BFOA based design of PID controller for two area Load Frequency Control with nonlinearities

This paper presents an application of the novel artificial intelligent search technique to find the parameters optimization of nonlinear Load Frequency Controller (LFC) considering Proportional Integral Derivative controller (PID) for a power system. A two area non reheat thermal system is considered to be equipped with PID controller. Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controller parameters to minimize the time domain objective function. The performance of the proposed technique has been evaluated with the performance of the conventional Ziegler Nichols (ZN) and Genetic Algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PID controller. By comparison with the conventional technique and GA, the effectiveness of the proposed BFOA is validated over different operating conditions, and system parameters variations.     

A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm

The primary aim of the Automatic Generation Control (AGC) is to maintain system frequency and tie-line interchanges in a predestine limits by regulating the power generation of electrical generators, in case of fluctuations in the system frequency and tie-line loadings. This paper proposes a new online intelligent strategy to realize the control of multi-area load frequency systems. The proposed intelligent strategy is based on a combination of a novel heuristic algorithm named Self-Adaptive Modified Bat Algorithm (SAMBA) and the Fuzzy Logic (FL) which is used to optimally tune parameters of Proportional–Integral (PI) controllers which are the most popular methods in this context. The proposed controller guaranties stability and robustness against uncertainties caused by external disturbances and impermanent dynamics that power systems face. To achieve an optimal performance, the SAMBA simultaneously optimizes the parameters of the proposed controller as well as the input and output membership functions. The control design methodology is applied on four-area interconnected power system, which represents a large-scale power system. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of Proportional Integral Derivative (PID) controller and Optimal Fuzzy PID (OFPID) controller, which are the most recent researches applied to the present problem. Simulation results demonstrate the successfulness and effectiveness of the Online-SAMBA Fuzzy PI (MBFPI) controller and its superiority over conventional approaches.     

A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (hFA–PS) technique is proposed for Automatic Generation Control (AGC) of multi-area power systems with the consideration of Generation Rate Constraint (GRC). Initially a two area non-reheat thermal system with Proportional Integral Derivative (PID) controller is considered and the parameters of PID controllers are optimized by Firefly Algorithm (FA) employing an Integral Time multiply Absolute Error (ITAE) objective function. Pattern Search (PS) is then employed to fine tune the best solution provided by FA. The superiority of the proposed hFA–PS based PID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI/PID controllers for the same interconnected power system. Furthermore, sensitivity analysis is performed to show the robustness of the optimized controller parameters by varying the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to multi area multi source hydro thermal power system with/without considering the effect of physical constraints such as time delay, reheat turbine, GRC, and Governor Dead Band (GDB) nonlinearity. The controller parameters of each area are optimized under normal and varied conditions using proposed hFA–PS technique. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.     

A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems

In this paper, a novel hybrid Particle Swarm Optimization (PSO) and Pattern Search (PS) optimized fuzzy PI controller is proposed for Automatic Generation Control (AGC) of multi area power systems. Initially a two area non-reheat thermal system is used and the gains of the fuzzy PI controller are optimized employing a hybrid PSO and PS (hPSO-PS) optimization technique. The superiority of the proposed fuzzy PI controller has been shown by comparing the results with Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), conventional Ziegler Nichols (ZN), Differential Evolution (DE) and hybrid BFOA and PSO based PI controllers for the same interconnected power system. Additionally, the proposed approach is further extended to multi source multi area hydro thermal power system with/without HVDC link. The superiority of the proposed approach is shown by comparing the results with some recently published approaches such as ZN tuned PI, Variable Structure System (VSS) based ZN tuned PI, GA tuned PI, VSS based GA tuned PI, Fuzzy Gain Scheduling (FGS) and VSS based FGS for the identical power systems. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters, size and position of step load perturbation The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of hybrid BFO-PSO and craziness based PSO approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different controllers in each area and the results are compared with hybrid BFO-PSO and ANFIS approaches.     

基于Fuzzy-PID控制的雷达伺服系统研究

针对传统PID控制方法存在的参数控制难度大、动态性能差等缺陷,提出基于Fuzzy PID控制器的雷达伺服系统。根据需求设计了以模糊控制器为核心的Fuzzy PID控制器,将传统PID控制与模糊控制进行有效结合。通过MATLAB软件对基于Fuzzy PID控制器的雷达伺服系统进行仿真实验。与传统PID控制方法相比,Fuzzy PID控制显著提高了雷达伺服系统的快速性和平稳性,有效改善了伺服系统的动态性能。     

Simulated annealing based optimal frequency and terminal voltage control of multi source multi area system

The article proposes optimal secondary controller for combined Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) of multi source multi area system using simulated annealing technique. When subjected to load disturbance, frequency, tie-line power and voltage fluctuations results higher oscillations. Speed governor of the system helps to match generation with the demand. But, fine tuning of frequency, tie-line power and voltage when subjected to load disturbance in multi source multi area system is achieved by secondary Proportional Integral Derivative (PID) controller. As a conventional benchmark PID controller is tuned using Zeigler Nichol’s (ZN) method and further optimized using Simulated Annealing (SA) technique. The performance of the system is validated and judged using performance indices.     

Optimal design of a robust discrete parallel FP + FI + FD controller for the Automatic Voltage Regulator system

The purpose of this paper is to design a good tracking controller for the generator Automatic Voltage Regulator (AVR) system. A fuzzy logic-based controller that is called Fuzzy P + Fuzzy I + Fuzzy D (FP + FI + FD) controller has been designed optimally and applied to AVR system. In the proposed method, optimal tuning of controller parameters is very important to achieve the desired level of robust performance. Thus, a hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (HGAPSO) technique has been used to find a better fuzzy system control. The motivation for using this hybrid method is to increase disturbance rejection effort, reduce fuzzy system efforts and take large parametric uncertainties into account. The developed FP + FI + FD control strategy leads to a flexible controller with simple structure that is easy to implement. The simulation results have been compared with the conventional Proportional–Integral–Derivative (PID) and fuzzy PID controllers. Three cases of simulation have been performed, case 1: comparing the tracking capability of the controllers, case 2: comparing the disturbance rejection capability of the controller and case 3: evaluating the performance of the controllers assuming that amplifier and exciter system parameters have 50% uncertainty. The simulation results shows that the proposed parallel FP + FI + FD controller has good performance from the perspective of overshoot/undershoot, settling time, and rise time in comparison with both conventional and fuzzy PID controllers.     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller

This article presents automatic generation control (AGC) of an interconnected multi area thermal system. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. A maiden attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more recent and powerful evolutionary computational technique Bat algorithm (BA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. Dynamic analysis has also been carried out for the controllers in presence of random load pattern, which reveals the superior performance of the PD–PID cascade controller. Sensitivity analysis reveals that the BA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters. The system dynamic performances are studied with 1% step load perturbation in Area1.     

自适应模糊PID控制的无刷直流电机及仿真

提出了利用自适应模糊PID控制器实现对永磁无刷直流电机调速系统进行设计的新方法。文中首先建立永磁无刷直流电机的数学模型，以此进行转速和电流双闭环调速系统控制；并通过调节PWM发生器的开关频率来减少转矩脉动。接着将模糊控制器和PID控制器通过自适应因子结合，在线自调整控制参数，进一步完善了PID控制器的性能，提高了系统的控制精度。并把MATLAB中的Fuzzy Toolbox和SIMULINK以及Power System Blockset有机结合起来，实现了该自适应模糊PID控制器的计算机仿真。结果表明，该方法有较高的精度。     

Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.     

ACO based speed control of SRM fed by photovoltaic system

This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.     

水轮发电机组GA模糊控制器研究

模糊控制器设计的困难之一就是确定量化因子和隶属度函数，它们对控制器性能具有重要影响。文中提出用遗传算法（GA）对水轮发电机组模糊控制器进行优化设计，描述了控制器结构，优化算法和动态仿真结果。仿真对比试验表明，经过GA优化的模糊控制器比经过MATLAB NCD工具箱优化的PID控制器具有更好的控制效果和更强的鲁棒性。     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

基于稳定性和电压精度协调控制的新型模糊励磁调节器

在保持比例积分微分(PID)励磁调节器优良电压调节特性的基础上,部分采取线性最优控制理论设计附加励磁调节通道,并通过一模糊控制器动态协调电压调节通道和附加调节通道的作用权重,设计了一种新型模糊励磁调节器。分析了电力系统各种典型运行状态及其对励磁调节的要求,总结了不同状态下电压和稳定的协调控制策略,以期对电压调节和增强阻尼进行动态协调。数值仿真结果表明,该新型励磁调节器在稳态时具有同PID一样高的电压调节精度,动态过程中则能明显提高系统阻尼特性,具有满意的控制效果,并对系统工况变化具有一定适应性。     

Sliding mode control with integral augmented sliding surface: design and experimental application to an electromechanical system

In this study, an integral augmented sliding mode control (SMC + I) has been proposed to improve control performance of systems. Stability of the closed-loop system is guaranteed in the sense of Lyapunov stability theorem. The effectiveness of the control solution is established by the stability analysis of the closed-loop system dynamics. The proposed controller is adopted to control speed of an electromechanical system. The experimental set-up reflects the emphasis on the practicability of the proposed sliding mode controller. The experimental results are presented and compared with the results obtained from conventional sliding mode control and Proportional + Integral + Derivative (PID) control. The experimental results verify that the proposed controller provides favorable tracking performance, faster and smoother speed regulation with regard to parameter variations and disturbances. The present study shows that the proposed controller, with its straightforward solution, is easily applicable to industrial problems and an alternative to conventional PID and sliding mode controllers.     

Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller

This paper presents the modified Fibonacci search based Maximum Power Point Tracking (MPPT) scheme for a Solar Photovoltaic Array (SPVA) under partial shaded conditions. Partial shaded SPV modules produce several local maximum power points, which makes the tracking of the global maximum power a difficult task. Most of conventional tracking methods fail to work properly under these nonuniform insolation conditions. The real Fibonacci search based MPPT fails to track the global peak (GP) under partial shaded conditions. This paper improves the method by considering power ripple and wide search range so that the proposed method tracks GP for all the conditions. It is checked for different shading patterns through simulation and verified experimentally. In this paper, the advantage of using Fuzzy Logic Controller (FLC) is also presented. Fuzzy rules are optimized using genetic algorithm (GA). Comparative studies have been made for Proportional plus Integral (PI), nonoptimized FLC and GA optimized FLC. From the simulation results, it is observed that the fuzzy controller reduces error and it gives rapid response to environmental changes. Furthermore, it does not require any tuning of the parameters, unlike conventional PI controller, wherein the controller gain parameters needs to be changed when solar insolation changes.     

Memory‐based IMC tuning of PID controllers for nonlinear systems

This paper describes a new memory‐based proportional, integral, derivative (PID) controller design. These PID parameters are tuned by the IMC‐PID method, which is derived from the relationship between the internal model control (IMC) and PID control. The IMC‐PID has a user‐specified parameter that greatly influences the control performance. The authors have already proposed a system identification scheme based on the memory‐based approach. In this paper, a new controller design scheme is discussed, whose system parameters and the corresponding suitable user‐specified parameter are simultaneously computed. Finally, the behavior of the newly proposed control scheme is examined in a simulation example. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于遗传算法的永磁同步直线电机PID控制研究

由于永磁同步直线电机系统运行过程中参数摄动和负载扰动等问题的存在,传统 PID控制器无法满足高精度伺服控制系统的要求。设计出一种基于遗传算法(GA)优化的 PID 控制器,并通过 Simulink 对永磁同步直线电机控制系统进行建模和仿真实验。仿真和实验表明,采用 GA优化的PID控制器与传统的 PID控制器在指定速度和负载扰动条件下相比,具有更好的动态稳定性和跟踪性能,能有效抑制参数摄动的影响并对负载扰动具有较强的鲁棒性,实验结果也证明了方案的有效性和可行性。     

基于遗传整定的永磁交流伺服系统模糊免疫PID控制器

提出了一种基于遗传整定的模糊免疫PID控制器,并将其应用于永磁交流伺服系统之中.该控制方法将生物免疫反馈机理与模糊控制相结合,由一个常规的PID控制器和一个免疫型比例控制器顺序串联实现,其中免疫比例控制器的非线性函数由模糊推理实现,PID及免疫比例控制器中的参数由遗传算法实现自寻优.利用功能强大的仿真工具Matlab/Simulink对交流伺服系统进行仿真,控制器算法简单,易于实现.结果表明,该控制方法具有响应速度快、鲁棒性强等优点,显著提高了系统性能.