Load frequency control and automatic generation control using fractional-order controllers

Recently, fractional calculus has received extensive attention and research. Accordingly, there is an increasing interest in fractional-order (FO) dynamic systems and controllers. The widely used classical integer-order proportional-integral controller and proportional-integral-derivative controller are usually adopted in the load frequency control (LFC) and automatic generation control (AGC) to improve the dynamic response and to eliminate or reduce steady-state errors. This paper utilizes the FO controllers to improve stability and response of LFC and AGC system. The paper uses the integral of the time-weighted absolute error performance index for optimal controller design. The paper investigates LFC and AGC for both isolated and interconnected power systems and shows that FO controllers perform better than classical integer-order controllers in theses systems.     

On the Use of Fractional-Order Controllers for Performance Improvements in AC Drives

Abstract—The control performance of AC electric drives is sensitive to and often degrades with parametric variations. This article proposes the use of fractional-order controllers to combat parameter variations. The speed control of an AC induction motor drive achieved through indirect field-oriented control is considered for illustration. The performance of the proposed controller is compared with a well-tuned proportional-integral controller when critical motor parameters are subject to parametric variations about their nominal values. Dynamic simulations in MATLAB/Simulink (The Math Works, Natick, Massachusetts, USA) are conducted to assess control performance.     

A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor

Abstract—The proportional-integral-derivative controllers were the most popular controllers of this century because of their remarkable effectiveness, and simplicity of implementation. However, proportional-integral-derivative controllers are usually poorly tuned in practice. This article presents a hybrid particle swarm optimization and bacterial foraging techniques for determining the optimal parameters of a proportional-integral-derivative controller for speed control of a permanent magnet brushless DC motor. The first part of the article deals with the system modeling and its verification where a model of modest accuracy cannot be expected to give a fair comparison of different controllers. The remaining parts of the article present the application of different optimization techniques to tune the proportional-integral-derivative controller as applied to the motor model. The particle swarm optimization, bacterial foraging, and bacterial foraging-particle swarm optimization algorithms are implemented in MATLAB while the GA Toolbox is used. The performance of the tuned controllers is simulated and experimentally verified to evaluate the main characteristics of each one. It is found that the proposed hybrid bacterial foraging-particle swarm optimization technique is more efficient in improving the step response characteristics and achieving the desired performance indices.     

Hybrid Control Robust Using Logic Fuzzy Applied to the Position Loop for Vector Control to Induction Motors

This article presents the study and implementation of position control for an induction motor drive using the vector control strategy to improve tracking of the motor shaft. Thus, a novel hybrid control technique is proposed in contrast to the proportional-integral-derivative controller with a fixed gain traditionally used in this type of application. The hybrid control relies on error techniques based on fuzzy rules systems weighting the control action of proportional-integral-derivative and generalized predictive controllers previously adjusted. The simulation and experimental results to check the performance and robustness of the control strategy demonstrate the merit of the proposed approach.     

Frequency Stabilization for Multi-area Thermal–Hydro Power System Using Genetic Algorithm-optimized Fuzzy Logic Controller in Deregulated Environment

Abstract—This article develops a model of load frequency control for an interconnected two-area thermal–hydro power system under a deregulated environment. In this article, a fuzzy logic controller is optimized by a genetic algorithm in two steps. The first step of fuzzy logic controller optimization is for variable range optimization, and the second step is for the optimization of scaling and gain parameters. Further, the genetic algorithm-optimized fuzzy logic controller is compared against a conventional proportional-integral-derivative controller and a simple fuzzy logic controller. The proposed genetic algorithm-optimized fuzzy logic controller shows better dynamic response following a step-load change with combination of poolco and bilateral contracts in a deregulated environment. In this article, the effect of the governor dead band is also considered. In addition, performance of genetic algorithm-optimized fuzzy logic controller also has been examined for various step-load changes in different distribution unit demands and compared with the proportional-integral-derivative controller and simple fuzzy logic controller.     

Frequency Regulation in Hybrid Power System Using Iterative Proportional-Integral-Derivative H ∞ Controller

This study considers frequency regulation in a hybrid power system consisting of conventional and distributed generation resources. The performance of two controllers—an H _∞ design via linear matrix inequalities and an iterative proportional-integral-derivative H _∞ via linear matrix inequalities—is assessed to maintain frequency deviation profile in acceptable limits. In the latter control design, the iterative linear matrix inequality approach is used to tune proportional-integral-derivative controller parameters subjected to H_∞ constraints in terms of the iterative linear matrix inequality. The efficacy of the control law and disturbance accommodation properties is shown. The robustness of these controllers is demonstrated in the hybrid power system with different load disturbance conditions, wind power, and parameter variations. Controller performance is compared with a suboptimal controller to demonstrate its superiority. It is found that the second controller design has satisfactory disturbance rejection properties and robustness against parameter variations over a wide range of conditions.     

Application of Firefly Algorithm for Load Frequency Control of Multi-area Interconnected Power System

Abstract—In this article, a firefly algorithm is proposed for load frequency control of multi-area power systems. Initially a two equal area non-reheat thermal system is considered and the optimum gains of the proportional integral/proportional integral derivative controller are optimized employing the firefly algorithm technique. The superiority of the proposed approach is demonstrated by comparing the results with some recently published techniques such as genetic algorithm, bacteria foraging optimization algorithm, differential evolution, particle swarm optimization, hybrid bacteria foraging optimization algorithm-particle swarm optimization, and Ziegler–Nichols-based controllers for the same interconnected power system. Further, the proposed approach is extended to a three-unequal-area thermal system considering generation rate constraint and governor dead-band. Investigations reveal on comparison that proportional integral derivative controller provides much better response compared to integral and proportional integral controllers. Additionally, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, and inertia constant in the range of +50 to –50% from their nominal values as well as the size and position of step load perturbation to demonstrate the robustness of the proposed firefly algorithm optimized proportional integral derivative controller.     

An Evaluation of Different Controllers Based on the Network Frequency Maintenance Strategy for a Large and Multi-control-area Interconnected Power System

Abstract—Maintaining the network frequency at its nominal value due to the load changes is one of the most important issues in the control and operation schedule of a large and multi-area interconnected power system. For the effective control solution, two types of tie-line bias control strategy-based controllers have been applied, i.e., classical and improved controllers. The classical controllers using conventional regulators, including integral, proportional–integral and proportional-integral-derivative, have achieved initial control results to bring the steady state back to the network. However, due to the very poor control features (e.g., large overshoots and long settling times), they need to be replaced with improved controllers, such as fuzzy logic and artificial neural network. To obtain an entire evaluation of the application of different load-frequency controllers, a five-control-area interconnected power system was built as a typical case study. Three improved controllers using proportional-plus-integral-based fuzzy logic and artificial neural network-based non-linear autoregressive moving average (NARMA-L2) architectures as well as their hybrid combination will also be investigated in this study. Simulation results obtained reveal that the improved controllers have obtained smaller overshoots (from 13.95 to 84.18%) and shorter settling times (from 19.91 to 65.71%) compared with the classical controllers.     

Experimental verification of a hybrid fuzzy control strategy for ahigh-performance brushless DC drive system

This paper describes the design and experimental verification of a hybrid fuzzy control system for a high-performance brushless DC motor drive. Both the design of the fuzzy controller and its integration with the proportional-integral (PI) in a global control system are discussed. The principle, of the proposed control system is to use a PI controller, which performs satisfactorily in most cases, while keeping in the background, a fuzzy controller, which is ready to take over the PI controller when severe perturbations occur. Strategies of the switching control for the hybrid controller is such that the fuzzy controller is activated whenever the resident system exhibits an oscillatory and/or overshoot behavior. Thus, the PI and fuzzy controllers can be managed to take advantage of their positive attributes. Performance of the hybrid fuzzy-PI controller is evaluated through a laboratory implementation. The laboratory implementation is based on a linguistic fuzzy controller whose design is derived from the expert knowledge gained during disturbances. Experimental results have shown excellent tracking performance of the proposed control system, and have convincingly demonstrated the usefulness of the hybrid fuzzy controller in high-performance drives with uncertainties     

基于模糊分数阶PID的含电动汽车的多能源微电网二次频率控制

为了解决分布式可再生能源间歇性发电和电动汽车接入微电网导致的微电网频率波动问题,针对含电动汽车接入的多能源微电网系统,设计了一种基于模糊自适应理论的分数阶比例-积分-微分(PID)二次频率控制器,通过建立模糊控制规则,结合频率偏差对模糊分数阶PID控制器参数进行实时在线整定.考虑光伏发电和风力发电输出功率波动、电动汽车接入微电网、随机负荷扰动3种不同场景,对所提出的模糊分数阶PID控制方法进行了仿真验证与量化分析.仿真结果表明,相比于传统PID控制器和分数阶PID控制器,所设计的模糊分数阶PID控制器使得系统频率响应振荡减少、超调量明显降低、动态调节时间更短,呈现出更强的抗扰动能力和鲁棒性,对于多能源微电网的二次频率调节具有优良的控制效果.     

多馈入交直流输电系统的模糊控制器协调优化算法

设计了一套阻尼区域间功率振荡的模糊控制器。在多馈入交直流输电系统的直流功率控制系统和发电机励磁系统中同时采用了该模糊控制器,并对影响其性能的关键参数进行了协调优化。为了解决优化结果容易限于局部最优的问题,采用了遗传算法进行全局并行寻优,同时引入序优化理论在概率意义上保证优化解的质量。仿真结果表明:与常规阻尼控制器相比,模糊控制器能更好地提高交直流互联系统的动态稳定性且具有鲁棒性。序优化遗传算法比传统遗传算法具有更稳定的性能,可作为多馈入交直流输电系统的模糊控制器参数协调优化的一种有效方法。     

Cascade controller based modeling of a four area thermal: gas AGC system with dependency of wind turbine generator and PEVs under restructured environment

This paper investigates automatic generation control (AGC) of a realistic hybrid four-control area system with a distinct arrangement of thermal units, gas units and additional power generation. A proportional-integral-double derivative cascaded with proportional-integral (PIDD-PI) controller is employed as secondary controller in each control area for robust restructured AGC considering bilateral transactions and contract violations. The Harris Hawks algorithm is used to determine the optimal controller gains and system parameters under several scenarios. Electric vehicle (EV) aggregators are employed in each area to participate fully along with thermal and gas units to compensate for the unscheduled system demand in the local area. A comparison of non-cascaded controllers such as PI-PD, PD-PID and the proposed PIDD-PI proves the superiority of the last. The effect of the decline in inertia is closely examined because of the sudden outage of a generating unit while at the same time considering the change in area frequency response characteristics and area control error. EV fleets make significant contributions to improving the system dynamics during system inertia loss. The use of EVs in the presence of a wind energy-supported grid can provide a stable efficacy to the power grid. Numerous simulations with higher load demands, stochastic communication delays in presence of the WTG plant, and violations in system loadings and changes in gas turbine time constants in the absence of WTG demonstrate the robustness of the proposed control approach.     

Artificial intelligence-based speed control of DTC induction motor drives—A comparative study

The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability.     

Integrated PLC-fuzzy PID Simulink implemented AVR system

Improving the transient response of power generation systems using automation control in a precise manner is the key issue. We design a fuzzy proportional integral derivative (PID) controller using Matlab and programmable logic controllers (PLCs) for a set point voltage control problem in the automatic voltage regulator (AVR) system. The controller objective is to maintain the terminal voltage all the time under any loads and operational conditions by attaining to the desired range via the regulation of the generator exciter voltage. The main voltage control system uses PLCs to implement the AVR action. The proposed fuzzy controller combines the genetic algorithm (GA), radial-basis function network (RBF-NN) identification and fuzzy logic control to determine the optimal PID controller parameters in AVR system. The RBF tuning for various operating conditions is further employed to develop the rule base of the Sugeno fuzzy system. The fuzzy PID controller (GNFPID) is further designed to transfer in PLCs (STEP 75.5) for implementing the AVR system with improved system response. An inherent interaction between two generator terminal voltage control and excitation current is revealed. The GNFPID controller configures the control signal based on interaction and there by reduces the voltage error and the oscillation in the terminal voltage control process. We achieve an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator for improve the transient response.     

改善交直流混合系统阻尼特性的HVDC模糊控制

设计了一个用于改善交直流混合系统阻尼特性的HVDC模糊控制器，该模糊控制器既能根据运行条件修改增益，提高其控制性能；同时该模糊控制器的附加控制量ΔU提高交流系统的稳定性，增强系统的阻尼，抑制系统的振荡。分别用传统控制与模糊控制方法对三机交直流系统的模型用NETOMAC软件进行了仿真，结果显示：在系统受到大干扰时，模糊控制器较传统的P-I控制器能更好地抑制交直流混合系统中交流系统的振荡、增强系统的阻尼，提高交流系统的稳定性。     

Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using Flexible Alternating Current Transmission System Devices

This article describes the load frequency control of a multi-area system. Each control area contains both a hydro and thermal power plant to form a multi-source multi-area hydro thermal system. The secondary proportional-integral controller has been tuned using Ziegler–Nichols, genetic algorithm, and fuzzy gain scheduling methods. On comparing the controller performance based on various performance indices, it is found that a fuzzy gain scheduling tuned proportional-integral controller is suitable for a multi-source multi-area hydro thermal system. Further improvement on the load frequency dynamics has been achieved by connecting superconducting magnetic energy storage unit in each control area and a static synchronous series compensator unit on a tie-line.     

Coordinated Controller Design of Grid-connected Variable-speed Wind Energy Conversion System with Model-based Predictive Control Using Response Surface Methodology

This paper presents an optimum design procedure for the coordinated tuning of machine side converter (MSC) and grid side converter (GSC) controllers of grid connected permanent magnet synchronous generator (PMSG). Model based predictive controller (MBPC) is used to control the MSC. MBPC based speed control design consists of two steps. A linearized state space model is employed to predict the future output (rotor speed). An optimal control law is derived by minimizing a quadratic performance index that considers the control effort and the difference between the predicted rotor speed and the reference rotor speed. A proportional-integral (PI) controller is used to control the GSC. The MSC and GSC controller parameters are determined by simultaneously optimizing the controller performance indices. The coordinated controller design is carried out in two steps. The analytical expression that relates the performance indices and the controller parameters is arrived using response surface methodology (RSM). The determination of controller parameters is posed as a constrained multi-objective optimization problem and solved employing NSGA-II (non-dominated sorted genetic algorithm II). The proposed methodology is tested on a sample power system with PMSG based WECS (Wind Energy Conversion System). Simulation results demonstrate the effectiveness of the proposed methodology.     

Field-programmable Gate Array Based Fuzzy Sliding-mode Controller for a Permanent Magnet Synchronous Motor Drive

Abstract—In this article, a field-programmable gate array based fuzzy sliding-mode controller is proposed to control a permanent magnet synchronous motor drive. First, the dynamics of a permanent magnet synchronous motor is derived, and the vector control scheme is introduced in the current loop. Next, to improve the performance of the permanent magnet synchronous motor drive, a fuzzy sliding-mode controller with an integral-operation switching surface is proposed and applied to the speed loop, in which a fuzzy inference mechanism is adopted to generate the reaching control signal. Further, an integrated hardware design method is developed to implement the field-oriented vector current controller and the proposed fuzzy sliding-mode speed controller on a single field programmable gate array chip. Finally, a prototyping platform based on an Altera field-programmable gate array is established to evaluate the ability of the proposed fully integrated solution in terms of control quality and time/area performances. The preferable performance of the proposed field-programmable gate array-based fuzzy sliding-mode control approach for permanent magnet synchronous motor drive is verified by the experimental results compared with the conventional proportional-integral control and sliding mode control schemes.     

电动汽车双向DC-DC变换器分数阶PIλ控制

针对电动汽车复合储能系统中两相交错双向DC-DC变换电路,基于分数阶微积分理论,在电能流动的两个方向中,均采用超级电容电流内环、输入电压外环的双闭环控制策略,为每环设计分数阶PI^λ控制器。通过建立双向变换电路不同工作模式下的小信号数学模型,依据穿越频率和转折频率特性对各分数阶控制器的三个参数进行整定。通过Matlab/Simulink分别搭建整数阶控制和分数阶控制下的电路仿真模型,对比分析变换器在不同控制作用下的稳态性能和动态性能,结果表明:分数阶控制时,变换器输出电压可更快到达稳态值,在电动汽车负载变化迅速的情况下,更具优越性。     

Comparative evaluation of sliding mode fuzzy controller and PID controller for a boost converter

Nonlinear controllers such as fuzzy controllers and sliding mode controllers have been applied to boost converters because of their nonlinear properties. Although both fuzzy and sliding mode controllers have desirable characteristics, they have disadvantages in practice when applied individually. A sliding mode fuzzy controller is proposed to control boost converters. The sliding mode fuzzy controller combines the advantages of both fuzzy controllers and sliding mode controllers. It also has advantages of its own that are well suited for digital control design and implementation. A sliding mode fuzzy controller is designed and verified with experimental results using a prototype boost converter with a DSP-based digital controller. Experimental results of the boost converter using sliding mode fuzzy control are evaluated in comparison with experimental results using a linear PID and PI controller. The comparison indicates that the sliding mode fuzzy controller is able to obtain the desired transient response under varying operating points without chattering. The startup response using sliding mode fuzzy control is superior to the response using PID and PI control, while the load transient response shows no obvious advantage.