Abstract
This paper investigates the problem of load frequency control by the application of metaheuristic optimisation techniques. In order to analyse the effect of the system model on the performance of the algorithm, two different two-area systems are considered, modelled using the Matlab/Simulink package. The first one consists of a reheat thermal area and a hydro area while the second one is made up of identical reheat thermal areas. To take into consideration the effects of practical constraints, nonlinearities such as Generation Rate Constraint, Governor Deadband and Boiler Dynamics are introduced. The Shuffled Frog Leaping Algorithm and Teaching Learning Based Optimisation are applied followed by a proposed hybrid of both algorithms to tune Proportional-Integral-Derivative (PID) controllers for the different areas of the systems, taking into account step load changes as inputs. The aim of this proposed algorithm is to merge the qualities of the individual algorithms to provide a more efficient one, converging faster to the optimal gains of the controllers. The results obtained proved the satisfactory performances of all algorithms and superiority of the hybrid Shuffled Frog Leaping Algorithm -Teaching Learning Based Optimisation technique in controlling frequency level in both systems investigated, where the main control measures such as peak values, settling times and steady-state values have been considered.Article HighlightsWhen considering a system of electrical mechanisms installed to produce, distribute and use electric energy, it is important to match supply with demand in order to keep frequency almost constant so as to ensure the safe and reliable use of equipment and maintain stability.The combination of two different algorithms efficiently improved the performance of the control system since the new system benefitted from the qualities of both strategies.Algorithm-based controllers provided robust and reliable frequency control and may be used to increase the quality of electrical energy under increasing and constantly changing demand.
Highlights
Load Frequency Control (LFC), termed as power system frequency control or regulation is one of the most important functions of the Automatic Generation Control (AGC)
In [17], optimisation techniques such as Genetic Algorithm (GA), Cultural Algorithm (CA) and Jaya-based Invasive Weed Optimization (JIWO), have been utilized to tune PID controllers and the results reported have proved to be satisfactory in improving the transient and steady-state responses of the frequency and tie-line power deviations of the system considered, with JIWO being the better technique in terms of optimality and convergence rate
This paper aims to apply the Shuffled leaping frog algorithm (SFLA), Teaching learning based optimisation (TLBO) and a proposed hybrid SFLA-TLBO optimisation techniques to optimize the gains of PID controllers for the load frequency control of a two-area system comprising of a reheat thermal and hydro areas and a non-linear two-area system consisting of two identical reheat thermal areas with non-linearities namely Governor Deadband (GDB), Generation Rate Constraint (GRC) and Boiler Dynamics (BD) by minimizing the fitness values obtained from the objective function
Summary
Load Frequency Control (LFC), termed as power system frequency control or regulation is one of the most important functions of the Automatic Generation Control (AGC). In [17], optimisation techniques such as Genetic Algorithm (GA), Cultural Algorithm (CA) and Jaya-based Invasive Weed Optimization (JIWO), have been utilized to tune PID controllers and the results reported have proved to be satisfactory in improving the transient and steady-state responses of the frequency and tie-line power deviations of the system considered, with JIWO being the better technique in terms of optimality and convergence rate. This paper aims to apply the SFLA, TLBO and a proposed hybrid SFLA-TLBO optimisation techniques to optimize the gains of PID controllers for the load frequency control of a two-area system comprising of a reheat thermal and hydro areas and a non-linear two-area system consisting of two identical reheat thermal areas with non-linearities namely GDB, GRC and BD by minimizing the fitness values obtained from the objective function.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
