Designing an adaptive type-2 fuzzy logic system load frequency control for a nonlinear time-delay power system

In this paper, a combination of type-2 fuzzy logic system (T2FLS) and a conventional feedback controller (CFC) has been designed for the load frequency control (LFC) of a nonlinear time-delay power system. In this approach, the T2FLS controller which is designed to overcome the uncertainties and nonlinearites of the controlled system is in the feedforward path and the CFC which plays an important role in the transient state is in the feedback path. A Lyapunov–Krasovskii functional has been used to ensure the stability of the system and the parameter adjustment laws for the T2FLS controller are derived using this functional. In this training method, the effect of delay has been considered in tuning the T2FLS controller parameters and thus the performance of the system has been improved. The T2FLS controller is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. To illustrate the effectiveness of the proposed method, a two-area nonlinear time-delay power system has been used and compared with the controller that uses the gradient-descend (GD) algorithm to tune the T2FLS controller parameters.     

Application of type-2 fuzzy logic system for load frequency control using feedback error learning approaches

In this paper, the type-2 fuzzy logic system (T2FLS) controller using the feedback error learning (FEL) strategy has been proposed for load frequency control (LFC) in the restructure power system. The original FEL strategy consists of an intelligent feedforward controller (INFC) (i.e. artificial neural network (ANN)) and the conventional feedback controller (CFC). The CFC acting as a general feedback controller to guarantee the stability of the system plays a crucial role in the transient state. The INFC is adopted in forward path to take over the control problem in the steady state. In this work, to improve the performance of the FEL strategy, the T2FLS is adopted instead of ANN in the INFC part due to its ability to model uncertainties, which may exist in the rules and measured data of sensors more effectively. The proposed FEL controller has been compared with a type-1 fuzzy logic system (T1FLS) – based FEL controller and the proportional, integral and derivative (PID) controller to highlight the effectiveness of the proposed method.     

Decentralized load frequency control for two-area interconnected power system

This paper proposes a decentralized output feedback control scheme applied to two-area interconnected power system. The controller synthesis problem is formulated as the scaled H∞control problem and a new LMI-based algorithm is proposed to compute the decentralized controller. The proposed controller provides robustness with regard to parametric uncertainties and also attenuates bounded exogenous disturbances in the sense of L2-gain. Simulation results clearly show the effectiveness of developed decentralized output feedback control scheme.     

Non-linear sliding mode load frequency control in multi-area power system

This paper addresses non-linear sliding mode controller (SMC) with matched and unmatched uncertainties for load frequency control (LFC) application in three-area interconnected power system. In conventional LFC scheme, as the nominal operating point varies due to system uncertainties, frequency deviations cannot be minimized. These lead to degradation in the dynamic performance or even system instability. In this paper, an effective control law is proposed against matched and unmatched uncertainties.. The proposed controller has ability to vary closed-loop system damping characteristics according to uncertainties and load disturbances present in the system. The frequency deviation converges to zero with minimum undershoot/overshoot, fast settling time, significantly reduced chattering and ensures asymptotic stability. In addition, the controller is robust in the presence of parameter uncertainties and different disturbance patterns. It also guarantees high dynamic performance in the presence of governor dead band (GDB) and generation rate constraint (GRC). Simulations are performed to compare the proposed controller with linear SMC. Using proposed control strategy, undershoot/overshoot and settling time gets reduced by approximately 30% with respect to linear SMC. The computed performance indices and qualitative results establish the superiority as well as applicability of the proposed design for the LFC problem. Further, the proposed controller scheme is validated on IEEE 39 bus large power system.     

Robust adaptive fuzzy logic power system stabilizer

This paper introduces a robust adaptive fuzzy controller as a power system stabilizer (RFPSS) used to damp inter-area modes of oscillation following disturbances in power systems. In contrast to the IEEE standard multi-band power system stabilizer (MB-PSS), robust adaptive fuzzy-based stabilizers are more efficient because they cope with oscillations at different operating points. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, components that ensure robust and adaptive performance are included in the control law to compensate for modelling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearities. The second system is an adaptive one that compensates for modelling errors. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.     

基于滑模控制的含风储多域电力系统负荷频率控制

建立含风储多域互联电力系统负荷频率控制(LFC)模型,同时考虑系统参数不确定性、储能系统和传统机组控制信道延时问题.为提高系统鲁棒性,降低储能系统的容量配置,针对含风储的LFC模型,设计滑模负荷频率控制器,并提出滑模负荷频率控制器和储能协调的控制策略.算例分析表明,所提出的协调控制策略在新能源大规模渗透和系统负荷波动情况下能够有效减小系统频率偏差和区域控制偏差,同时降低储能系统的配置容量,提高电力系统安全稳定运行的经济性.     

预估模糊逻辑控制在电厂水处理中的应用

在电厂水处理加药控制系统中,由于被控对象具有纯滞后、大惯性、难以建立数学模型等特点,常规的PID控制难以满足加药系统的控制要求。着重介绍了水处理加药系统的估模糊逻辑控制方案及其设计方法.并进行了仿真分析,表明采用预估模糊逻辑算法对加药系统控制能够得到满意的效果。     

基于模糊逻辑的弹性飞机机动载荷主动控制

为减小多操纵面无尾飞翼飞机的机动载荷,建立了机动载荷控制问题的多变量优化问题数学模型,并采用分布估计算法进行求解,得到不同马赫数条件下各操纵面的控制分配系数.在此基础上,运用T-S模糊理论对控制分配系数进行模糊合成,得到了模糊控制分配系数,从而无需重新设计即可实现全包线范围内的鲁棒机动载荷控制.仿真实验结果表明,采用基于模糊逻辑的机动载荷控制方案可使机动飞行时翼根弯矩增量的峰值减小率高达80％以上.     

Bio-inspired fuzzy logic based tuning of power system stabilizer

In this paper, bacteria foraging optimization (BFO) – a bio-inspired technique, is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. For on-line, off-nominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.     

无线传感器网络的模糊功率控制技术

无线传感器网络节点通过撒播造成节点分布不均,分簇后导致分簇疏密不均,影响了网络性能,需要对簇头进行功率控制使网络结构更加优化.但这是一个难以确定其准确数学模型的过程,因此提出了一种采用模糊技术调整簇头功率以改变拓扑结构的方案.最后进行了仿真,实验结果证明通过分簇大小控制以后,网络的生存时间和通信量都有增加.     

含混合储能的互联电力系统传感器容错负荷频率控制

为了改善多区域互联系统电能质量的问题,在原有负荷频率控制系统(LFC)的基础上引入由超级电容和蓄电池两者组成的混合储能系统(HESS),并针对电力系统中难以避免的传感器故障问题设计传感器主动容错策略.首先,建立含HESS的LFC系统模型用以减少负荷扰动对系统频率带来的影响;其次,针对带HESS的互联电力系统设计传感器主...     

基于模糊逻辑系统的输出跟踪控制问题

针对一类未知的非线性互联大系统，设计间接自适应模糊控制器以实现跟踪控制，采用模糊控制，模糊逻辑逼近和模糊滑模控制相结合的方法，对维数较低的子系统未知动态和维数较高的互联项未知动态分别采用两类模糊规则进行逼近，对系统的外部干扰及模糊逼近误差采用模糊滑模控制予以抵消，基于Lyapunov方法实现模糊系统中的参数自适应律并在线调节，所设计的间接自适应控制器使系统在Lyapunov意义下稳定，且跟踪误差趋近于0，仿真结果表明了该设计方法的正确性。     

Three-dimensional fuzzy logic system for process modeling and control

The traditional fuzzy logic system (FLS) can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.     

仿手工雕刻的非线性模糊控制器的设计

本文介绍了一种自学习非线性模糊控制器的设计，以及它在雕刻加工中的具体应用。     

A probabilistic fuzzy logic system for modeling and control

In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.     

Maiden application of colliding bodies optimizer for load frequency control of two-area nonreheated thermal and hydrothermal power systems

This article presents a novel load frequency control (LFC) approach using colliding bodies optimizer (CBO) for frequency stabilization of interlinked multiarea electric power systems. The optimal parameters of the suggested CBO-based proportional–integral–derivative-filter controller ascertain an effective LFC solution. First, a well-known and widely used linearized two-area nonreheated thermal power system is examined to demonstrate the efficacy of the proposed approach. The effectiveness of the proposed method is analyzed by comparing the outcomes of several recently presented LFC schemes. The performance analysis shows a settling time improvement of 3.10%, 14.29%, and 18.66% in the case of area-1 and area-2 frequencies and tie-line power deviations compared with an imperialist competitive algorithm (ICA)-based controller. The robustness of the proposed scheme is also evaluated in the presence of various operating scenarios. Additionally, the work is extended to a two-area nonreheated hydrothermal power system. The proposed method shows an improvement of over 60% in the performance index compared with several existing techniques-based controllers such as optics-inspired optimization, gray-wolf optimization, quasi-oppositional differential search algorithm, bacterial foraging optimization algorithm, and ICA.     

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.     

On the power of fuzzy logic

In this article we address the issues brought up by Elkan in his article, “The paradoxical success of fuzzy logic,” [IEEE Expert, 3–8 (1994)]. Elkan's work has caused concern since it purportedly reveals a Fuzzy Logic weakness regarding its theoretical foundations. A further investigation of Elkan's theorem (“Theorem 1”) revealed that its conclusion is not correct. After indicating the points where we disagree with Elkan, we reformulate Theorem 1, calling this new version “Theorem 2.” Theorems 1 and 2 have the same hypotheses but different conclusions. According to Theorem 2 there is a region of points that do hold the equivalence in the hypotheses of Theorem 1. In other words, one does not need to change the definition of logical equivalence in Theorem 1 in order to prove that Fuzzy Logic does not collapse to a two-valued logic. In a further analysis of Theorem 2 we show that Elkan's work does not affect the power of Fuzzy Logic to model vagueness. © 1996 John Wiley & Sons, Inc.     

风电介入下的多区域互联电力系统分布式经济模型预测负荷频率控制

针对风电介入下的多区域互联电力系统,提出一种分布式经济模型预测负荷频率控制策略.通过将大规模互联电力系统分解成若干个动态耦合的子系统,这些子系统能够利用网络交流并共享信息,使得各区域的控制器实现各自优化问题的求解.同时,在满足状态约束和控制输入约束的前提下,遵循传统火力发电优先、风力发电配合的原则,通过在线求解优化问题,实现风电介入下的多区域互联电力系统的负荷频率控制.为了提高系统整体运行经济性,所提出的分布式经济模型预测控制器将负荷调频成本、燃料消耗成本以及风力发电成本等经济性指标考虑在内.仿真结果表明,在阶跃负荷扰动下,所设计的控制器不仅可以满足调频要求,在降低计算负担和提高经济性能方面也具有一定优势.     

Robustness of fuzzy logic control for an uncertain dynamic system

Based on the similarity between prevalent fuzzy logic controllers (FLC) and the conventional robust controller, i.e., the variable structure controller, control theoretic analysis of a fuzzy control system is presented in the sense of Lyapunov. As well as the robustness of the fuzzy control system against uncertainties of a controlled process, this analysis gives an account of the relationship between control performance and the design parameters of the FLC, which has been obscure in the theory of fuzzy control