This study presents the automatic generation control of an unequal four-area thermal system with appropriate generation rate constraint and governor dead band (GDB). Performances of several classical controllers, such...
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This study presents the automatic generation control of an unequal four-area thermal system with appropriate generation rate constraint and governor dead band (GDB). Performances of several classical controllers, such as single degree-of-freedom proportional-integral-derivative (PID), two degree-of-freedom PID (2DOF-PID) and three degree-of-freedom PID (3DOF-PID) as secondary controllers are evaluated separately in the system. An attempt is made to apply the successful evolutionary optimisation technique named as biogeography-based optimisationtechnique for simultaneous optimisation of several variables, such as controller gains, setpoint weights and so on. Comparison of dynamic responses corresponding to PID, 2DOF-PID and 3DOF-PID reveals that 3DOF-PID controller outperforms the others. Sensitivity analysis reveals that the optimum gains and other parameters of the optimal controller obtained at nominal conditions are robust and need not be reset for wide changes in system condition like system loading, system parameters such as inertia constant (H), synchronising coefficient (T-ij) and GDB. The performance of 3DOF-PID controller is also studied with different step-load perturbations (SLPs) and random-load perturbations (RLPs). Analysis proves that 3DOF-PID controller performs better than 2DOF-PID at different SLPs and RLPs.
This paper presents a novel search algorithm, called bacteria foraging optimisation (BFO) for the design of linear phase positive symmetric FIR low pass, high pass, band pass and band stop filters, realising the respe...
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This paper presents a novel search algorithm, called bacteria foraging optimisation (BFO) for the design of linear phase positive symmetric FIR low pass, high pass, band pass and band stop filters, realising the respective ideal filter specifications. BFO is a population-based evolutionaryoptimisation concept used to solve nonlinear optimisation problem where each individual maintains the propagation of genes. BFO favours propagation of genes of those animals which have efficient foraging strategies and eliminate those animals that have weak foraging strategies i.e., method of finding, handling and taking in food. All animals with their own physiological and environmental constraints, try to maximise the consumption of energy per unit time interval. The performances of BFO-based FIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimisation (PSO optimisationtechniques. The simulation results justify that BFO is the best optimiser among the other optimisationtechniques, not only in the convergence speed but also in the accuracy and the optimal performances of the designed filters.
Chaotic whale optimisation algorithm (CWOA) is a metaheuristic real-parameter optimisation algorithm. This study appears to be well capable of providing solution to the transient stability constrained optimal power fl...
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Chaotic whale optimisation algorithm (CWOA) is a metaheuristic real-parameter optimisation algorithm. This study appears to be well capable of providing solution to the transient stability constrained optimal power flow (OPF) problem of power system. Basically, transient stability constrained OPF (TSCOPF) problem is the extended study of conventional OPF problem while additionally, considering transient stability constraints along with other previously considered equality and inequality constraints of the conventional OPF problem. Here, CWOA algorithm is validated by choosing two test power systems viz. (a) New England 10-generator, 39-bus and (b) 17-generator, 162-bus test systems. Considering multiple contingency cases, the main objective of the proposed algorithm is minimisation of the total fuel cost of these two test systems. Simulation test results, as obtained from the proposed CWOA, are compared to the results offered by some other evolutionary optimisation techniques surfaced in the recent state-of-the-art literature. The results presented in this study indicate that the proposed algorithm shows its efficacy over other recently originated popular optimisationtechniques (including basic WOA) in terms of extending potential of offering higher quality solutions, effectiveness and faster convergence speed.
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