Comparison of performances of several FACTS devices using Cuckoo search algorithm optimized 2DOF controllers in multi-area AGC

This paper presents automatic generation control (AGC) of three unequal area thermal systems with single reheat turbine and appropriate generation rate constraints (GRC) in each area. A two degree of freedom (2DOF) controller called 2DOF-integral plus double derivative (2DOF-IDD) is proposed for the first time in AGC as secondary controller. Secondary controller gains and other parameters are optimized simultaneously using a more recent evolutionary computational technique called Cuckoo Search algorithm (CS). The system dynamic responses for various 2DOF controllers such as 2DOF-PI, 2DOF-PID, and 2DOF-DD are compared. Investigations reveal that responses with 2DOF-IDD are better than others. Performance of several FACTS devices such as Static synchronous series compensator (SSSC), Thyristor controlled series capacitor (TCSC), Thyristor controlled phase shifter (TCPS), and Interline power flow controller (IPFC) in presence of 2DOF-IDD controller are compared and found that the dynamic responses with IPFC are better than others. For the first time in AGC, a case study is performed with placement of IPFC and observed that IPFC present in all three areas of the system performs better. Sensitivity analysis reveals that the CS optimized 2DOF-IDD controller parameters obtained in presence of IPFC in all three areas at nominal condition of loading and size of step load perturbation (SLP) are robust and need not be reset with wide changes in system loading and SLP. Also, the comparison of convergence curve of various algorithms reveals that CS algorithm converges much faster than others.     

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.     

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.     

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.     

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.     

A new coordination strategy of SSSC and PSS controllers in power system using SOA algorithm based on Pareto method

Along with the development of power grids and increasing the use of Flexible AC Transmission System (FACTS) devices, complex and unexpected interactions will be increased in power system. With considering to the non-linearity of power system, operating point changes and reaction between power system and FACTS devices, using of linear methods are not suitable for controller design. Therefore, the nonlinear model to design of Power System Stabilizer (PSS) and Static Synchronous Series Compensator (SSSC) coordinated controllers is considered here. In this paper, a new multi-objective function as an optimization problem is proposed for this coordination process. Also a beneficial strategy to solve this optimization problem using Seeker Optimization Algorithm (SOA) based on Pareto optimum method with high convergence speed is presented. In order to evaluate the performance of the proposed method, coordination strategy is applied on a four-machine system under different contingencies. The results of the proposed multi-objective function are obtained and compared with others in this system and finally, superior ability of the proposed method is observed.     

A hybrid BFOA–MOL approach for FACTS-based damping controller design using modified local input signal

A hybrid Bacteria Foraging Optimization Algorithm and Many Optimization Liaisons (hBFOA–MOL) is employed in this paper to design a Flexible Ac Transmission Systems (FACTS)-based damping controller for power system stability improvement. The input signal to the FACTS-based damping controller is derived from the locally measurable line active power. The design problem of the proposed controller is formulated as an optimization problem and hBFOA–MOL algorithm is employed to search for the optimal controller parameters. At the outset, this concept is applied to a Static Synchronous Series Compensator (SSSC) connected in a single-machine infinite bus (SMIB) power system and then extended to a two-area four-machine power system. The performances of the proposed controllers are evaluated in SMIB and multi-machine power system subjected to various severe disturbances. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with both local and remote signals. It is observed that the proposed controller with modified local input signal exhibits a superior damping performance in comparison to both remote and local input signals.     

Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system

This paper presents automatic generation control (AGC) of interconnected two equal area, three and five unequal-areas thermal systems provided with single reheat turbine and generation rate constraints of 3% per minute in each area. A maiden attempt is made to apply integral plus double derivative (IDD) controller in AGC. Controller gains in the two-area system are optimized using classical approach whereas in the three and five area systems controller gains and governor speed regulation parameters (R_i) are simultaneously optimized by using a more recent and powerful evolutionary computational technique called bacterial foraging (BF) technique. Investigations reveal on comparison that Integral (I), Proportional-Integral (PI), Integral-Derivative (ID), or Proportional-Integral-Derivative (PID) controllers all provide more or less same response where as Integral-Double Derivative (IDD) controller provides much better response. Sensitivity analysis reveals the robustness of the optimized IDD controller gains and R_i of the five area system to wide changes in inertia constant (H), reheat time constant (T_r), reheat coefficient (K_r), system loading condition and size and position of step-load perturbation.     

基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计

针对传统控制器存在的响应速度较慢、超调较大及鲁棒性较差等问题,提出一种基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计方法。首先,建立含飞轮储能的两区域互联电网AGC系统模型,模拟飞轮储能联合火电机组参与AGC的过程。然后,设计一种基于串级PI-(1+PD)算法的AGC控制器。外环采用PI控制,内环采用带滤波系数的(1+PD)控制。在保证系统稳态性能的前提下,提高动态响应速度和抗扰能力,并通过粒子群算法的迭代寻优获得最优的控制器参数。最后,基于Matlab/Simulink进行算例仿真分析。结果表明：与传统PID控制和PI-PD控制相比,所提方法不仅具有更快的响应速度与更小的超调量,而且增强了系统抵御内部参数摄动的鲁棒性。     

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.     

A new multi-objective fuzzy-GA formulation for optimal placement and sizing of shunt FACTS controller

The location and sizing of FACTS controllers for voltage stability enhancement is an important consideration for practical power systems. In this paper, a strategy for placement and sizing of shunt FACTS controller using Fuzzy logic and Real Coded Genetic Algorithm is proposed. A fuzzy performance index based on distance to saddle node bifurcation, voltage profile and capacity of shunt FACTS controller is proposed. The proposed technique can be used to find the most effective location and optimal size of the shunt FACTS devices. The proposed approach has been applied on IEEE 14-bus and IEEE 57-bus test systems. The application results are promising.     

计及FACTS装置的概率特征根分析

概率特征根分析计及了较宽范围的系统运行方式变化,利用柔性交流输电系统FACTS(Flexible AC Transmission System)装置上的附加控制器可以改善系统的动态特性,将现有的概率特征根分析扩展到包含FACTS功能模块。以并联型静态无功补偿器SVC(Static Var Compensator)和串联型可控串联补偿器TCSC(Thyristor-Controlled Series Capacitor)为例,在原有概率特征根模型的基础上,依据具体的元件模型和控制器表达,确定了形成系统状态空间方程时的相应线性化表达式;详细讨论了功能增加后系统状态方程矩阵的形成;通过补充相关的灵敏度计算,完成了计及FACTS装置的电力系统多运行方式下的小干扰稳定性分析。最后,在一个八机系统上进行了试算。在选定的附加控制器参数下,比较了增加SVC前后的系统主导特征根的变化,考察了附加控制器增益变化对临界特征根的影响。     

Loadability enhancement with FACTS devices using gravitational search algorithm

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations.     

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.     

Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller

Optimal power flow with generation reallocation is a suitable method for better utilization of the existing system. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Out of the FACTS devices unified power flow controller (UPFC) is a versatile device, capable of controlling the power system parameters like voltage magnitude, phase angle and line impedance individually or simultaneously. The main aim of this paper is to minimize real power losses in a power system using BAT search algorithm without and with the presence of UPFC. Minimization of real power losses is done by considering the power generated by generator buses, voltage magnitudes at generator buses and reactive power injection from reactive power compensators. The proposed BAT algorithm based Optimal Power Flow (OPF) has been tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results of the system with and without UPFC are compared in terms of active power losses in the transmission line using BAT algorithm. The obtained results are also compared with Genetic algorithm (GA).     

Bat inspired algorithm based optimal design of model predictive load frequency control

Bat inspired algorithm (BIA) has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, BIA-based design of model predictive controllers (MPCs) is proposed for load frequency control (LFC) to enhance the damping of oscillations in power systems. The proposed model predictive load frequency controllers are termed as MPLFCs. Two-area hydro-thermal system, equipped with MPLFCs, is considered to accomplish this study. The suggested power system model considers generation rate constraint (GRC) and governor dead band (GDB). Time delays imposed to the power system by governor-turbine, thermodynamic process, and communication channels are accounted for as well. BIA is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed controller has been compared to those of the conventional PI controller based on integral square error (ISE) technique and the PI controller optimized by genetic algorithms (GA), in order to demonstrate the superior efficiency of the BIA-based MPLFCs. Simulation results emphasis on the better performance of the proposed MPLFCs compared to conventional and GA-based PI controllers over a wide range of operating conditions and system parameters uncertainties.     

考虑饱和环节的自动发电控制时滞系统串级控制

自动发电控制(AGC)调节过程中存在发电机变化率约束、时延等约束条件,使得基于线性模型的AGC控制策略不能反映真实电力系统的频率调节特性。针对AGC时滞系统同时存在饱和与时延的问题,提出了一种基于内外环比例—积分(PI)稳定域的串级控制系统遗传优化策略。基于AGC系统的负荷频率控制与机组控制组成的串级控制回路,采用Hopf分岔代数判据和时滞系统稳定域理论,分别求取了内外回路的PI稳定域,证明了饱和及时延参数会影响到PI稳定域的变化。通过将稳定域转化为控制器参数优化的约束条件后,对内环优化采用不同指标进行对比,证明了绝对误差积分(IAE)指标对于扰动具有更好的抑制能力;而对外环的对比表明采用时间乘平方误差积分(ITSE)指标具有更小的波动量。遗传优化结果表明所提控制策略能够有效抑制饱和及时延环节对系统性能的影响。     

基于最优比例积分的电力弹簧控制器设计

电力弹簧((Electric Spring,ES)能有效缓解分布式发电系统中出现的间歇性和不稳定性问题,但是在现有控制方式下易引入谐波,且响应时间较长,严重影响了电力弹簧的调节效果。针对PI和PR控制器应用于ES这个非线性复杂系统上稳定性和鲁棒性较差的特点,应用改进的单纯型加速算法(simplex method,SPX),以时间乘以误差平方积分准则作为寻优目标函数,对PI控制器的参数进行实时调整、寻优,使ES的动态响应效果达到最佳。仿真结果表明最优PI控制器能快速、无超调的跟踪ES系统的电压设定值,具有较强的适应性和稳定性。     

Swarm intelligence based algorithms for reactive power planning with Flexible AC transmission system devices

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive Particle Swarm Optimization) and EPSO (Evolutionary Particle Swarm Optimization) are used for the optimal setting of the Var sources and FACTS devices. The result obtained with the proposed approach is compared with the result found by the conventional RPP (Reactive power planning) approach where shunt capacitors, transformer tap setting arrangements and reactive generations of generators are used as planning variables. It is observed that reactive power planning with FACTS devices yields much better result in terms of reducing active power loss and total operating cost of the system even considering the investment costs of FACTS devices.     

Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system

A new optimization technique called Cuckoo Search (CS) algorithm for optimum tuning of PI controllers for Load Frequency Control (LFC) is suggested in this paper. A time domain based-objective function is established to robustly tune the parameters of PI-based LFC which is solved by the CS algorithm to attain the most optimistic results. A three-area interconnected system is investigated as a test system under various loading conditions where system nonlinearities are taken into account to confirm the effectiveness of the suggested algorithm. Simulation results are introduced to show the enhanced performance of the developed CS based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and conventional integral controller. These results denote that the proposed controllers offer better performance over others in terms of settling times and various indices.