. The photovoltaic system is the basic component of a PV device, so the PV array needs to be run at the highest power point to optimize the system’s energy production. In the system, a solar tracking system is needed to function at the maximum power point. The characteristics of a PV cell’s output power and energy are semi, and therefore its output fluctuates in compliance with the difference in solar irradiation and temperature. An amount of studies has been aimed at designing successful MPPT schemes over the last decade. Research projects are still increasing interesting; however, discovering building FACTS devices and resolving their digital quality issues. Besides, the architecture of MPPTs that could cope with irradiance and temperature is difficult. The establishment of innovative strategies and transmitted source inverter control techniques for a PV device with different pulse widths. To build a filter that remove the solar array PV panel’s using current instantaneous reactive power. The voltage regulation filter based mostly on PV array is then configured with adjusted adaptive threshold control with sequence analysis size. The MATLAB simulator and FPGA and legitimate digital numerical simulations are expected to show strong harmonic adjustment for the existing PV-based SAPF.

Retraction: FPGA-based adaptive linear-line approximation and implementation for optimum photovoltaic device power control (J. Phys.: Conf. Ser. 1964 062087)

This article has been retracted by IOP Publishing following an allegation that this article cites multiple sources that have no relation to the work. IOP Publishing has investigated in line with the COPE guidelines, and finds the work is nonsensical, with conclusions that are unsupported by the data presented and with multiple irrelevant references.IOP Publishing wishes to credit the authors of one of the unsupported references for bringing the issue to our attention.IOP Publishing Limited have been unable to contact the authors regarding this retraction, despite numerous attempts. The authors are encouraged to contact IOP Publishing Limited if they wish to contest this retraction.Retraction published: 26 April 2022

Bregman-style Online Convex Optimization with Energy Harvesting Constraints

This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve O(√T) regret by using a battery with an O(√T) capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from O(√n) to O(√log(n)). The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component.

Two-stage ADMM-based distributed optimal reactive power control method for wind farms considering wake effects

Since the connection of small-scale wind farms to distribution networks, power grid voltage stability has been reduced with increasing wind penetration in recent years, owing to the variable reactive power consumption of wind generators. In this study, a two-stage reactive power optimization method based on the alternating direction method of multipliers (ADMM) algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems. Unlike existing optimal reactive power control methods, the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure. Furthermore, under the partition concept, the consensus protocol is not needed to solve the optimization problems. In this method, the influence of the wake effect of each wind turbine is also considered in the control design. Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.

Wireless Power and Energy Harvesting Control in IoD by Deep Reinforcement Learning

Internet of Drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. The performance of IoD is greatly affected by drones' battery capacities. We hence utilize the energy harvesting technology to charge the batteries and the wireless power control to adjust the drone wireless transmission power in order to address this challenge. In our work, we investigate the joint optimization of power control and energy harvesting control to determine each drone's transmission power and the transmitted energy from the charging station in time-varying IoD networks. Our objective is to minimize the long-term average system energy cost constrained by the drones' battery capacities and quality of service (QoS) requirements. A Markov Decision Process (MDP) is formulated to characterize the power and energy harvesting control process in time-varying IoD networks. A modified actor-critic reinforcement learning algorithm is then proposed to tackle our problem and its performance is demonstrated via extensive simulations.

A New Power Control Algorithm in MMSE Receiver for D2D Underlying Massive MIMO System

Device to device (D2D) underlying massive MIMO cellular network is a robust deployment which enables network to enhance its throughput. It also improves services and applications for the proximity-based wireless communication. However, an important challenge in such deployment is mutual interference. Interference, in the uplink spectrum, reusing the same resource with cellular user, is caused by D2D users. In this paper, we study a distributed power control (DPC) algorithm, using minimum mean square error (MMSE) filter in receiver, to mitigate the produced interference in this deployment scenario. For the DPC algorithm, employing the coverage probability of D2D links, an optimal power control approach is proposed, which maximizes the spectral efficiency of D2D links. Using this modeling approach, it is possible to derive closed-form analytical expressions for the coverage probabilities and ergodic spectral efficiency, which give insight into how the various network parameters interact and affect the link.‎ Also, the DPC algorithm is modeled by stochastic geometry and receiver filter is designed by estimation theory that a new structure in this robust network is an approach to improve spectral efficiency. Simulation results illustrate enhancing coverage probability performance of D2D links in term of the target (signal to interference ratio) SIR with respect to different receiver filter and other parameters which are existing in D2D links.

Power Control Algorithm Based on a Cooperative Game in User-Centric Unmanned Aerial Vehicle Group

The quality of service (QoS) of a user in user-centric unmanned aerial vehicle group (UUAVG) is degraded by complex cochannel interference; hence, a cooperative game power control (CGPC) algorithm in UUAVG is proposed. The algorithm helps to establish a downlink power control model of the UUAVG, construct a product of the signal to interference noise ratio function of each user as a utility function of the cooperative game, and deduce the optimal power control scheme using the Lagrange function. This scheme reduces the interference of the service unmanned aerial vehicle (UAV) to edge users and improves the communication quality of all the users as well as the throughput of the entire system. Simulation results show that the average throughput of the CGPC algorithm improved by 10.32% compared with the traditional Stackelberg Game based Nonunified Pricing Power Control (SGNPPC) algorithm. This shows that the CGPC algorithm can effectively reduce the transmission power of the cooperative UAV and enhance the capacity of the entire system, ensuring communication quality.

Cyclic Control Optimization Algorithm for Stirling Engines

The ideal Stirling cycle describes a specific way to operate an equilibrium Stirling engine. This cycle consists of two isothermal and two isochoric strokes. For non-equilibrium Stirling engines, which may feature various irreversibilities and whose dynamics is characterized by a set of coupled ordinary differential equations, a control strategy that is based on the ideal cycle will not necessarily yield the best performance—for example, it will not generally lead to maximum power. In this paper, we present a method to optimize the engine’s piston paths for different objectives; in particular, power and efficiency. Here, the focus is on an indirect iterative gradient algorithm that we use to solve the cyclic optimal control problem. The cyclic optimal control problem leads to a Hamiltonian system that features a symmetry between its state and costate subproblems. The symmetry manifests itself in the existence of mutually related attractive and repulsive limit cycles. Our algorithm exploits these limit cycles to solve the state and costate problems with periodic boundary conditions. A description of the algorithm is provided and it is explained how the control can be embedded in the system dynamics. Moreover, the optimization results obtained for an exemplary Stirling engine model are discussed. For this Stirling engine model, a comparison of the optimized piston paths against harmonic piston paths shows significant gains in both power and efficiency. At the maximum power point, the relative power gain due to the power-optimal control is ca. 28%, whereas the relative efficiency gain due to the efficiency-optimal control at the maximum efficiency point is ca. 10%.

Joint UAV channel modeling and power control for 5G IoT networks

This paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

MBSFN or SC-PTM: How to Efficiently Multicast/Broadcast

In Multimedia Broadcast/Multicast Service(MBMS), Multicast/Broadcast Single Frequency Network (MBSFN) and Single-cell Point-to-multipoint Network (SC-PTM) are two essential ways to organize networks to provide multicast services. MBSFN shows its unique advantage of enhancing signals at the boundary of two cells, however, highly requiring synchronization of symbols. SC-PTM shows the advantage of the flexibility on the network deployment. The comparison and complementarity of these two modes are explored. In this paper, we analyze the reception performance of MBSFN and SC-PTM from multiple perspectives. We first compare the successful transmission probability (STP) of MBSFN with that of SC-PTM. Then, we consider optimal power control problems in MBSFN and SC-PTM. The corresponding power control optimization problems are with fraction and max-min forms and solved by the Dinkelbach algorithm. Furthermore, we propose a joint mode-selection and power-control method in the hybrid mode of MBSFN and SC-PTM, where each cell could select the multicast mode from MBSFN and SC-PTM. This is a mixed-integer nonlinear programming (MINLP) problem and solved by the concave-convex procedure (CCCP) and the Dinkelbach algorithm. To further improve the throughput, the appropriate successful reception proportion is selected under the opportunistic multicast. Finally, the numerical simulations show the performance of our algorithms and compare the throughput of two modes, which verify our analysis.

Power Optimal Control Method of Network Big Data for Mechanical Vibration Wireless Sensor

With the gradual deepening of research on wireless sensor networks at home and abroad in recent years, some wireless sensor network application systems have emerged in the fields of academic research and engineering applications. Especially in the industrial field, wireless sensor network, as a new information perception technology, has attracted more and more attention due to its advantages of self-organization, flexible deployment, strong scalability, and on-chip processing capabilities. The purpose of this paper is to study the power optimization control method of wireless sensor of mechanical vibration by network big data. In view of the current problem of power control of mechanical vibration wireless sensor network nodes, first, under the premise of ensuring the accuracy of data collection, the key factors affecting the power control of mechanical vibration wireless sensor network nodes are analyzed, the wireless sensor power control model is designed, and the network big data is proposed. Impact on the power of wireless sensors; finally, based on the above research, the fuzzy power control algorithm and its simulation are specifically studied. First, a point-to-point transmission power control system model is designed, and the fuzzy logic controller module is designed in detail, including fuzzy Part of the membership function design, fuzzy rule determination and the use of defuzzification methods. Then use simulation tools to build a point-to-point simulation model and a simulated network simulation model. Finally, these two models are used to compare the performance of fuzzy power control algorithm and fixed power method. The experimental results show that the network survival time using fuzzy power control algorithm is twice that of the fixed power method. The fuzzy power control algorithm has great advantages in both effective communication distance and network survival time.

Voltage control approach based on PCPM distributed algorithm in the presence of high PV penetration: a stochastic modeling

The uncertainty of solar generation is a challenging issue for the operation and control of distribution systems. Also, because of the large number of control and operation variables in advanced distribution networks, the centralized optimization technique requires high computational capabilities. In this paper, a distributed voltage regulation approach in a distribution system based on the predictor corrector proximal multiplier (PCPM) algorithm has been presented. This paper focuses on the voltage rise in distribution systems with high penetration of photovoltaics. The proposed method uses a clustering approach to divide the network into partitions based on the coupling degrees among different nodes. The optimal reactive power control strategy is conducted in each partition and integrated using PCPM. The proposed method is tested on 69 and 123 nodes IEEE distribution test systems. The results confirmed that the proposed method achieves the optimal solution with the same accuracy as the centralized method but in a much shorter computation time.

UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization

This paper considers secure communications for an underlay cognitive radio network (CRN) in the presence of an external eavesdropper (Eve). The secrecy performance of CRNs is usually limited by the primary receiver's interference power constraint. To overcome this issue, we propose to use an unmanned aerial vehicle (UAV) as a friendly jammer to interfere Eve in decoding the confidential message from the secondary transmitter. Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the non-convexity of the objective and non-convexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the design problem, we first transform it into a more tractable form and then develop an iterative algorithm for its solution by leveraging the inner approximation (IA) framework. Combining tools from IA framework and S-procedure, we further extend the proposed algorithm to a more realistic scenario, where the imperfect location information of ground nodes (including Eve, secondary receiver and primary receiver) is considered, resulting in the average worst-case secrecy rate. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches.

Joint resource allocation and routing optimization for spectrum aggregation based CRAHNs

As an integration of spectrum aggregation (SA) and cognitive radio Ad Hoc networks (CRAHNs), SA-enabled CRAHNs are capable of utilizing non-continuous spectrum components and is promising in improving the network performance. However, existing research on CRAHNs mainly assuming of an ideal spectrum sensing while ignoring the false alarm probability, resulting in an inaccurate capacity characterization. Hence, in this paper, we propose a more accurate capacity characterization while the detection and false alarm probability are considered. Moreover, we propose the joint optimization model for SA-enabled CRAHNs constrained by QoS requirements of primary users and network resource allocation. We propose the prim-dual method to decompose this problem into two sub-problems: a physical (PHY) layer sub-problem on CC assignment and power allocation, and a network layer sub-problem on route selection. Besides, these two sub-problems are coupled on the link capacity constraint. For sub-problem at PHY layer, we propose the genetic algorithm to obtain the optimal CC assignment and successive convex approximation method to the find the optimal power allocation. For sub-problem at network layer, we propose to apply the standard convex method to find the optimal solution. The numerical simulations demonstrate that network throughput can be improved by increasing the number of CCs. The obtained network throughput outperforms algorithms that without power control, spectrum aggregation or route optimization.