Power Transmission Scheme Research Articles

Benefit evaluation of HVAC and HVDC for offshore wind power transmission system under the multidimensional index

With the development of offshore wind power transmission technology and the emergence of new dimensions in deep-sea wind power scenarios, some indexes in the existing evaluation system cannot meet the comprehensive benefit evaluation of multiple offshore wind power transmission schemes in new operating scenarios, making the identification of the optimal economic distance of different schemes vague. Therefore, this paper integrates multiple parameters into the total life cycle cost and levelized cost of energy indexes for traditional and new transmission schemes in deep-sea scenarios, considers power generation income, environmental and carbon index, and proposes a multidimensional evaluation method for offshore wind power transmission systems, and analyzes the sensitivity of the constructed indicators. Then, using actual geographic information data, the established parameter fusion multidimensional evaluation method is used to analyze the technical and economic ranges of four offshore transmission schemes. The results show that the optimal economic critical interval of active commutation type high voltage direct current transmission system (CSC) is 98 km, which is about 23 % higher than that of voltage source converter based high voltage direct current transmission system (VSC). Therefore, CSC is more suitable for deep offshore wind power transmission than VSC and has better comprehensive technical and economic performance.

Optimal Multiple Wind Power Transmission Schemes Based on a Life Cycle Cost Analysis Model

Due to the high cost and complex challenges faced by offshore wind power transmission, economic research into offshore wind power transmission can provide a scientific basis for optimal decision-making on offshore wind power projects. Based on the analysis of the topology structure and characteristics of typical wind power transmission schemes, this paper compares the economic benefits of five different transmission schemes with a 3.6 GW sizeable onshore wind farm as the primary case. Research includes traditional high voltage alternating current (HVAC), voltage source converter high voltage direct current transmission (VSC-HVDC), a fractional frequency transmission system (FFTS), and two hybrid DC (MMC-LCC and DR-MMC) transmission scenarios. The entire life cycle cost analysis model (LCCA) is employed to thoroughly assess the cumulative impact of initial investment costs, operational expenses, and eventual scrap costs on top of the overall transmission scheme’s total cost. This comprehensive evaluation ensures a nuanced understanding of the financial implications across the project’s entire lifespan. In this example, HVAC has an economic advantage over VSC-HVDC in the transmission distance range of 78 km, and the financial range of a FFTS is 78–117 km. DR-MMC is better than the flexible DC delivery scheme in terms of transmission capacity, scalability, and offshore working platform construction costs in the DC delivery scheme. Therefore, the hybrid DC delivery scheme of offshore wind power composed of multi-type converters has excellent application prospects.

Covert throughput maximization for NOMA based visible light covert communication networks

Covert throughput maximization for a non-orthogonal multiple access (NOMA)-based visible light covert communication (VLCC) network is investigated. The network consists of a light emitting diode (LED) transmitter, two NOMA users (one public, one covert), and a monitor tasked with detecting any covert transmissions between the LED and the covert user. The transmitter leverages its interaction with the public user to mask the covert communication with the covert user, adopting a random power transmission scheme. This strategy serves to amplify the monitor’s detection uncertainty and significantly enhance the covertness of the VLCC network. Two VLCC scenarios are covered: For the indoor static VLCC scenario where the LED is fixed, subject to the minimum detection error probability of the monitor (covertness constraint) and the outage probability of NOMA users (reliability constraint), the covert throughput is maximized by optimizing the ratio of the LED’s power allocation factor (PAF). For the mobile VLCC scenario where the LED is mounted on an unmanned aerial vehicle (UAV), subject to the constraints of the covertness, reliability and UAV’s flight region, the optimal LED’s PAF ratio and UAV’s location are jointly obtained via a graphical approach. Finally, simulations are carried out to analyze the influence of VLCC parameters on the maximum covert throughput, and results show that compared with benchmark schemes, the proposed scheme can greatly improve the covert throughput.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

An Incorporated Power and Data Synchronous Transmission Method for SRG Integrated Wind Power Generation Systems

Reliable communication places important roles in renewable generation for operating status monitoring, fault alarming, cooperative control and so on. For existing power and data synchronous transmission strategies applied to switched reluctance generator (SRG) systems, the communication rate is limited by very low power switching frequencies. Hence, in this paper, a new PDST strategy is proposed and implemented in an SRG integrated wind generation system by incorporating power and data composite modulation method with signal injection method. For lower signal band, the data is modulated together with power by regulating the lower power switch of each phase in a fixed interval with given frequency, and for the higher signal band, the data in form of voltage ripple is injected into the bus by a coupling circuit. Therefore, specific voltage ripples with different given frequencies can be embedded into the power line for carrying signals, and the frequency band of signal bus can be well exploited. On signal receiver end, the voltage ripples are sampled and analyzed by fast Fourier transform method for demodulating the transmitted data. The feasibility of the proposed incorporated data transmission method is verified by experimental testing.

Rotation-Tolerant Wireless Power Transmission Scheme with Smart Positioning for Cognitive Research on Moving Animals.

This article presents a novel wireless power transmission scheme designed for moving loads. Specifically focused on compensating for the tilt misalignment of the receiver. By utilizing phase-shifted excitation signals from an array of transmitters, the proposed scheme effectively mitigates the impact of misalignment, locally. The application of this scheme holds particular relevance for studying the behavior of moving animals in cognitive research. The system incorporates a cage with two transmitter arrays positioned on the top and bottom sides. To smart determining the receiver's position, the proposed structure utilizes current feedback from the driving circuits and employs SVM (Support Vector Machine) classification algorithms for positioning. Furthermore, when the receiver coil is tilted, a phase shift mechanism significantly enhances the power delivered to the receiver. Additionally, the use of an overlapped transmitter array enhances rotation tolerance and improves the uniformity of the magnetic fields for moving objects. The performance of the proposed scheme is validated through extensive simulations and measurements using a fabricated prototype. Notably, the designed system achieves a power delivery of 296 mW to the load at a 90° angular misalignment, compared to 1.67 µW delivered by conventional array system.

Offshore Wind Power Transmission Decision-making Based on Grey Correlation and Topsis

With the profound transformation of the global energy structure, the development and utilization of wind energy have become the focus of global attention. The offshore wind power industry has ushered in a historic development opportunity and China’s offshore wind power industry has entered the fast track of development. However, due to the variety of transmission schemes available for offshore wind power, there are many differences in the economy, reliability, environment, risk, and other aspects of different transmission schemes, so it is difficult to make the optimal decision. To comprehensively compare the advantages and disadvantages of different transmission schemes in actual scenarios, this paper first identifies their influencing factors to obtain an evaluation index system and uses the entropy weight method to determine the index weight based on the index evaluation data of typical offshore wind power transmission projects at home and abroad, and the grey correlation method and TOPSIS method are integrated to build a comprehensive evaluation model suitable for offshore power transmission. Finally, the scientific and rigorous nature of the research method and model is verified by example analysis. The optimal decision comprehensive evaluation model constructed in this paper can realize scientific and comprehensive evaluation of offshore wind power transmission schemes and has important decision-making significance for the selection of transmission schemes in different scenarios.

Time-synchronized carbon flow metering scheme for electric power transmission, transformation, and distribution networks

Faced with the pressure of energy conservation and emission reduction, the power industry is urgently requires low-carbon transformation. The carbon flow calculation theory redistributes the actual carbon generated by the power plant to the branch and loads customers with the power flow. This paper first introduces the theory of carbon flow calculation and the carbon metrics corresponding to the electricity metrics. Second, a time-synchronous technology is introduced for the carbon flow calculation of transmission, transformation, and distribution networks, and a time-synchronous-based carbon metering system is conceived. The impact of time deviation on carbon metering is elucidated through simulation experiments of IEEE14 standard nodes, and finally, relevant suggestions are made for future research ideas and technical routes.

Research on power gathering problem and power transmission strategy in Guangxi coastal area

the construction of power supply is affected by resource conditions, plant construction conditions, load distribution and power grid structure, which shows a trend of more and more centralized distribution.If the power supply is too concentrated, the short circuit current will be increased, the risk of system security and stability will be increased, and the regional power transmission channel will be insufficient.The key to solve these problems is to find a suitable power transmission strategy.On the basis of fully summarizing the typical structure of domestic power grid, this paper puts forward the construction strategy of group transmission and layered transmission, and applies it to the construction of clean energy transmission grid in Guangxi coastal areas.

Eight Typical Schemes of Offshore Wind Power Transmission and Their Key Technical Problems

Focusing on the three technical features related to the offshore wind turbine and the offshore transmission channel, eight schemes of offshore wind power transmission and their corresponding key technologies are discussed. The first technical feature describes the grid-forming capability of the offshore wind turbine; two types of wind turbines are discussed, namely, the grid-following wind turbine and the grid-forming wind turbine. The second technical feature describes the frequency characteristics of the output voltage and current of the offshore wind turbine, including DC, low-frequency AC, power-frequency AC and medium-frequency AC. The third technical feature describes the power transmission modes adopted by the offshore high-voltage main transmission channel, which are HVDC, high-voltage low-frequency AC and high-voltage power-frequency AC. Finally, the technical characteristics and maturity of the eight offshore wind power transmission schemes are reviewed, and a technical development direction is recommended.

Stability Study of Time Lag Disturbance in an Automatic Tractor Steering System Based on Sliding Mode Predictive Control

To improve the working accuracy and anti-interference capability of the steering operation of an automatic tractor, this paper investigates the tractor steering system. In response to the current problems of high steering resistance during tractor field operations and the low service life of the drive shaft of conventional electric steering wheel solutions, an electro-hydraulic coupled power-assisted solution combining EPS and HPS is proposed. The combination of the EPS system’s high control accuracy and sensitive steering operation and the hydraulic power system’s large steering torque greatly reduces the power of the power motor and battery performance requirements, optimizing the power transmission scheme to achieve green and energy-saving purposes. Secondly, the research is focused on the influence of external disturbances on the stability of the steering system during tractor operation, and a combination of model predictive control and sliding mode control is used to study the steering system control strategy. It is finally demonstrated through simulations and experiments that it can compensate for the disturbance of the system control parameters by external disturbances, has the ability of MPC to handle input constraints and maintains the advantages of SMC robustness.

Radio-frequency plasma to clean ITER front-end diagnostic mirrors in geometry of Edge Thomson Scattering system

The ITER Edge Thomson scattering (ETS) system provides electron temperature and density profile measurements in the ITER tokamak. In collection optics, the front-end metallic first and second mirrors are expected to experience contamination with beryllium, tungsten and construction materials. Plasma cleaning based on a low-pressure radiofrequency discharge is expected to sputter contaminants. In the plasma cleaning system, a water-cooled first mirror is combined with a powered electrode. Water cooling was realized as a notch filter for the driving frequency with the electrode grounded for a DC-voltage. To understand plasma cleaning effects, a new test model reproducing the ETS First and the Second mirror geometries in a vacuum chamber was developed. Ion energies and fluxes were measured for 40–50 MHz discharges in argon and helium at 1–10 Pa with and without the notch filter for various power transmission schemes. Powers in plasma were increased to 300–400 W to achieve ion fluxes suitable for cleaning. 40 MHz discharges were used for cleaning as being more stable. In helium at 5 Pa the ion flux of 1.3·1019 ions·m−2 s−1 and the ion energies of 120–140 eV were considered for cleaning. Sputtering rates of metal layers were measured at 4–5 nm h−1 for W/WO3 films. Sputtering rates were non-uniform over the electrode and were lower than 0.5 nm h−1 at the edges. At 40–50 MHz, two independent discharges could be ignited at the First and the Second mirrors in argon and helium, and were maintained with minimum interference. Redeposition rate on the walls was estimated as 1–1.5 nm h−1, mainly consisting of the chamber construction materials. Parasitic discharges were observed at powers above 200 W in plasma and influenced plasma stability at pressures 1–2 Pa. The results are important for a number of ITER optical diagnostics where plasma cleaning of front-end water-cooled diagnostic mirrors shall be used.

Broadcasting in Cognitive Radio Networks: A Fountain Codes Approach

The present paper addresses the performance of the broadcasting scheme in cognitive radio networks (CRNs) under a fountain codes (FC) approach. Particularly, closed-form expressions are derived for the cumulative distribution function (CDF) and the expectation of the number of FC clock cycles required for broadcasting packets to all secondary receivers. The energy efficiency (EE) of the secondary networks (SN) is also provided. Moreover, a simple transmit power scheme is proposed to enhance the performance of the SN and guarantee the quality-of-service (QoS) of the primary network (PN). The trends of the introduced closed-form performance metrics are then examined versus important network parameters allowing for the optimization of those parameters. Finally, numerical results are provided to confirm the accuracy of the derived mathematical framework and the superior performance of the introduced FC scheme over uncoded broadcasting protocols.

Power allocation model for residential homes using AI-based IoT

Conventional power transmission and distribution schemes have been completely transformed by the development of the Smart Grid (SG). Nearly every field has been impacted by technology and advancements, including smart grids. Electricity also isn't inexpensive to produce, so Smart Meters (SM) play a crucial role in controlling, managing, and performing efficiency to use the electricity successfully on the customer side, which would be referred to as demand side management (DSM). A Home Energy Management System (HEMS) has been proposed in the research study as a way of optimizing home appliances and saving as much money as possible. The Eagle Hard Optimization (EHO) method and our hybridization of the EHO technique have been implemented. EHO appears to be a Genetic Algorithm (GA) known as a Genetic EHO (GEHO). Compared to normal EHO and unplanned systems, simulation results showed that GEHO better planned the devices to lower the maximum price. Peak to Average Ration packs (PAR) has additionally been noted. Due to the planning of the largest amount of devices on both sides, GEHO& unforeseen has PAR equal, while EHO has a low PAR. Various Operation Time Intervals (OTI) were used to help in efficiency measures. With each of the three plans, capacity & costs money have indeed been thoroughly analyzed.

A Power and Spectrum Efficient Uplink Transmission Scheme for QoS-Constrained IoT Networks

Nonorthogonal multiplexing (NOM) is a novel superposition coding scheme that has been recently proposed to improve the throughput of wireless systems. However, restricting the number of multiplexed packets to two limits the throughput improvement of nonorthogonal multiplexing (NOM) to 100% in best-case scenarios. Therefore, this work presents a generalized NOM (GNOM) design with an unlimited number of multiplexed packets. In the multiplexing process, new and retransmitted packets due to arq are combined while considering the impact of channel conditions on the power assigned per packet. The proposed GNOM employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for iot nodes with diverse computational capabilities and quality of service requirements. The obtained results demonstrate the effectiveness of the proposed scheme, which offers up to 200% throughput improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.

Aerial Refueling: Scheduling Wireless Energy Charging for UAV Enabled Data Collection

The working scope and working time of small size unmanned aerial vehicles (UAVs) are limited because of the limited battery capacity. In this work, inspired by aerial refueling for aircrafts, we propose a wireless power transmission (WPT) scheme by categorizing the UAVs into charging UAV (CUAV) and mission UAV (MUAV) for charging UAVs without interrupting the mission. In the proposed aerial refueling scheme, mission UAVs (MUAV) can be recharged by charging UAVs on the fly and operate in a perpetual manner. The feasibility of aerially wireless charging for small UAVs is firstly discussed and evaluated. Then we consider a practical application scenario of multiple MUAVs for collecting data from several points of interest, where the MUAVs are recharged by CUAVs. Accordingly, the issue of scheduling the flying path and charging process of each CUAV to minimize the mission time arises. Deep reinforcement learning (DRL) based algorithms for scheduling both single and multiple CUAVs are proposed and deployed. Extensive simulation evaluations demonstrate that, by applying the proposed aerial refueling scheme, CUAVs can explore and optimize the scheduling strategies, thereby improving the system performance in terms of mission completion and charging efficiency.

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

Secure transmission in satellite-UAV integrated system against eavesdropping and jamming: A two-level stackelberg game model

Aiming at the physical layer security (PLS) secure transmission existing in the information backhaul link of the satellite-UAV integrated (SUI) network, a two-layer Stackelberg game model (TSGM) that can resist full-duplex (FD) eavesdropping and jamming attacks is proposed. The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game. The source UAV adjusts its own transmission power strategy according to the attacker's jamming strategy to resist malicious jamming attacks. The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game, and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks. Aiming at the "selfishness" of UAV nodes, a price incentive mechanism is established to encourage UAV to actively participate in cooperation, so as to maximize the advantages of cooperative communication. For the proposed TSGM, we construct the utility function and analyze the closed equilibrium solution of the game model, and design a three-stage optimal response iterative (TORI) algorithm to solve the game equilibrium. The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.

Multiagent DDPG-Based Joint Task Partitioning and Power Control in Fog Computing Networks

Fog computing is an energy-efficient and cost-effective paradigm to help alleviate the pressure of resource-constrained mobile devices (MDs) running computation-intensive applications. In this article, we investigate the joint task partitioning and power control problem in a fog computing network with multiple MDs and fog devices (FDs), where each MD has to complete a periodic computation task under the constraints of delay and energy consumption. Each task can be partitioned into multiple subtasks and offloaded to the FDs according to the task partition strategy and transmission power strategy to reduce task execution delay and energy consumption. To this end, we present a multiagent deep deterministic policy gradient (MADDPG)-based task offloading algorithm for MDs to maximize the long-term system utility including the execution delay and energy consumption. Each MD inputs the local information, e.g., the task requirements, the available communication, and computation resources of the FDs, the computation resources, and the battery level of the MD into a distributed actor network to generate a task offloading policy, while a centralized critic network is used to update the weights of the actor networks to improve offloading performance. Numerical simulation results demonstrate the effectiveness of the proposed scheme in improving the system utility, reducing the average execution delay as well as the average energy consumption.