Banaja Mohanty

Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA-PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA-PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA-PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to -50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA-PSO optimized PI controller. 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 craziness based particle swarm optimization (CRAZYPSO) 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 PI coefficients and comparison between ANFIS and proposed approach has been provided.

A new multicarrier SPWM technique for five level cascaded H-bridge inverter

Many researchers have investigated modulation techniques applied to the cascaded multilevel inverter to improve its harmonics performance. Different methods have been proposed for cascaded multilevel inverter modulation that one of them is level-shifted PWM method (LS-PWM). In this paper, a novel multicarrier SPWM technique which uses a trapezoidal triangular carrier is proposed for a five level cascaded multilevel inverter. This carrier waveform is being implemented with different LS-PWM techniques such as phase disposition (PD), phase opposition disposition (POD) and alternative phase opposition disposition (APOD). The line voltage and total harmonics distortion (THD) obtained in various techniques are compared with the outputs of triangular carrier wave. To validate the improvement, these PWM techniques are simulated for a 1KW, 3φ five level inverter with 2 KHz switching frequency using MATLAB/SIMULINK. The effect of modulation index on the line voltage and harmonics are also analyzed. The proposed switching technique enhances the fundamental component of the output voltage and lowers total harmonic distortion.

Performance Comparison of FOA Optimized Several Classical Controllers for Multi-area Multi-sources Power System

This study extensively represents a practical power system using various sources in each control area connected through AC/DC transmission link. Each area includes reheat thermal, hydro and nuclear power plant using proper generation rate constraints. A new population-based algorithm, i.e. fruit fly optimization algorithm (FOA), is applied for tuning purpose. Initially, the parameters of integral (I) controller and parameters of HVDC are optimized with FOA algorithm employing integral time absolute error (ITAE) as an objective function, and to show supremacy of FOA technique, the system performances are compared with GA and PSO algorithms. Further, performances of different conventional controllers like integral-derivative (ID), proportional-integral (PI), proportional-integral-derivative (PID), integral-double-derivative (IDD) and proportional-integral-double-derivative (PIDD) are compared with FOA algorithm for the concerned system. The investigation reveals that PIDD controller tuned with FOA algorithm outperforms than other controllers. Additionally, sensitivity analysis is executed with parameters of the system and operative load conditions variation. It is observed from simulation outcomes that the optimum gains of the controller are robust enough to sustain wide variation in loading condition and system parameters.

Optimized 2DOF PID for AGC of Multi-area Power System Using Dragonfly Algorithm

This paper presents the automatic generation control (AGC) of an interconnected two-area multi-source hydro-thermal power system. The considered system performance is studied and analyzed with proportional-integral (PI), proportional-integral-derivative (PID) and 2 degree of freedom PID(2DOF PID). The gains of the controllers are optimized using dragonfly algorithm (DA). The performance of the DA algorithm is matched with the genetic algorithm (GA) and hybrid firefly and pattern search technique to state its superiority. The comparative results show that 2DOF PID scheme tuned using DA gives better results than the classical controllers.

Performance analysis of moth flame optimization algorithm for AGC system

Abstract In this paper, an attempt has been made for comprehensive study of proportional-integral-double-derivative(PIDD) controller to solve automatic generation control (AGC) problem by applying moth flame optimization algorithm (MFOA). At first two unequal areas of thermal system is considered and the gains of PID/IDD/PIDD controller are optimized using MFOA technique. Simulation study depicted that MFOA-optimized PIDD controller provides better system performances considering settling time, overshoot and undershoot of area frequency and deviations in tie-line power as compared to other optimization techniques considered in this paper. The generation rate constraint (GRC) is included for two-area thermal system and dynamic stability of the system is investigated and compared with recent competitive algorithms. Further, the study is extended to non-linear AGC system with diverse source of generation. Generating unit in each control area consists of hydro, thermal and nuclear generation. Sensitivity analysis reveals that the MFOA-optimized PIDD controller parameter obtained at nominal condition need not necessary to change for wide changes in system parameters and with variation in random step load perturbation.

Load frequency controller design via GSO algorithm for nonlinear interconnected power system

Glow Swarm Optimization(GSO) and Particle Swarm Optimization(PSO) algorithms are proposed in this paper for optimal tuning of PI controllers for Load Frequency ControUer(LFC) design. To visualize the effectiveness of the proposed method, a two area interconnected power system is considered as a test system. To prove the robustness of the proposed controller and to stabilize the frequency of oscillations, the design process takes a large range of operating conditions and system nonlinearities in to account. The simulation results emphasis on the superiority of GSO algorithm over BAT and Simulated Annealing (SA) in tuning PI controller parameters with the help of different performance indices. The result analysis demonstrate that the proposed algorithm achieves robust performance for different load perturbations in second as well as both areas as compared to BAT and SA approaches.

Performance evaluation of differential evolution algorithm on automatic generation control

In this paper load frequency control of two area interconnected power system with classical controller is considered. The optimum gains of PI controller is optimized using genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), particle swarm optimization (PSO) and differential evolution (DE) algorithm. The results of all the heuristic optimization algorithms are compared. Investigations are carried out considering step load change in one area and simultaneously both areas. Investigations reveal that proposed DE algorithm performs better compared to other techniques. To show the superiority of proposed controller, size of step load perturbation is varied and dynamic performances are studied.