Load frequency control of power system under deregulated environment using optimal firefly algorithm

In this paper, a novel Firefly Algorithm (FA) optimized hybrid fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of multi area multi source system under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. As the effectiveness of FA depends on algorithm control parameters such as randomization, attractiveness, absorption coefficient and number of fireflies are systematically investigated, the control parameters of FA are tuned by carrying out multiple runs of algorithm for each control parameter variation then the best FA control parameters are suggested. Additionally, the superiority of the FA is demonstrated by comparing the results with tuned Genetic Algorithm (GA). To investigate the effectiveness of the proposed approach, time domain simulations are carried out considering different contracted scenarios and the comparative results are presented. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions. It is observed from the simulation results that the designed controllers are robust and the optimum gains of proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed control scheme is evaluated under random step load disturbance.     

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.     

Application of firefly algorithm with online wavelet filter in automatic generation control of an interconnected reheat thermal power system

This paper presents the implementation of the Firefly Algorithm (FA) with an online wavelet filter on the automatic generation control (AGC) model for a three unequal area interconnected reheat thermal power system. The model includes time delay, dead zone, boiler, Generation Rate Constraint (GRC), and high frequency noise components. A novel filtering technique based on wavelet transform is introduced for the purpose of removing noise(s) from the ACE signal. The performance of the filter is measured by formulating a signal integrity index. The simulation results show that the FA is able to outperform the Particle Swarm Optimization (PSO) in obtaining the minimum objective function based on Integral of Time Weighted Squared Error (ITSE). The results also shows that the proposed online wavelet filter performs with a higher degree of efficiency compared to the conventional low pass filter when the practical model of the AGC is analyzed. Further investigation by varying the GRC and time delay parameter confirms the robustness of the FA tuned controller with the online wavelet filter.     

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.     

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.     

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.     

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 Novel Hybrid Salp Swarm Differential Evolution Algorithm Based 2DOF Tilted-Integral-Derivative Controller for Restructured AGC

Abstract

This article elevates the study of Automatic Generation Control (AGC) for a multisource interconnected power system under the restructured environment. Physical constraints such as Generation Rate Constraints (GRC), Governor Dead Band (GDB) and Boiler Dynamics (BD) are incorporated into the system for a more realistic approach. Distribution Generation (DG) system is also considered to incorporate the effects of renewable energy sources penetration. A novel cascaded topology of 2-Degree of Freedom Tilted-Integral-Derivative Controller with filter (2DOF-TIDN) is proposed for anticipated AGC mechanism. A maiden application of Hybrid Salp Swarm Differential Evolution Algorithm (HSSDEA) has been successfully applied to get the different optimum gains of the proposed controller. Capacitive Energy Storage (CES) system is also incorporated for the proposed system for the AGC mechanism. The supremacy of the proposed controller is examined by comparing with other well-known or benchmark controllers. The robustness of the proposed system has been analyzed for the contract violation case of deregulation. Finally, the efficacy of the proposed work has been verified by comparing with the previously published work of literature on same platform.     

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.     

Application of modular multilevel converter for AGC in an interconnected power system

This article demonstrates the maiden application of a new Modular Multi level Converter based Series Compensation (MMCS) technique for multi area Automatic Generation Control (AGC) interconnected system. Primarily MMCS has been modeled in state space form and proposes an appropriate location in AGC to obtain the better dynamic responses in frequency, tie-line power and individual generating power; further to quench the oscillation for sudden changes in load. The system has been studied the operation of MMCS and investigated with Generation Rate Constraints (GRC) of reheat turbines used in system. Further, selection of suitable integral and proportional–integral controller gain has been investigated with Integral Square Error (ISE) technique and Particle Swarm Optimization (PSO) technique for step load perturbation (SLP) in area-1 with performance index as its objective function by making control parameters as variables. System with MMCS is compared with out MMCS and observed performance has been increased and results are explored.     

Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system

In this paper, a hybrid combination of Neuro and Fuzzy is proposed as a controller to solve the Automatic Generation Control (AGC) problem in a restructured power system that operates under deregulation pedestal on the bilateral policy. In each control area, the effects of the possible contracts are treated as a set of new input signal in a modified traditional dynamical model. The prominent advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter discrepancy and system nonlinearities. This newly developed strategy leads to a flexible controller with a simple structure that is easy to implement and consequently it can be constructive for the real world power system. The proposed method is tested on a three-area hydro-thermal power system in consideration with Generation Rate Constraint (GRC) for different contracted scenarios under diverse operating conditions. The results of the proposed controller are evaluated with the Hybrid Particle Swarm Optimisation (HCPSO), Real Coded Genetic Algorithm (RCGA) and Artificial Neural Network (ANN) controllers to illustrate its robust performance.     

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.     

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.     

Grasshopper optimization algorithm optimized multistage controller for automatic generation control of a power system with FACTS devices

This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1+ PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1+ PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1+ PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.     

Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm

This paper presents a new and hybrid algorithm based on Firefly Algorithm (FA) and Recursive Least Square (RLS) for power system harmonic estimation. The hybrid FA–RLS algorithm is developed for estimating harmonics, inter harmonics and sub harmonics from a distorted and noise corrupted power signal. The basic strategy of the proposed algorithm is to integrate FA for getting the optimum initial weights for RLS algorithm that sequentially updates the unknown parameters (weights) of the harmonic signal. Simulation and practical validation is made with experimentation of the algorithms with real time data obtained from a solar connected inverter system. Comparison of results amongst recently proposed Artificial Bee Colony Least Square (ABC–LS), Bacteria Foraging Optimized Recursive Least Square (BFO–RLS) and FA–RLS algorithms reveals that proposed FA–RLS algorithm is the best in terms of accuracy, convergence and computational time.     

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.     

A new PID load frequency controller design method in frequency domain through direct synthesis approach

In this paper a new PID controller design method based on the direct synthesis (DS) approach of controller design in frequency domain is presented. The parameters of the PID controller are obtained through frequency response matching with the DS controller. The method yields linear algebraic equations, solution of which gives the controller parameters. The design method has been developed for single-area as well as multi-area power systems. Non-linearity like the generation rate constraint (GRC) has been considered. Several examples are taken from the literature to demonstrate the effectiveness of the proposed method in comparison with some prevalent design methods.     

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.     

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.     

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.