A novel of fuzzy PSS based on new objective function in multimachine power system

This article proposed a new control strategy based on Takagi–Sugeno fuzzy model for deceasing the power system oscillation. This controller is based on the parallel distributed compensation structure, the stability of the whole closed‐loop model is provided using a general Lyapunov‐Krasovski functional. Also, in this article, a new objective function has been considered to test the proposed Fuzzy Power System Stabilizer in different load conditions which increase the system damping after the system undergoes a disturbance. So, for testing the effectiveness of the proposed controller, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been used over 16 machine 68 bus power system. The eigenvalue analysis and nonlinear time domain simulation results proof the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 288–298, 2016     

Analysis of limit cycle behavior in DC–DC boost converters

This paper deals with limit cycle behaviors in DC–DC boost converters with a proportional-integral (PI) voltage compensator, which is a popular design solution for increasing output voltage in power electronics. Extensive cycle-by-cycle numerical simulations are used to capture all limit cycle behaviors. It is found that there exist two types of limit cycle behaviors rather than only one type in a boost converter. For each type of limit cycle, its underlying mechanism is revealed by circuit analysis. Moreover, the critical condition is derived to predict the occurrence of the limit cycle behaviors in terms of Routh stability criterion, and the analytical expressions for the limit cycles I and II are given based on the averaged model approach. Finally, these theoretical results are verified by numerical simulations and circuit experiments.     

Chaos control of new Ikeda–Lorenz systems by GYC partial region stability theory

A new strategy to achieve chaos control by GYC partial region stability theory is proposed. By using the GYC partial region stability theory, the Lyapunov function is a simple linear homogeneous function of error states, the controllers are more simple and have less simulation error because they are in lower degree than that of traditional controllers. Simulation results for a new Ikeda–Lorenz system show the effectiveness of this strategy. Copyright © 2008 John Wiley & Sons, Ltd.     

A generic design methodology for sliding mode control of switched systems

The aim of this paper is to present a generic methodology to design sliding mode controllers for multivariable switched systems affine in control such as dc–dc power converters. An original formulation of the so-called reachability condition, suitable for this class of systems, is established. Based on the choice of a Lyapunov-like function and parameterized by a single weighting matrix, it allows several kinds of control strategies to be derived, namely conventional piecewise continuous strategies as well as discrete (Boolean) strategies. Its application to the important subclass of linear time invariant systems is investigated more specifically. In the Boolean case, the present approach is also compared to another hybrid one called the stabilizing approach. Eventually, its efficiency as a design methodology, as well as the performance of the resulting control, are shown by simulating it on non-trivial examples of power converters.     

功率放大器失真问题研究及预失真建模

电子器件中的功率放大器常常伴随着非线性失真效应.为解决此类非线性失真问题,通过研究无记忆和有记忆功放的失真特性,运用最小二乘法来构建多种形式的特性拟合函数,在选择了效果较好的特性拟合函数基础上,根据实际约束条件进行预失真模型的建立,使失真处理后的输出信号趋于线性,最后从信号的功率谱密度的角度出发检验预失真模型的补偿效果,证明模型具有较好的可行性和准确性.     

基于非对称演化博弈的京冀雾霾协同治理联盟稳定性分析

当前,雾霾已成为制约首都经济、社会发展的首要环境问题。本文基于生态补偿及生态索赔的视角,构建了京冀雾霾治理联盟的非对称演化博弈模型,分析了京冀雾霾治理联盟的稳定性,并得到最优策略组合。研究结果表明:在不实行行政干预的情况下,由于成本因素及当地政府生态索赔措施的疲软,企业不会自主选择节能减排策略。合理的生态补偿标准对于企业采取节能减排策略至关重要。当两地生态索赔的总值低于企业节能减排的成本时,无益于企业采取节能减排策略,且生态补偿的额度过高会使得企业宁愿索赔不治污。只有两地生态索赔的总值高于企业节能减排成本,企业才会采取节能减排策略,并且实施节能减排策略不会被生态补偿的额度撼动。     

Implementation of parallel branch-and-bound algorithms – experiences with the graph partitioning problem

Parallel processing is one of the essential concepts in the attempts to increase the computational power available for solving continuous and discrete optimization problems. In the case where an optimization algorithm is search-based, crucial issues of parallel distributed implementations are work-load distribution and granularity, i.e. how to distribute the search space among processors and how to control the amount of processing between interprocessor communication. The present paper compares distributed implementations of two branch-and-bound algorithms for the graph partitioning problem: Given an undirected graph with an even number of edges and weights assigned to each edge, partition the vertices into two subsets of equal size such that the sum of the costs of edges connecting vertices in different subsets is as small as possible. The problem is known to be NP-complete. The two branch-and-bound methods compared differ in design strategy: One is based on time-consuming bound calculations leading to tight bounds and thus a narrow search tree with few nodes, whereas the other employs an easy bound calculation scheme leading to a larger search tree. Both have been implemented on an iPSC-hypercube with 32 processors. We investigate the influence of the design strategy on the performance of the algorithms.     

Nonlinear behavior and characterization of a piezoelectric laminated microbeam system

A methodology of analyzing and characterizing the responses of a piezoelectric laminated microbeam system actuated by AC and DC voltages is developed in this research. The present development is based on the piezoelectric theory, Euler–Bernoulli hypothesis, and a newly developed periodicity–ratio (P–R) approach. The electric excitation loading on the beam is considered to be generated by AC and DC interactions. The control voltage of the piezoelectric layer and the geometric nonlinearity of the beam are also taken into account. The analysis of the nonlinear motion trend of the beam system with multiple parameters is carried out with the employment of the P–R criterion. The findings of the research are significant for the design of microbeam systems and micro-structures.     

Quantum numerical control for free elementary particle

This work is to survey the quantum control for free elementary particle numerically. Without considering of the magnetic and electronic fields, control theory of nucleus (nucleons and meson) will be proposed. Furthermore, computational approach will be preformed for control landscape in two dimensions (2D) to illustrate the theoretic results.     

Wavelet neural network based on islanding detection via inverter‐based DG

In this article, a passive neurowavelet based on islanding detection technique for grid‐connected inverter‐based distributed generation has been developed. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem‐free. Unintentional islanding is one of the encountered problems. Islanding is the situation where the distribution system containing both distributed generator and loads is separated from the main grid as a result of many reasons such as electrical faults and their subsequent switching incidents, equipment failures, or preplanned switching events like maintenance. The proposed method utilizes and combines wavelet analysis and artificial neural network to detect islanding. Discrete wavelet transform is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. Passive schemes have a large nondetection zone (NDZ) and concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulations results, performed by MATLAB/Simulink, shows that the proposed method has a small NDZ. Also, this method is capable of detecting islanding accurately within the minimum standard time. © 2014 Wiley Periodicals, Inc. Complexity 21: 309–324, 2015     

AIS Based Distributed Wireless Sensor Network for Mobile Search and Rescue Robot Tracking

This research presents the implementation of GSCF, an AIS-based control framework, on a distributed wireless sensor network for tracking search and rescue robots in open fields. The General Suppression Control Framework (GSCF) is a framework inspired by the suppression hypothesis of the immune discrimination theory. The framework consists of five distinct components; each carries a specific function that can generate long-term and short-term influences to other components by the use of humoral and cellular signals. The goal of the research is to develop mathematical models that can assist the control and analyses of robots behavior through the use of Suppressor Cells in the Suppression Modulator. Acquire data from the physical robot will be used as simulation parameters in future search and rescue research.     

Irreversible investments with delayed reaction: an application to generation re-dispatch in power system operation

In this article we consider how the operator of an electric power system should activate bids on the regulating power market in order to minimize the expected operation cost. Important characteristics of the problem are reaction times of actors on the regulating market and ramp-rates for production changes in power plants. Neglecting these will in general lead to major underestimation of the operation cost. Including reaction times and ramp-rates leads to an impulse control problem with delayed reaction. Two numerical schemes to solve this problem are proposed. The first scheme is based on the least-squares Monte Carlo method developed by Longstaff and Schwartz (Rev Financ Stud 14:113–148, 2001). The second scheme which turns out to be more efficient when solving problems with delays, is based on the regression Monte Carlo method developed by Tsitsiklis and van Roy (IEEE Trans Autom Control 44(10):1840–1851, 1999) and (IEEE Trans Neural Netw 12(4):694–703, 2001). The main contribution of the article is the idea of using stochastic control to find an optimal strategy for power system operation and the numerical solution schemes proposed to solve impulse control problems with delayed reaction.     

Pricing a European Basket Option in the Presence of Proportional Transaction Costs

A crucial assumption in the Black–Scholes theory of options pricing is the no transaction costs assumption. However, following such a strategy in the presence of transaction costs would lead to immediate ruin. This paper presents a stochastic control approach to the pricing and hedging of a European basket option, dependent on primitive assets whose prices are modelled as lognormal diffusions, in the presence of costs proportional to the size of the transaction. Under certain assumptions on the individual preferences, it is able to reduce the dimensionality of the resulting control problem. This facilitates considerably the study of the value function and the characterisation of the optimal trading policy. For solution of the problem a perturbation analysis scheme is utilized to derive a non‐trivial, asymptotically optimal result. The findings reveal that this result can be expressed by means of a small correction to the corresponding solution of the frictionless Black–Scholes type problem, resembling a multi‐dimensional ‘bandwidth’ around the vanilla case, which, moreover, is readily tractable.     

Functions via everyday actions: Support or obstacle?

The general context of this paper is the power of intuitive thinking, and how it can help or hinder analytical thinking. The research literature in cognitive psychology teems with tasks where intuitive thinking leads subjects to “non-normative” answers, including tasks for which they have all the knowledge necessary for the normative answer. The best explanation to date for such phenomena is dual-process theory, which stipulates the activation of a quick automatic intuitive process (System 1), together with the failure of the heavy, lazy, and computationally expensive analytical process (System 2) to intervene and correct the intuitive response.In an earlier paper, we have documented a clash between intuitive and analytical thinking concerning functions, which we have termed the changing-the-input phenomenon. The discovery of the changing-the-input phenomenon, however, left us with a puzzle: Why has this phenomenon concerning functions – a purely mathematical concept – been observed in computer science classes but not in mathematics ones? The purpose of the present paper is to address this puzzle. More generally we ask, under what conditions the changing-the-input phenomenon will or will not be manifested? Still more generally, in learning about functions, when is the intuitive scaffolding of functions via actions-on-tangible-objects helpful, and when does it get in the way of deeper understanding?     

Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria

System identification is an important means for obtaining dynamical models for process control applications; experimental testing represents the most time-consuming step in this task. The design of constrained, “plant-friendly” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear problem example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has great benefits compared to multisine signals that minimize crest factor. The constrained nonlinear optimization problems that are solved represent challenges even for high-performance optimization software.     

Computation of viability kernels: a case study of by-catch fisheries

Traditional means of studying environmental economics and management problems consist of optimal control and dynamic game models that are solved for optimal or equilibrium strategies. Notwithstanding the possibility of multiple equilibria, the models’ users—managers or planners—will usually be provided with a single optimal or equilibrium strategy no matter how reliable, or unreliable, the underlying models and their parameters are. In this paper we follow an alternative approach to policy making that is based on viability theory. It establishes “satisficing” (in the sense of Simon), or viable, policies that keep the dynamic system in a constraint set and are, generically, multiple and amenable to each manager’s own prioritisation. Moreover, they can depend on fewer parameters than the optimal or equilibrium strategies and hence be more robust. For the determination of these (viable) policies, computation of “viability kernels” is crucial. We introduce a MATLAB application, under the name of VIKAASA, which allows us to compute approximations to viability kernels. We discuss two algorithms implemented in VIKAASA. One approximates the viability kernel by the locus of state space positions for which solutions to an auxiliary cost-minimising optimal control problem can be found. The lack of any solution implies the infinite value function and indicates an evolution which leaves the constraint set in finite time, therefore defining the point from which the evolution originates as belonging to the kernel’s complement. The other algorithm accepts a point as viable if the system’s dynamics can be stabilised from this point. We comment on the pros and cons of each algorithm. We apply viability theory and the VIKAASA software to a problem of by-catch fisheries exploited by one or two fleets and provide rules concerning the proportion of fish biomass and the fishing effort that a sustainable fishery’s exploitation should follow.     

功率放大器非线性特性及预失真建模

信号的功率放大器是电子通信系统的关键器件之一,功放的输出信号相对于输入信号可能产生非线性变形,这将带来无益的干扰信号,研究其机理并采取措施改善,具有重要意义.通过利用无记忆非线性功放和记忆非线性功放的实测数据用数学方法对其分别进行建模,而后使用前置预失真器的方法改善功放的非线性特性,并对其中预失真器的建模做了研究,采用间接学习型结构构建预失真器模型.仿真结果显示功放非线性模型结合预失真器模型能够很好地逼近实际情况,并且能很好地抑制带外频谱扩展.     

Optimizing microwind rural electrification projects. A case study in Peru

Electrification systems based on the use of renewable energy sources are suitable for providing electricity to isolated communities autonomously. Specifically, electrification by wind power is one of the technological options that have been used recently in projects implemented in the Andean highlands of Peru. To date, these projects have tended to install individual microwind turbines at each demand point. Alternatively, we propose a solution that considers both individual generators and microgrids. We develop a mathematical model that gives the location and size or type of the wind turbines and the design of the microgrids, taking into account the demand of the consumption points and the wind potential. The criterion is the minimization of the initial investment cost required to meet the demand. The model is validated by application to a real case in the northern highlands of Peru. Results show that microgrids are used despite the village dispersion, and the solutions significantly reduce the initial investment costs.     

Chaos control on autonomous and non-autonomous systems with various types of genetic algorithm-optimized weak perturbations

Recently, we proposed a chaos control strategy with weak Fourier signals optimized by using a genetic algorithm (GA) and demonstrated its merits in controlling Lorenz and Rössler systems (Physical Review E, 2004). In this continuation work, performance of various types of signals, namely periodic continuous, periodic discrete, and constant bias (non-periodic), applied to an autonomous (Rössler) system and a non-autonomous (Murali–Lakshmanan–Chua, MLC) system are investigated. An index of relative robustness is proposed for measuring the noise-resisting ability of the control signals. The results reveal that the constant signal has the strongest noise-resisting ability, the periodic pulse signal has the weakest, and the Fourier signal falls in between. Phase modulation generally shortens the transient time period and is additionally beneficial to non-autonomous systems in minimizing significantly the signal power. By searching with the present GA-optimization, it is demonstrated that the minimum-power signal for controlling the non-autonomous (MLC) system is the signal with a frequency exactly the same as that of the system forcing but with phase modulation. The effectiveness of the GA-optimized signals of extremely low power employed in alternatively switching control of non-autonomous systems is also demonstrated.     

On Intrinsic Complexity of Nash Equilibrium Problems and Bilevel Optimization

In this article we study generalized Nash equilibrium problems (GNEP) and bilevel optimization side by side. This perspective comes from the crucial fact that both problems heavily depend on parametric issues. Observing the intrinsic complexity of GNEP and bilevel optimization, we emphasize that it originates from unavoidable degeneracies occurring in parametric optimization. Under intrinsic complexity, we understand the involved geometrical complexity of Nash equilibria and bilevel feasible sets, such as the appearance of kinks and boundary points, non-closedness, discontinuity and bifurcation effects. The main goal is to illustrate the complexity of those problems originating from parametric optimization and singularity theory. By taking the study of singularities in parametric optimization into account, the structural analysis of Nash equilibria and bilevel feasible sets is performed. For GNEPs, the number of players’ common constraints becomes crucial. In fact, for GNEPs without common constraints and for classical NEPs we show that—generically—all Nash equilibria are jointly nondegenerate Karush–Kuhn–Tucker points. Consequently, they are isolated. However, in presence of common constraints Nash equilibria will constitute a higher dimensional set. In bilevel optimization, we describe the global structure of the bilevel feasible set in case of a one-dimensional leader’s variable. We point out that the typical discontinuities of the leader’s objective function will be caused by follower’s singularities. The latter phenomenon occurs independently of the viewpoint of the optimistic or pessimistic approach. In case of higher dimensions, optimistic and pessimistic approaches are discussed with respect to possible bifurcation of the follower’s solutions.