• Energy Research
• 2016-2025close

The function of Automatic Generation Control (AGC) is to regulate the output power of the corresponding generator in response to changes in system frequency or tie line loading or in both cases within a prescribed limit. In this paper Differential Evolution (DE) based Proportional-Integral (PI) controller is designed and simulated to observe its performance for a two area Hydro-Thermal power system. The purpose of the DE is to find out the optimal parameter values of the PI controller (K p and K i ). The Optimal set of values is chosen based on eigenvalue of system matrix and objective function. The performance was evaluated based on the transient response (Settling time and peak overshoot) of the system while different step load changes were applied on both or either of the areas. The proposed controller has been found functioning properly for not only smaller (1%) but also larger (5%) load disturbances. All the simulations are done using MATLAB/SIMULINK software.

This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study. To be published in Engineering Applications of Modern Metaheuristics under Studies in Computational Intelligence series

Load Frequency Control (LFC) for any power generating station is a subject of great concern for power system researchers. With the changes of load demand, frequency starts fluctuating which results in deviation in tie line power flow and frequency deviation at consumer end. To overcome this problem, many control techniques have been adopted. In early days fixed value integral/proportional-integral control, Optimal Control, Quantitative feedback theory, pole placement etc. methods were applied. In recent times, neural network, fuzzy logic, genetic algorithm controllers are replacing the conventional techniques. All the control techniques are used to find the optimal values of the PID/PI controller gain parameters (Kp, Ki, Kd) for which system stability is confirmed with minimum of Area Control Error (ACE). Differential Evolution (DE) which is a newer branch of genetic algorithms has been successfully applied in this problem. In this paper DE based PI controller has been implemented for Hydro-Thermal power plants to find out the optimal value of gain parameters for system stability. Nonlinearity has been considered in governor part of the thermal area for practical scenario. 1% step load changes have been applied to both areas simultaneously and individually to confirm its performance. Desired set of controller gain parameters (Kp, Ki) are selected based on eigenvalue and minimum value of Objective Function. All simulations are done in the MATLAB/SIMULINK environment.

Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional–integral controller designed to improve the small signal dynamic response of a grid-connected solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. The proposed proportional–integral (PI) controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures, grid current d–q components and dc voltage with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue-based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy between the obtained and desired eigenvalues is minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed-loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain-based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, nonparametric statistical analyses, namely one-sample Kolmogorov–Smirnov (KS) test and paired sample t test, are used to identify the statistical advantage of DE algorithm over the other two. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

The critical challenge for an efficient islanding operation of a distribution system having Distributed Generation (DG) is preserving the frequency and voltage stability. Contemporary load shedding schemes are inefficient and do not adequately assess the optimum amount of load to shed which results in either excessive or inadequate load shedding. Hence, this paper presents an optimal load shedding technique using Chaotic Slime Mould Algorithm (CSMA) with sinusoidal map in order to achieve greater efficiency. A constrained function with static voltage stability margin (VSM) index and total remaining load after load shedding was applied to accomplish the evaluation. A total of three islanding scenarios of IEEE 33 bus and IEEE 69 bus radial distribution systems were used as test systems to assess the efficacy of the proposed load shedding approach using MATLAB software. To identify performance enhancements, the developed method was compared to Backtrack Search Algorithm (BSA) and the original SMA. According to the results, CSMA outperforms both BSA and SMA in terms of remaining load and voltage stability margin index values in all the test systems.