The monotonic increasing relationship between average powers of CMOS VLSI circuits with and without delay and its applications

The authors theoretically describe the monotonic increasing relationship between average powers of a CMOS VLSI circuit with and without delay. The power of an ideal circuit without delay, which can be fast computed, has been used as the evaluation criterion for the power of a practical circuit with delay, which needs more computing time, in such fields as fast estimation for the average power and the maximum power, and fast optimization for the low test power. The authors propose a novel simulation approach that uses delay-free power to compact a long input vector pair sequence into a short sequence and then, uses the compacted one to fast simulate the average (or maximum) power for a CMOS circuit. In comparison with the traditional simulation approach that uses an un-compacted input sequence to simulate the average (or maximum) power, experiment results demonstrate that in the field of fast estimation for the average power, the present approach can be 6-10 times faster without significant loss in accur     

Optimal power allocation for homogeneous and heterogeneous CA-MIMO systems

Carrier aggregation (CA) technique has been adopted by 3GPP LTE-Advanced due to its ability of enhancing the spectrum efficiency and peak data rates through aggregating multiple component carriers (CCs). Two main factors make power control optimization very essential for CA-MIMO radio link: the different channel characteristics in multiple CCs, and multiple power constraint conditions (per-CC, per-antenna and pertransmitter power constraints) in the actual CA system deployment. This paper firstly derives the degenerate conditions of optimal power allocation for a single-transmitter CA-MIMO system. Stemming from the derived degenerate conditions, we propose a modified hybrid gradient optimal power allocation(MHGOPA) algorithm to maximize the system performance. Simulation results verify the validity of the proposed resource allocation approach by comparing with baseline average power allocation algorithm. Finally, we extend the MHGOPA algorithm into a heterogeneous CA network with multiple transmitters working simultaneously.     

Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids

The control of battery energy storage systems(BESSs)plays an important role in the management of microgrids.In this paper,the problem of balancing the state-ofcharge(SoC)of the networked battery units in a BESS while meeting the total charging/discharging power requirement is formulated and solved as a distributed control problem.Conditions on the communication topology among the battery units are established under which a control law is designed for each battery unit to solve the control problem based on distributed average reference power estimators and distributed average unit state estimators.Two types of estimators are proposed.One achieves asymptotic estimation and the other achieves finite time estimation.We show that,under the proposed control laws,SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power.A simulation example is shown to verify the theoretical results.     

LQR and LQG based optimal switching techniques for PSS and UPFC in power systems

Selection of better optimized unified power flow controller (UPFC) control inputs along with simultaneous coordinated design of power system stabilizer (PSS) is a challenge in the present scenario of power systems. Hence, in this paper, four sets of experiments performed are presented. First set of experiments are without disturbance scenario where switching is done using linear quadratic regulators (LQR’s). Second set is for power systems with disturbances using linear quadratic gaussian (LQG). Switching control algorithms presented here are tested on the single machine infinite bus (SMIB) linearised Phillips Heffron model of power system using MATLAB/SIMULINKr platform.     

The driving force for development of IC and system in future: Reducing the power consumption and improving the ratio of performance to power consumption

With the development of information technology, integrated circuits (IC) and system which target high performance and low power consumption have widely penetrated to all the aspects of national economy, national defense construction and people’s life. With the continuous increase in IC integration density, the power consumption is becoming the limiting factor. It turns out that the driving force of the future IC and system development is the reduction of the power consumption and improvement of the performa...     

A power-aware code-compression design for RISC/VLIW architecture

We studied the architecture of embedded computing systems from the viewpoint of power consumption in memory systems and used a selective-code-compression (SCC) approach to realize our design.Based on the LZW (Lempel-Ziv-Welch) compression algorithm,we propose a novel cost effective compression and decompression method.The goal of our study was to develop a new SCC approach with an extended decision policy based on the prediction of power consumption.Our decompression method had to be easily implemented in hardware and to collaborate with the embedded processor.The hardware implementation of our decompression engine uses the TSMC 0.18μm-2p6m model and its cell-based libraries.To calculate power consumption more accurately,we used a static analysis method to estimate the power overhead of the decompression engine.We also used variable sized branch blocks and considered several features of very long instruction word (VLIW) processors for our compression,including the instruction level parallelism (ILP) technique and the scheduling of instructions.Our code-compression methods are not limited to VLIW machines,and can be applied to other kinds of reduced instruction set computer (RISC) architecture.     

Balancing the power consumption speed in flat and hierarchical WSN

A combination of a cluster tree routing protocol and an Ad hoc on demand vector (AODV) routing protocol is used in the latest ZigBee standard wireless sensor networks (WSNs) technology. However, the AODV routing protocol has no means by which to take into consideration the power consumption of the nodes during the routing process. Therefore, a new approach is proposed in this paper to balance the power consumption speed and to distribute the responsibilities of routing among fiat wireless sensor nodes and the three levels of hierarchical wireless sensor nodes. These three levels are based on the three types of devices, which are used in the ZigBee standard: the coordinator, the touters, and the end devices. In this paper, we have compared the original AODV routing protocol with our extension approach for the distribution of power consumption. Based on the simulation results, our new approach has achieved better performance in terms of increasing the lifetime of the fiat wireless sensor network, the personal area network (PAN)coordinator, the touters, and the whole network of the hierarchical wireless sensor network. Additionally, it has better performance in terms of distributing the power consumption among the key nodes of the wireless sensor network.     

Low power DCVSL circuits employing AC power supply

In view of changing the type of energy conversion in CMOS circuits, this paper investigates low power CMOS circuit design, which adopts a gradually changing power clock. First, we discuss the algebraic expressions and the corresponding properties of clocked power signals. Then the design procedure is summed up for converting complementary CMOS logic gates employing DC power to the power-clocked CMOS gates employing AC power. On this basis, the design of differential cas-code voltage switch logic (DCVSL) circuits employing AC power clocks is proposed. The PSPICE simulations using a sinusoidal power-clock demonstrate that the designed power-clocked DCVSL circuit has a correct logic function and low power characteristics. Finally, an interface circuit to convert clocked signals into the standard logic levels of a CMOS circuit is proposed, and its validity is verified by computer simulations.     

Adaptive BER-constraint-based power allocation for downlink MC-CDMA systems with linear MMSE receiver

Power allocation plays a crucial role in wireless communications. To facilitate the efficient allocation of limited transmit power, in this paper, a bit error rate (BER) constraint based power allocation scheme is proposed for the downlink of multi-carrier code division multiple access (MC-CDMA) systems with linear minimum mean square error (MMSE) receiver. According to the instantaneous path gains, the proposed scheme can determine the required minimum transmit power on each channel to meet the BER constraint. The simulation results show that the proposed scheme can consistently provide high-quality solutions with a very few iteration time, and also prove that it is efficient in reducing the transmit power while meeting BER constraint, and thereby improves the system performance.     

A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

Maximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.

     

基于无线传感器网络的光伏系统MPPT应用研究

光伏电池输出的功率随外界环境条件的变化而变化,通常采用最大功率点跟踪技术以获得最大功率输出。结合无线传感器网络(WSNs)节点的工作方式与光伏系统的特点,提出了一种基于WSNs的光伏系统最大功率点跟踪技术。针对开路电压法的不足,利用WSNs节点的测温工作方式来进行温度补偿。当系统工作在最大功率点附近时,引入阻抗匹配算法,可有效消减光伏输出功率在最大功率点处的振荡现象,从而提高系统效率。仿真结果验证了该方法的可行性和有效性。     

基于光伏阵列模型预测的混合型MPPT研究

阴影条件下光伏阵列的输出特性曲线呈现多峰值特性,现有的在线式MPPT方法的追踪时间长,追踪过程中电压波动大,容易造成阵列失配.为解决这一问题,提出了一种基于模型预测的混合型MPPT算法,将初步寻优过程放入软件中处理,通过“检测—预测—再寻优”的过程完成多峰值情况下的MPPT.建模仿真证明:与现有MPPT方法相比,基于模型预测的混合型MPPT算法能够有效缩短系统的寻优时间,且电压波动更小,适用于阴影条件下光伏阵列的最大功率追踪.     

Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking

The maximum power point tracking (MPPT) technique is applied in the photovoltaic (PV) systems to achieve the maximum power from a PV panel in different atmospheric conditions and to optimize the efficiency of a panel. A proportional-integral-derivative (PID) controller was used in this study for tracking the maximum power point (MPP). A fuzzy gain scheduling system with optimized rules by subtractive clustering algorithm was employed for tuning the PID controller parameters based on error and error-difference in an online mode. In addition, an Elman-type recurrent neural network (RNN) was used for inverse identification of the PV system and for estimating the solar radiation intensity to determine the MPP voltage. The optimum number of neurons in the single hidden-layer of the RNN was determined by binary particle swarm optimization algorithm. The weights of this RNN were also optimized by using a hybrid method based on the Levenberg-Marquardt algorithm and gravitational search algorithm (GSA). In the proposed fitness function for optimization, both the RNN size and its convergence accuracy were considered. Thus, the algorithm for RNN optimization attempts to minimize both the structural complexity and the mean square error. Simulation results revealed superior performance of GSA in comparison with particle swarm, cuckoo, and grey wolf optimization algorithms. The performance of the proposed MPPT method was evaluated under four different ambient conditions. Our experimental results show that the proposed MPPT method is more efficient than the three competitive methods presented in recent years.     

Maximum power tracking of a generic photovoltaic system via a fuzzy controller and a two-stage DC–DC converter

This study presents a new two-stage DC–DC converter for maximum power point tracking (MPPT) and a voltage boost of a generic photovoltaic (PV) system. An intelligent MPPT of PV system based on fuzzy logic control (FLC) is presented to adaptively design the proposed fuzzy controlled MPPT controller (FC-MPPTC) while a voltage boost controller (VBC) is used to fix the output voltage to a voltage level that is higher than the required operating voltage to the back-end grid impedance. Modeling and simulation on the PV system and the DC–DC converter circuit are achieved by state-space and the software Powersim. The PV string considered has the rated power around 600?VA under varied partial shadings. The FC-MPPTC and VBC are designed and realized by a DSP module (TMS320F2812) to adjust the duty cycle in the two-stage DC–DC converter. A special FLC algorithm is forged to render an MPPT faster and more accurate than conventional MPPT technique, perturb and observe (P&O). The simulations are intended to validate the performance of the proposed FC-MPPTC. Experiments are conducted and results show that MPPT can be achieved in a fast pace and the efficiency reaches over 90?%, even up to 96?%. It is also found that the optimized tracking speed of the proposed FC-MPPTC is in fact more stable and faster than the general P&O method with the boost voltage capable of offering a stable DC output.     

基于PI调节占空比的自适应MPPT方法

最大功率点跟踪(MPPT)技术是提高光伏发电效率的重要途径之一;扰动观测法是MPPT控制中最常用的方法,针对其无法兼顾跟踪速度与最大功率点跟踪过程的震荡问题,提出了一种基于PI调节占空比的自适应MPPT方法,该方法针对占空比采用基于PI调节的自适应策略;通过Simulink建模仿真,与其他方法进行对比分析,结果显示了该方法可显著地提高了最大功率点跟踪的速度与精度。     

分段变步长MPPT算法在风力发电系统中的应用

针对传统定步长爬山搜索(HCS)法在风力发电系统最大功率跟踪(MPPT)控制过程中的快速性和准确性矛盾,提出了一种基于爬山搜索法和模糊控制的分段变步长MPPT算法.该算法根据发电机P-ω特性曲线对最大功率点(MPP)跟踪过程进行分段,使系统能够根据工作点所在的区域选择合适的跟踪算法和步长完成最大功率跟踪.在Matlab/Simulink中分别对提出的模糊分段变步长算法和传统爬山搜索法进行了仿真.仿真结果表明:所提算法明显地改善了系统跟踪MPP的速度和稳态精度,在MPPT方面明显优于传统的爬山搜索法.     

基于自适应线性调节抗干扰PSO的光伏群体MPPT控制

光伏发电已成为新能源发电的主要研究方向,但当外界环境发生突变或由于遮挡使光伏阵列出现阴影时,传统的最大功率点跟踪(MPPT)算法会出现误判或因陷入局部最大功率点等问题而失效。针对这些问题,提出了一种自适应线性调节的粒子群(PSO)算法,并采用一个MPPT控制器同时实现多支路光伏阵列群体MPPT控制。最后,通过仿真验证所提控制策略的有效性。结果表明,自适应线性调节PSO群控方法振荡小,可实时精准跟踪最大功率点,控制电路较为简单,降低系统控制成本。     

A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm

Abstract

Maximum Power Point Trackers (MPPTs) are power electronic conditioners used in photovoltaic (PV) system to ensure that PV structures feed maximum power for the given ambient temperature and sun’s irradiation. When the PV panels are shaded by a fraction due to any environment hindrances then, conventional MPPT trackers may fail in tracking the appropriate peak power as there will be multi power peaks. In this work, a shuffled frog leap algorithm (SFLA) is proposed and it successfully identifies the global maximum power point among other local maxima. The SFLA MPPT is compared with a well-entrenched conventional perturb and observe (P&O) MPPT algorithm and a global search particle swarm optimisation (PSO) MPPT. The simulation results reveal that the proposed algorithm is highly advantageous than P&O, as it tracks nearly 30% more power for a given shading pattern. The credible nature of the proposed SFLA is ensured when it outplays PSO MPPT in convergence. The whole system is realised in MATLAB/Simulink environment.     

采用自适应扰动观察法的光伏并网系统最大功率点跟踪

提出了一种自适应扰动观察（P＆O）算法,用于在不同天气条件下太阳能光伏（PV）并网系统的最大功率点跟踪（MPPT）控制策略。该策略对于从太阳能光伏电池板中,获取最大的功率输出是十分重要的。利用一种依赖于功率变化的可变的扰动步长,提出了改进的自适应扰动观察算法。最后将通过仿真所得到的数据与传统的扰动观察算法进行了比较,结果表明所提出MPPT算法的收敛值和速度得到了改善,稳定时间缩短25%,稳态值提高20%以上,在太阳能光伏并网系统的最大功率点跟踪时是有效而实用的。