TLBO optimized sliding mode controller for multi-area multi-source nonlinear interconnected AGC system

This paper represents design of output feedback sliding mode controller (SMC) for multi area multi-source interconnected power system. After designing output feedback SMC, teaching and learning based optimization (TLBO) technique is utilized to optimize feedback gain and switching vector of the controller. The superiority of the proposed approach is shown by comparing the result with output feedback tuned SMC with differential evolution and particle swarm optimization and state feedback SMC tuned with genetic algorithm for a two area thermal interconnected power system. Further, the proposed approach is extended to multi-area multi-source non linear automatic generation control (AGC) system with/without HVDC link. First area consists up thermal, hydro and gas; second area consists up thermal, hydro and nuclear as generating unit. Additionally, the superiority of proposed approach is shown by sensitivity analysis, which is carried out with wide changes in system parameters.     

基于事件驱动的含风电互联电网负荷频率鲁棒控制

风电输出功率具有强随机性和波动性的特点,因此风电高占比带来的输入电能波动给多区负荷频率控制带来更大挑战。计及风功率预测误差对负荷频率控制的影响,且考虑到未来开放式通信环境对区域控制误差信号传输的影响,文中提出了一种基于事件驱动通信下的鲁棒负荷频率控制策略,以保证含风电电力系统频率稳定性的同时尽可能地减少网络通信传输量。以典型含风电的两区域负荷频率控制为例进行了仿真研究,仿真结果表明,文中所提出的基于事件驱动通信的鲁棒控制器相对于常规比例—积分控制器而言,不仅能够有效保证风功率波动下的频率输出的l2增益性能,还能减少系统平稳态的通信次数,具有良好的频率控制性能。     

Robust decentralized load frequency control of interconnected power system with Generation Rate Constraint using Type-2 fuzzy approach

The Load Frequency Control (LFC) problem has been a major subject in electrical power system design/operation. LFC is becoming more significant recently with increasing size, changing structure and complexity in interconnected power systems. In practice LFC systems use simple Proportional Integral (PI) controllers. As the PI control parameters are usually tuned, based on classical approaches. Moreover, they have fixed gains; hence are incapable of obtaining good dynamic performance for a wide range of operating conditions and various load changes, in multi-area power system. Literature shows that fuzzy logic controller, one of the most useful approaches, for utilizing expert knowledge, is adaptive in nature and is applied successfully for power system stabilization control. This paper proposes a Type-2 (T2) fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The performance of the Type-2 (T2) controller is compared with conventional controller and Type-1 (T1) fuzzy controller with regard to Generation Rate Constraint (GRC). The system parametric uncertainties are verified by changing parameters by 40% simultaneously from their typical values.     

A new technique in hydro thermal interconnected automatic generation control system by using minority charge carrier inspired algorithm

This article deals with Automatic Generation Control (AGC) of a multi area interconnected hydro thermal system in different modes using intelligent integral and proportional-integral controllers and provides the comparative analysis of electrical and mechanical governors. Appropriate Generation Rate Constraints (GRC) has been considered for the hydro and thermal generation plants. These cumulated thermal areas are considered with reheat turbines. Performances of reheat turbine mechanical governor and hydro turbine electrical governor with its dynamic responses have been investigated. Further, selection of suitable integral and proportional-integral controllers has been investigated with a Minority Charge carrier Inspired Algorithm (MCI). Cumulative system performance is examined considering with different load perturbation in both cumulative thermal areas. Further, system is investigated with different frequency bias values and results are explored.     

Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system

This paper highlights the load frequency control using dual mode Bat algorithm based scheduling of PI controllers for interconnected power systems. The bat inspired algorithm based on the echolocation of bats has been developed in 2010. In this study, the bat inspired algorithm based dual mode PI controller is applied to the multi-area interconnected thermal power system in order to tune the parameter PI controllers. The proposed controller is simple in structure and easy for implementation. The proposed controller was compared with those from conventional the PI controllers and Fuzzy gain scheduling of PI controllers. The simulation results show the point that the proposed bat inspired algorithm, based dual mode gain scheduling of PI controllers (BIDPI), provides better transient as well as steady state of response. It is also found that the proposed controller is less sensitive to the changes in system parameters.     

Fractional-order Proportional-integral-derivative-based Automatic Generation Control in Deregulated Power Systems

Abstract—In this article, implementation of a fractional-order proportional-integral-derivative controller is proposed as supplementary automatic generation control of interconnected multi-area deregulated power systems that operates under the effects of bilateral contracts on the dynamics. The tuning of the fractional-order proportional-integral-derivative controller parameters is formulated as an optimization problem and solved by employing a genetic algorithm. The traditional automatic generation control loop incorporated with the concept of the distribution company participation matrix and fractional-order proportional-integral-derivative supplementary controller is simulated for different operating cases; a comparison among the conventional integral controller, hybrid fuzzy proportional-integral controller, genetic algorithm-tuned proportional-integral controller, and genetic algorithm-tuned proportional-integral-derivative controllers is presented. The simulation results show that the system employing a genetic algorithm-tuned fractional-order proportional-integral-derivative controller has better performance than the integer-order hybrid fuzzy proportional-integral, genetic algorithm-tuned proportional-integral, and genetic algorithm-tuned proportional-integral-derivative controllers in terms of settling time and overshoot.     

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.     

Optimal Automatic Generation Control with Hydro,Thermal, Gas,and Wind Power Plants in 2-Area Interconnected Power System

Abstract

This paper explores automatic generation control (AGC) of a more realistic 2-area multi-source power system comprising hydro, thermal, gas, and wind energy sources-based power plants in each control area. The wind power plants (WPPs) have been growing continuously worldwide due to their inherent feature of providing eco-friendly sustainable energy. But, operations of WPPs are associated with system stability problems due to lack of inertia. However, WPPs do not participate in the elimination of mismatch between generation and demand by AGC but disturbance can be injected by the WPPs due to the stochastic nature of wind energy. An optimal controller based on full state feedback control theory is designed to conduct the study. The system dynamic performance analysis is carried out for 1% step load disturbance in corresponding control areas. It is observed that the system dynamic graphs of deviation in area frequency and tie-line power are significantly improved with the implementation of optimal AGC controller compared to GA tuned classical controller. It has also been shown that the WPPs aid the increase in load disturbance when the input wind power reduces but it negates the effect of increase in load disturbance for increase in wind energy to the WPPs.     

Adaptive fuzzy logic load frequency control of multi-area power system

Based on indirect adaptive fuzzy control technique, a new load frequency control (LFC) scheme for multi-area power system is proposed. The power systems under study have the characterization of unknown parameters. Local load frequency controller is designed using the frequency and tie-line power deviations of each area. In the controller design, the approximation capabilities of fuzzy systems are employed to identify the unknown functions, formulate suitable adaptive control law and updating algorithms for the controller parameters. It is proved that the proposed controller ensures the boundedness of all variables of the closed-loop system and the tracking error. Moreover, in the proposed controller an auxiliary control signal is introduced to attenuate the effect of fuzzy approximation error and to mitigate the effect of external disturbance on the tracking performance. Simulation results of a three-area power system are presented to validate the effectiveness of the proposed LFC and show its superiority over a classical PID controller.     

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.     

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.     

Adaptive fuzzy gain scheduling for load frequency control

An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers has been proposed. A Sugeno type fuzzy inference system is used in the proposed controller. The Sugeno type fuzzy inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a very nonlinear system moves around in its operating space. The proposed adaptive controller requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions     

智能PID控制器及其在锅炉过热汽温自适应控制中的仿真研究

提出了一种新型智能PID控制器，并在电厂主汽温串级控制系统中进行仿真研究。智能PID控制器由模糊PD控制器和自调节积分环节并联组成，其特点是：模糊控制器的规则库由自适应神经元在线调节，积分器的增益由模糊推理机在线整定。因而该控制系统是无模型控制系统，无须被控对象的精确数学模型，系统可以实现参数的自整定，具有很强的自学习能力。对某超临界600MW直流锅炉主汽温控制的仿真结果表明，这种控制器可以实现多个工况点的控制，具有很强的自适应能力，并且在大范围的负荷变化和时变控制系统中仍具有稳定的控制效果、较强的鲁棒性和较好的抗扰动性能。     

Design of biogeography optimization based dual mode gain scheduling of fractional order PI load frequency controllers for multi source interconnected power systems

This paper presents the effect on application of biogeography optimization (BBODMFOPI) based dual mode gain scheduling of fractional order proportional integral controllers for load frequency control (LFC) of a multi source multi area interconnected power systems. This controller has three parameters to be tuned. Thus, it provided one more degree of freedom in comparison with the conventional proportional integral (PI) controller. For proper tuning of the controller parameters, Biogeography-Based Optimization (BBO) was applied. BBO is a novel evolutionary algorithm which involves the methodology of making the system effectively by using mathematical techniques. The dual mode concept is also incorporated in this work, because it can improve the system performance. In this work, simulation investigations were taken out on a two-area power system with different generating units. The simulation results show that the proposed biogeography optimization based dual mode gain scheduling of fractional order PI controllers, provide better transient as well as steady state response. It is also found that the proposed controller is less sensitive to the changes in system parameters and robust under different operating condition of the power systems.     

Robust analysis and design of power system load frequency control using the Kharitonov's theorem

This paper presents a robust decentralized proportional-integral (PI) control design as a solution of the load frequency control (LFC) in a multi-area power system. In the proposed methodology, the system robustness margin and transient performance are optimized simultaneously to achieve the optimum PI controller parameters. The Kharitonov’s theorem is used to determine the robustness margin, i.e., the maximal uncertainty bounds under which the stable performance of the power system is guaranteed. The integral time square error (ITSE) is applied to quantify the transient performance of the LFC system. In order to tune the PI gains, the control objective function is optimized using the genetic algorithm (GA). To validate the effectiveness of the proposed approach, some time based simulations are performed on a three-area power system and the results are then compared with an optimal PI controller. The comparisons show that the proposed control strategy provides the satisfactory robust performance for the wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other methods.     

AGC of a multi-area multi-source hydrothermal power system interconnected via AC/DC parallel links under deregulated environment

This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system.     

基于遗传算法的火电单元机组模糊控制系统

火电单元机组是一个多变量、强耦合、大时滞复杂的非线性系统，难以建立精确的数学模型，其控制系统的设计面临着严峻的挑战。文章提出了基于遗传算法的火电单元机组模糊控制系统。遗传算法用全局优化模糊控制器隶属度函数的比例因子，优化后的模糊控制器较好地补偿了负荷扰动所引起的单元机组的实际值与设定值的偏差。仿真结果表明，该系统有良好的动态特性，能适应于火电单元机组大范围运行时的负荷跟踪。     

Genetically tuned fuzzy PID controller in two area reheat thermal power system

This paper demonstrates the design and analysis of automatic generation control using intelligent genetic algorithm tuned fuzzy based controller. A two area thermal power system simulated for four different scenarios considers a reheat steam turbine in each area with Generator rate constraints. The Integral Time Squared Error (ITSE) employed to get an objective function for the optimization of controller gains. The simulation results compared with the conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA) tuned PID controller and GA tuned Fuzzy PID controller. The proposed GA tuned Fuzzy based PID Controller can generate the best performance for peak overshoot, undershoot and settling time with step load disturbances. Robustness of the performance of the proposed controller provided with system parametric uncertainties.     

Power-Frequency Balance in Multi-generation System Using Optimized Fuzzy Logic Controller

This paper emphasizes the development of control strategy for inter-area oscillation suppression for a unified two-area hydro–thermal deregulated power system. A proportional derivative-type fuzzy logic controller with integral (PDFLC+I) controller was proposed for automatic generation control. Further comparisons among conventional integral controller, proportional integral derivative controller, and PDFLC+I are carried out, where the PDFLC+I controller is optimized by four different optimization techniques namely, algorithm, ant colony optimization, classical particle swarm optimization, and adaptive particle swarm optimization. In PDFLC+I controller optimization, scaling parameters of controllers are optimized. A comparative study shows that the proposed PDFLC+I controller has a better dynamic response following a step load change for the combination of PoolCo and bilateral contract-type transaction in deregulated environment. Proposed controller performance has also been examined for ±30% variation in system parameters. Non-linearity in the form of governor dead band is taken into account during simulation.     

Load frequency control of a realistic power system with multi-source power generation

In this paper, load frequency control (LFC) of a realistic power system with multi-source power generation is presented. The single area power system includes dynamics of thermal with reheat turbine, hydro and gas power plants. Appropriate generation rate constraints (GRCs) are considered for the thermal and hydro plants. In practice, access to all the state variables of a system is not possible and also their measurement is costly and difficult. Usually only a reduced number of state variables or linear combinations thereof, are available. To resolve this difficulty, optimal output feedback controller which uses only the output state variables is proposed. The performances of the proposed controller are compared with the full state feedback controller. The action of this proposed controller provides satisfactory balance between frequency overshoot and transient oscillations with zero steady state error in the multi-source power system environment. The effect of regulation parameter (R) on the frequency deviation response is examined. The sensitivity analysis reveals that the proposed controller is quite robust and optimum controller gains once set for nominal condition need not to be changed for ±25% variations in the system parameters and operating load condition from their nominal values. To show the effectiveness of the proposed controller on the actual power system, the LFC of hydro power plants operational in KHOZESTAN (a province in southwest of Iran) has also been presented.