Adaptive Power Control Research Articles

A fuzzy logic based method for fault tolerant hierarchical load management of more electric aircraft

With the increasing number of electrical loads, load management of the more electric aircraft becomes crucial for reliability and efficiency. One of the major challenge is to develop an optimal and reliable adaptive power control. This paper presents a three-level load management method with dedicated time steps for fault tolerance and increasing calculation efficiency. Both the operative mode and the health level of the loads are taken into account in the control using fuzzy logic. The electrical system of a V-tail more electric aircraft that consists of a generator, an auxiliary power unit, and several AC/DC buses and loads is examined by the proposed method in normal and faulty cases. Compared with some conventional methods, the proposed load management method has the advantage of efficiently shedding loads according to the power imbalance and the fault situation.

Adaptive direct power control based on ANN-GWO for grid interactive renewable energy systems with an improved synchronization technique

This paper investigates the improvement of synchronization technique for single-phase inverter. Specifically, the paper proposes a modified structure of second-order generalized integrator with frequency-locked loop (SOGI-FLL) with FLL gain normalization. The proposed structure enhances the frequency detection, which makes it a powerful technique under distorted grid voltage. The validation of the proposed synchronization method includes simulations and experimental tests using Xilinx field programmable gate array (FPGA) as the target device. Moreover, time domain simulations using the direct power control (DPC) with the proposed structure are performed. The decoupled active and reactive powers are controlled using the artificial neural networks (ANNs) trained by the mean of a metaheuristic algorithm. In this paper, the grey wolf optimizer (GWO) is proposed to train the multilayer perceptron (MLP). The proposed approach shows better generation of synchronization signals and smooth power quality, making it suitable for grid-tied and microgrids (MGs) power systems control.

Adaptive Power Control Algorithm for Dynamical Transmitted Power Optimization in Mixed-Line-Rate Optical Networks

An adaptive power control algorithm (APCA) based on the proportional–integral–derivative (PID) algorithm with PID gains auto-tuning and particle swarm optimization (PSO) is proposed for mixed-line-rate optical networks. The APCA-PID-PSO dynamically controls the transmitted power according to the link state variations from traffic, channel impairments, as well as other channel-power coupling effects. The link state is based on the channel estimation of the quality of transmission parameters obtained from the optical performance monitors. The APCA-PID-PSO effectiveness in dynamically mitigating the power penalty under measurements’ conditions with uncertainties and noise as well as under perturbations in the power dynamical is demonstrated by numerical results.

Power sharing adaptive control strategy for a microgrid with multiple storage and renewable energy sources

SummaryWith the rapid growth of single‐phase renewable energy source units within three‐phase microgrids, the accuracy of the power sharing per phase among distributed generation (DG) units is necessary. This paper proposes an adaptive power control strategy based on new sliding mode control for islanding operation of a microgrid with multiple three‐phase and single‐phase DGs. The studied microgrid consists of two three‐phase photovoltaic (PV) arrays and three single‐phase DG units, which include PV, fuel cell, and battery systems. In order to control the three‐phase DGs properly, an observer is used to estimate the uncertain system parameters. Then, according to the estimated values, a controller is adapted to the new system conditions. In this control strategy, one of the three‐phase PV arrays is responsible for adjusting the voltage and frequency, and the other is responsible for controlling the load current to manage the power of three‐phase inverters. To control the single‐phase DGs, their power and voltage references are assigned based on the corresponding phase of the three‐phase DGs responsible for power and voltage control. Subsequently, the power and voltage control operation is performed in a per‐unit system. The proposed control strategy is strongly stable against variation of conditions, disturbances, and microgrid parameter uncertainties, and it regulates the active and reactive powers injected by DGs. The feasibility of the proposed control strategy is validated using simulation studies on a multibus islanded microgrid with balanced and unbalanced loading conditions via MATLAB/Simulink software. The results indicate that the proposed approach is able to provide faster response and lower steady‐state error compared with the conventional proportional integral controller.

Adaptive Power Control Scheme for Mobile Wireless Sensor Networks

One of the major factors that affects the performance of wireless sensor networks (WSN) is its limited battery capacity. Mobile wireless sensor networks (MWSN) are just like WSN except either sensor nodes or sink or both can move. Similar to WSN nodes, in MWSN have limited power resources, so network life depends on battery power consumption. Most of the energy consumed by the network is when transmitting or exchange of control information and data is performed. There are different techniques presented by the researchers to conserve energy like topology control, power management, and data driven methods, etc. In this paper, an adaptive power control strategy for conserving energy in MWSN is presented. The proposed scheme uses variable transmission power to reduce energy usage in the transmission of data and control packets and estimates the energy consumed in unicast and broadcast communication of data and control packets. Sensor nodes adjust their transmission power level at every stage of communication which not only reduces the energy utilization by the sensor nodes but also increases data reception at the sink. This is achieved by limiting unnecessary transmissions within sensing regions where they are not intended to reach or exist.

Auto-Tuning PID Distributed Power Control for Next-Generation Passive Optical Networks

This work proposes an adaptive auto-tuning distributed power control strategy aided by proportionalintegral- derivative (PID) and by Adaline artificial neural network (AANN) approaches. The power control mechanism is realized for the upstream of next-generation passive optical networks (NG-PON), primarily deployed in the context of optical code division multiplexing access passive networks. The primary results demonstrate the ability of control and adaptive auto-tuning of the proposed AANN-based DPCA, considering realistic error estimates in the optical channel. For the sake of comparison, an adaptive auto-tuning procedure through the Tyreus–Lyuben method is included, indicating superior Euclidean norm of the NMSE performance combined with the lower complexity of the proposed NN-based DPCA method.

A Self-Reliant DC Microgrid: Sizing, Control, Adaptive Dynamic Power Management, and Experimental Analysis

This paper presents an adaptive power management and control for a self-reliant dc microgrid powered by photovoltaic (PV) array and fuel cell stack that is escorted with the supercapacitor- and electrolyzer-based hybrid energy storage device. The prime operational impediments associated with this dc microgrid are the unpredictable variations in the PV generation and load demand, fuel starvation phenomenon of the fuel cell, the time constant associated with the electrolyzer operation, and state-of-charge limitation of the supercapacitor. In this paper, the above-mentioned issues are addressed by employing an adaptive dynamic power management strategy (ADPMS) that supervises the overall power flow in the system. The ADPMS enables the co-existence of high power density and high energy density storage devices to deliver the power flow required by the loads. The expeditious support extended by the supercapacitor helps in swift regulation of the dc-link voltage during unforeseen transient load/source power variations. The electrolyzer–fuel cell combination employed in this system can supplement a battery bank and can equip high energy density and self-reliance to the system. The set-reset flip-flop based fixed frequency current controller is simple and effective in accurately tracking the reference currents established by the ADPMS. The proposed ADPMS along with the control is validated for transient unforeseen load profiles and renewable generation changes via simulation studies and its performance is also validated through an experimental analysis on a laboratory-scale dc microgrid testbed.

A Power Control Algorithm Based on Outage Probability Awareness in Vehicular Ad Hoc Networks

This paper addresses the problem of adaptive power control based on outage probability minimization in Vehicular Ad Hoc Networks (VANETs), called a Power Control Algorithm Based on Outage Probability Awareness (PC-OPA). Unlike most of the existing works, our power control method aims at minimizing the outage probability and then is subject to the density of nodes in certain area. To fulfill power control, cumulative interference is assumed to be available at the transmitter of each terminal. The transmitters sent data by maximum power and then get the cumulative interference-aware outage probability. Furthermore, we build the interference model by stochastic geometric theory and then derive the expression between outage probability and cumulative interference. According to the expression, we adjust the transmitter power and optimize the outage probability. Simulation results are provided to demonstrate the effectiveness of the proposed power control strategies. It is shown that the PC-OPA can achieve a significant performance gain in terms of the outage probability and throughputs. Comparing MPC (Maximum Power Control algorithm) and WFPC (Water-Filled Power Control algorithm), the proposed PC-OPA decreased by 23% in terms of the outage probability and increased by 25% in terms of throughputs.

Adaptive Constant Power Control of MHz GaN-Based AC/DC Converters for Low Power Applications

With offline single-phase power supplies moving toward the megahertz switching frequency range, the converter's bus capacitor becomes the bottleneck of further improvement of power density. Partial constant power operation is effective in reducing the bus capacitance and capacitor current stress for low-power applications. However, its impact on circuit design and operation design has not been studied thoroughly. With the developed circuit operation and loss model, this paper presents design considerations and power loss analysis of megahertz ac/dc converters in partial constant power operation. An adaptive constant power control is also proposed, based on the developed models, to improve the light-load efficiency and power factor. A 65 W, megahertz critical conduction mode GaN-based ac/dc converter prototype has been developed to validate the analysis and circuit operation method. Compared with power factor correction (PFC) operation, partial constant power control exhibits an efficiency drop of less than 1%. However, a 50% reduction of bus capacitance can be realized. With the proposed adaptive constant power control, the light-load efficiency and power factor can be improved to be a level similar to the PFC operation. This paper ends with a discussion of the possibility of extending the proposed operation method to higher power applications.

Distance-Driven Consensus Quantification

Distributed cooperative control requires that every participant shares a consistent view of objectives and the world. Information is periodically disseminated over a noisy time-varying network topology so that all the agents asymptotically converge to a common value. However, the strict global consensus is of excessive resource consumption and not mandatory for the majority of coordination tasks. To better satisfy such quantitative requirements of consensus in the practical multi-agent systems, this paper proposes a real time and distance-driven consensus quantification model especially for C-ITS applications. This model encodes agents’ spatial location distribution into their mutual consensus quantification through introducing their inter-distance into consensus calculation. Accordingly, this paper proposes a distance-driven-consensus-based power adaptive control method as a practical use case of the quantitative framework of consensus, by which agents can autonomously optimize the transmit power through balancing the desired consensus benefit and power cost according to the real timely predicted local consensus. We perform extensive numerical calculations to investigate the effectiveness and the applicability of the consensus quantification framework and the power adaptive control method. The results show that the model can effectively capture the real time consensus fluctuation as the multi-agent systems evolve and can provide reliable decision basis to cooperative control, in such way to restrict the consensus extent to a target value and to tradeoff between the anticipated consensus level and the paid cost accordingly.

Enhanced Bayesian Based MPPT Controller for PV Systems

We tackle the problem of a photovoltaic (PV) controller for maximum power point tracking (MPPT) under varying insolation and shading conditions. A general-purpose adaptive maximum power controller is tailored to maintain operation of the PV system at the maximum power point while constantly avoiding local maxima for changing environmental conditions. While a variety of conventional MPPT algorithms have been designed for ideal operating situations, very few were able to deliver true maximum power under abrupt changes in sun shading. Under these dynamic changes, most MPPT techniques fail to rapidly locate the global maximum power point and are stuck at global maxima, leading therefore to inconsistent power generation and low system efficiency. In this paper, we apply Bayesian fusion, a machine learning technique otherwise used for unsupervised classification, curve detection, and image segmentation, in order to achieve global MPPT in record time. Simulation results validated with real-life experimental studies demonstrated the ameliorations of the proposed technique compared to state-of-the-art methods. Using this algorithm, the total output power of the solar system is maximized while minimizing the steady-state oscillations and the tracking time.

Distributed heuristic adaptive power control algorithms in femto cellular networks for improved performance

AbstractAdaptiveness of femto base station's (FBS's) transmission power (TP) is crucial in determining Quality of Service and ecofriendliness of a network. Joint power optimization and admission control problems have been reformulated to identify the TP of v already deployed FBSs, and the problem is shown to be computationally hard. Accordingly, an adaptive distributed heuristic called power search algorithm (PSA) is proposed. A learning algorithm analytically identifies the received signal strength‐based coverage of each FBS in all directions during planning. A 3‐dimensional reference matrix, ie, REF, for each FBS, is thereby formulated and stored in the corresponding FBSs. Power search algorithm handles call admission/termination at run time. For call admission, PSA identifies the serving FBS and the required minimum TP. A suitable data structure is also maintained by PSA for efficient call handling. A new call is dropped if its admission degrades the Quality of Service of existing end‐users. For call termination, change in TP is triggered if the highest power level of the FBS was being exercised to serve this particular user. The worst‐case run‐time complexity of PSA turns out to be O(log2N), where N is the number of TP levels of a particular FBS. Comparing with existing heuristics, improved performance of PSA includes highest throughput and signal‐to‐interference‐plus‐noise ratio while minimizing starvation and cumulative TP with lower complexity even for high end‐user density. Exhaustive simulation reveals that the probability of an end‐user having signal‐to‐interference‐plus‐noise ratio above 0 dB is 0.83 even in the worst case scenario. Accordingly, the proposed PSA is claimed to be superior.

Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs.

The energy-efficient and reliable delivery of data packets in resource constraint underwater wireless sensor networks (UWSNs) is one of the key considerations to enhance the network lifetime. The traditional re-transmissions approach consumes the node battery and increases the communication overhead, which results in congestion and affects the reliable data packet delivery in the network. To ensure the reliability and conserve the node battery, in this paper, we propose adaptive forwarding layer multipath power control routing protocol to reduce the energy dissipation, achieve the data reliability and avoid the energy hole problem. In order to achieve the reliability, tree based topology is exploited to direct multiple copies of the data packet towards the surface through cross nodes in the network. The energy dissipation is reduced by a substantial amount with the selection of low noise path between the source and the destination including the information of neighbors of the potential forwarder node. Extensive simulation results show that our proposed work outperforms the compared existing scheme in terms of energy efficiency and packet received ratio (PRR).

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross‐layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of near‐sink area is high and that in far‐sink area is low, a cross‐layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission‐only mechanism.

Optimal Power Splitting for Simultaneous Wireless Information and Power Transfer in Amplify-and-Forward Multiple-Relay Systems

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) amplify-and-forward relay systems, in which multiple relay nodes support communication between a source node and a destination node. Relay node transceivers are equipped with radio frequency energy harvesters for a power source to retransmit the signals from the source to the destination node. Both the power splitting (PS) ratio and adaptive power control are jointly optimized for SWIPT multiple-relay systems. We prove that the optimal power allocation strategy in our joint solution is to fully use the harvested power, which is contrary to conventional non-SWIPT multiple-relay systems. Based on this result, we propose an optimal centralized PS scheme and suboptimal distributed PS methods. Simulation results demonstrate that the proposed schemes outperform the naive PS schemes in terms of the average rate and the bit-error rate.

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.

Link Adaptive Power Control and Allocation for Energy–Efficient Downlink Transmissions in LTE Systems

It is axiomatic that providing more transmission power by the cell, returns high data rates; but in contrary, more power is consumed which leads to energy exhaustion. The Quality of Service (QoS) in long term evolution urban macrocell networks gives a high concern to green communication by wisely utilizing the limited cell power to improve network performance. Nevertheless, in conventional schemes, it is observed that the maximum power assigned to the evolved Node B (eNB) is fully utilized each time transmission interval regardless the transmitted amount of data. Consequently, a high level of power dissipation commonly occurs at the eNB that is caused by either an unused allocated power or an excessive subchannel power allocation which is beyond the required portion for data blocks transmission. Therefore, in this paper, we propose an efficient scheme, namely link adaptive power control and allocation (LaPCA) to mitigate the overused transmission power of the cell, and thereby, enhancing system energy efficiency while maintaining a good QoS level. The main principle in LaPCA is to control the portion of cell transmission power to be proportional to the volume of data flows that are nominated for transmission during the scheduling process. This power is then distributed over the allocated subchannels by means of nonconvex optimization to enhance system performance. System-level simulations reveal that LaPCA achieves an outstanding energy efficiency and maintains an increased throughput level and low loss ratio as more traffic load is offered to the network.

A Novel Cognitive Satellite Network With GEO and LEO Broadband Systems in the Downlink Case

With the development of satellite communication, the number of satellites in space continuously increases. However, the available spectrum resources are scarce. To address the spectrum scarcity, sharing the spectrum between different communication systems is a promising option. In this paper, a novel cognitive satellite network with geostationary earth orbit (GEO) and low earth orbit (LEO) broadband systems is studied in the downlink case. First, we present a general interference analysis model and simplify it by transforming the spatial dimension into the time dimension according to the satellite motion. Based on the interference analysis, an optimization algorithm with beamhopping and adaptive power control techniques is proposed, which can simultaneously enhance the spectral efficiency and protect the primary system. The system performance of the coexistence scenario is evaluated in terms of the system throughput. Numerical results demonstrate that the spectrum-sharing method between GEO and LEO systems is feasible, and the cognitive network can achieve a high spectral efficiency. Furthermore, the factors that affect the system in the spectral coexistence scenario are analyzed.

Adaptive Reactive Power Control of PV Power Plants for Improved Power Transfer Capability Under Ultra-Weak Grid Conditions

This paper analyzes the power transfer limitation of the photovoltaic (PV) power plant under the ultra-weak grid condition, i.e., when the short-circuit ratio (SCR) is close to 1. It explicitly identifies that a minimum SCR of 2 is required for the PV power plant to deliver the rated active power when operating with the unity power factor. Then, considering the reactive power compensation from PV inverters, the minimum SCR in respect to power factor (PF) is derived, and the optimized coordination of the active and reactive power is exploited. It is revealed that the power transfer capability of PV power plant under the ultra-weak grid is significantly improved with the low PF operation. An adaptive reactive power droop control is next proposed to effectively distribute the reactive power demands to the individual inverters, and meanwhile, maximize the power transfer capacity of the PV power plant. Simulation results of a 200-MW PV power plant demonstrate that the proposed method can ensure the rated power transfer of PV power plant with the SCR of 1.25, provided that the PV inverters are operated with the minimal PF=0.9.

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