Reheat Thermal Power System Research Articles

Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems

The current study presents the load frequency control (LFC) of multi-area reheat thermal power system with proportional-integral-derivative (PID) controller. The interconnected control areas are provided with a single stage reheat turbine in all areas. The proportional gain (KP), integral gain (KI) and derivative gain (KD) values of the PID controller are simultaneously optimised using recent and powerful evolutionary computational intelligence technique, namely the particle swarm optimisation (PSO) algorithm. The superiority of the PSO-based PID controller has been proved by comparing its performance to recent modern optimisation techniques such as hill climbing (HC) algorithm and genetic algorithm (GA) tuned controllers for the same multi-area thermal power system. For the analysis, the time domain specification and 1% step load perturbation (1% SLP) are considered in thermal area 1. The simulation result showed that the proposed PSO-based PID controller provides superior dynamic response over other optimisation technique (HC and GA)-based PID controller.

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.

Fuzzy Gain Scheduling Controllers for Automatic Generation Control of Two-area Interconnected Electrical Power Systems

In this study, fuzzy gain scheduling controllers are proposed for automatic generation control of interconnected electrical power systems. Primarily, the study is done for automatic generation control of a two-area non-reheat thermal power system, and the parameters of fuzzy gain scheduling controllers are optimized by a genetic algorithm. Simulation results show the performance of fuzzy gain scheduling controllers is superior compared to the optimal and controllers based upon the gravitational search, the bacteria foraging optimization, and the hybrid bacteria foraging optimization–particle swarm optimization algorithms for an identical power system. The proposed approach is further protracted to a two-area reheat thermal system; the benefits of the fuzzy gain scheduling approach are demonstrated over optimal, conventional proportional-integral, and genetic algorithm-based integral controllers. Next, a multi-source multi-area hydro thermal system is considered, and the superiority of fuzzy gain scheduling controllers is established by comparing the results to the genetic algorithm and best claimed hybrid firefly algorithm–pattern search technique-based controllers. Finally, the effectiveness of the proposed approach is established for a two-area restructured reheat thermal power system. The simulation results indicate that the proposed fuzzy gain scheduling controllers work efficiently and provide better dynamic performance without being redesigned for separate systems.

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD) controller for Automatic Generation Control (AGC) of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO) algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC) is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS), Firefly Algorithm (FA), Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB) non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.

Design of GA Tuned Two-degree Freedom of PID Controller for an Interconnected Three Area Automatic Generation Control System

In this paper an innovative controller named parallel two-degree freedom of PID controller has deliberated and employed in an interconnected three area reheat thermal power system. The optimal controller gains obtained by using Genetic algorithm and the Integral Time Squared Error (ITSE) employed as an objective function. To realize the system practically, physical constraints like generator rate constraints and governor dead band nonlinearities have considered. To compensate the loss of kinetic energy during sudden load increase Superconducting Magnetic Energy Storage used in each area. The results gained by using GA tuned parallel two-degree freedom PID controller has compared with the conventional Ziegler’s Nichols tuned PID controller and genetically tuned PID controller. The GA tuned parallel two-degree freedom of PID controller produces better transient response characteristics for frequency and tie line power deviations under step load perturbations. The system simulation have realized by means of MATLAB/SIMULINK software.

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.

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.

Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control

This paper presents the implementation of multiobjective based optimization of Artificial Bee Colony (ABC) algorithm for Load Frequency Control (LFC) on a two area interconnected reheat thermal power system. The ABC algorithm is currently being applied in many research works due to the local and global search capability of the algorithm. This paper uses the weighted sum approach of the ABC to optimize the PID controller’s gains to provide a compromise between the frequency response’s settling time and maximum overshoot. The composite objective function comprising both objectives is characterized by the performance criterions – Integral of Time Multiplied Absolute Error (ITAE) and Integral of Time Weighted Squared Error (ITSE). Analysis is carried out to determine the best weightage set for this investigation. A performance index based on Least Average Error (LAE) is formulated to calculate the index of each weightage set. In order to ensure effective compensation in the system output, the PID controllers for both areas are tuned simultaneously. The tuning performance of the algorithm is evaluated by comparing the performance of the proposed controller with conventional PI and PID controller. The robustness of the proposed algorithm is further investigated by evaluating the response of the system under simultaneous step load perturbation (SLP), changing load demand and collectively varying system parameters in the range of ±50%. The simulation result shows the dynamic response of the controller emphasizes on the compromise between the settling time and maximum overshoot of the frequency response. Furthermore, the proposed algorithm is robust enough to operate under different operating conditions and system parameter variations.

Load Frequency Control of an Interconnected Reheat Thermal system using Type-2 fuzzy system including SMES units

The transient behavior of many large scale systems is heavily influenced by perturbations and in particular, usually, due to changes in operating points. Load Frequency Control (LFC) in power systems is very important in order to supply reliable electric power with good quality. The goal of LFC is to maintain zero steady state errors for frequency deviations in each control area. Several control strategies, such as classical control, optimal control, suboptimal control, adaptive control, variable structure control etc. have been employed in the past to explore an optimum controller for LFC. In practice LFC systems use simple Proportional Integral (PI) controllers, Proportional Integral Derivative (PID) controllers etc. This paper presents a method based on Type-2 Fuzzy System (T2FS) for Load frequency control (LFC) of power systems including Superconducting magnetic energy storage (SMES) units of a two-area interconnected reheat thermal system. 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), Boiler Dynamics (BD) and SMES. The salient advantage of this controller is its high insensitivity to large load changes and plant parameter variations even in the presence of non-linearities. The proposed method is tested on a two-area power system to illustrate its robust performance with various area load changes. The performance of the Type-2 (T2) fuzzy controller is compared with optimal PID (Khamsum’s optimal PID) controller and Fuzzy PI Controller (Type-1 Fuzzy) controller in the presence of GRC, BD and SMES. Simulation results confirm the high robustness of the proposed SMES controller with small power capacity against various disturbances and system uncertainties in comparison with SMES in the previous research.

Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system

This study extensively presents the Automatic Generation Control (AGC) application of Artificial Bee Colony (ABC) algorithm. This algorithm is one of the new population based optimization algorithms which have been developed since 2005. In this study, the algorithm is applied to the interconnected reheat thermal power system in order to tune the parameters of PI and PID controllers which are used for AGC. The tuning performance of the algorithm is compared with that of Particle Swarm Optimization (PSO) algorithm through transient response analysis method. In addition to these, the robustness analysis is applied to the power system which is optimized by ABC algorithm so as to determine its response towards changing in the load and the system parameters, varied in the range of ±50%. The behavior of the system is also investigated with this analysis towards the different cost functions such as integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE) and integral of time multiplied absolute error (ITAE). At the end of the study, it is seen that the ABC algorithm is successfully applied to the AGC in the application of interconnected reheat thermal power system, and it shows better tuning capability than the other similar population based optimization algorithm. Furthermore, it is also seen that the proposed system is robust and is not affected by changing in the load, the power system parameters and the cost functions.

Application of hybrid fuzzy PID controller for three-area power system with generation rate constraint

This paper presents a new approach for load frequency control of three-area interconnected reheat thermal power system with the consideration of generation rate constraint (GRC). The PID-type fuzzy controller is combined with a conventional PID controller to enhance the performance and robustness of the controller. The proposed controller called hybrid fuzzy PID (HFLPID) controller has been designed for a three-area connected power system. Two performance criteria were utilised for the comparison. First, settling times and overshoots of the frequency deviation were compared. Later, integral of time multiplied absolute error (ITAE) analysis was carried out to compare all the controllers. All the models were simulated by MATLAB 7.0/Simulink software. The simulation results show that the proposed controller developed in this study performs better than the other controllers. Robustness of proposed controller is checked by analysing the system response with varying system parameters.

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.

Fuzzy Rule Based Load Frequency Control in a Parallel AC – DC Interconnected Power Systems Through Hvdc Link

this paper, a fuzzy logic controller is proposed for an application of HVDC link to stabilize the frequency oscillation in a parallel AC - DC interconnected power systems. When an interconnected AC power system is subjected to a load disturbance, system frequency may be considerably disturbed and becomes oscillatory. By utilizing the system interconnections as the control channels of HVDC link, the tie line power modulation of HVDC link through interconnections is applicable for stabilizing the frequency oscillation of AC system. The conventional Integral controller does not yield adequate control performance. To overcome this problem Fuzzy Logic Controller (FLC) is employed with a set of control rules. The proposed control technique is studied for a two area non - reheat thermal power systems. From simulation results, the performance of the FLC is better during load disturbances.

An Extended Horizon Self Tuning Load Frequency Controller

Abstract An explicit extended horizon controller for the frequency and tie line power control of interconnected reheat thermal power systems is described. The design of the controller is based on a multistep cost function whose minimization yields the required control sequences. The control algorithm offers several tuning parameters for performance enhancement and their effects on overall load frequency control responses are studied.