Mobile Station Position Estimation using Remote Radio Head with Adaptive Power Control

The adaptive power control of CDMA （Code Division Multiple Access） communications between multiple MSs （Mobile Stations） with a link-budget based SIR （Signal-to-lnterference Ratio） estimate is applied to inner loop power control algorithms. CTR （Consecutive Transmit-Power-Control Ratio） calculated from these algorithms can estimate MS speed, together with MS moving distance, but cannot estimate MS position. In this paper, RRH （Remote Radio Head） is introduced and it is concluded that BS （Base Station） calculates MS distance with CTR from one of RRHs, in addition BS that has RSSIs （Received Signal Strength Indicators） information from other two RRHs can estimate MS position.

A wideband LC-VCO with small VCO gain variation and adaptive power control

A wideband LC tank voltage-controlled-oscillator（VCO） is proposed.To solve the impacts of wideband operation on VCO gain（K_（VCO）） variation and start-up constraint,a binary-weighted varactor array and a binary-weighted negative resistance array all with optimal unit values are designed.Implemented in a 0.18μm CMOS process,the proposed VCO shows a frequency tuning range from 1.9 to 3.1 GHz,with a current consumption varying accordingly from 14.2 to 4 mA from a 1.8 V supply.With the proposed K_（VCO） suppression technique,the K_（VCO） varies from 50 to 60 MHz/V in the entire frequency range.The measured phase noise is -117 dBc/Hz at a 1 MHz offset from a 3 GHz carrier.

Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

An energy storage station(ESS)usually includes multiple battery systems under parallel operation.In each battery system,a power conversion system(PCS)is used to connect the power system with the battery pack.When allocating the ESS power to multi-parallel PCSs in situations with fluctuating operation,the existing power control methods for parallel PCSs have difficulty in achieving the optimal efficiency during a long-term time period.In addition,existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality.To overcome these challenges,an adaptive power control method based on the double-layer Q-learning algorithm for n parallel PCSs of the ESS is proposed in this paper.First,a selection method for the power allocation coefficient is developed to avoid repeated actions.Then,the outer action space is divided into n+1 power allocation modes according to the power allocation characteristics of the optimal operation efficiency.The inner layer uses an actor neural network to determine the optimal action strategy of power allocations in the non-steady state.Compared with existing power control methods,the proposed method achieves better performance for both static and dynamic operation efficiency optimization.The proposed method optimizes the overall operation efficiency of PCSs effectively under the fluctuating power outputs of the ESS.

Novel Energy-Efficient Opportunistic Routing Protocol for Marine Wireless Sensor Networks Based on Compressed Sensing and Power Control

In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.

Adaptive fractional integral terminal sliding mode power control of UPFC in DFIG wind farm penetrated multimachine power system

With an aim to improve the transient stability of a DFIG wind farm penetrated multimachine power system(MPN),an adaptive fractional integral terminal sliding mode power control(AFITSMPC)strategy has been proposed for the unified power flow controller(UPFC),which is compensating the MPN.The proposed AFITSMPC controls the dq-axis series injected voltage,which controls the admittance model(AM)of the UPFC.As a result the power output of the DFIG stabilizes which helps in maintaining the equilibrium between the electrical and mechanical power of the nearby generators.Subsequently the rotor angular deviation of the respective generators gets recovered,which significantly stabilizes the MPN.The proposed AFITSMPC for the admittance model of the UPFC has been validated in a DFIG wind farm penetrated 2 area 4 machine power system in the MATLAB environment.The robustness and efficacy of the proposed control strategy of the UPFC,in contrast to the conventional PI control is vindicated under a number of intrinsic operating conditions,and the results analyzed are satisfactory.