Application of Group Hunting Search Optimized Cascade PD-Fractional Order PID Controller in Interconnected Thermal Power System

This paper is an endeavor to enhance the performance of the Automatic Generation Control (AGC) by adopting cascade PD-FOPID (Proportional Derivative - Fractional Order PID) controller in a two-area mutually connected thermal power plant with Generation Rate Constraint (GRC). The performance of the cascade PD-FOPID controller is validated by contrasting PID and FOPID controllers implemented in each area as AGC. The basic goal of the design of these controllers is to lessen the area control error (ACE) of corresponding area by conceding the frequency and tie-line power deviation. Group Hunting Search (GHS) algorithm is adopted to explore the gain parameters of the controllers to lessen the objective function (ITAE). A small step load transition of 0.01 p.u. is enforced in area-1 to investigate the controller performance. Cascade PD-FOPID controller optimized by GHS algorithm performs precisely better than PID and FOPID controller in the proposed system. Citation: Nayak, J. R., and Shaw, B. (2018). Application of Group Hunting Search Optimized Cascade PD-Fractional Order PID Controller in Interconnected Thermal Power System. Trends in Renewable Energy, 4, 22-33. DOI: 10.17737/tre.2018.4.3.004

Evolutionary algorithms based tuning of PID controller for an AVR system

In this paper, an evolutionary algorithm based optimization algorithm is proposed with new objective function to design a PID controller for the automatic voltage regulator (AVR) system. The new objective function is proposed to improve the transient response of the AVR control system and to obtain the optimal values of controller gain. In this paper, particle swarm optimization (PSO) and cuckoo search (CS) algorithms are proposed to tune the parameters of a PID controller for the control of AVR system. Simulation results are capable and illustrate the effectiveness of the proposed method. Numerical and simulation results based on the proposed tuning approach on PID control of an AVR system for servo and regulatory control show the excellent performance of PSO and CS optimization algorithms

A fractional order PID tuning tool for automatic voltage regulator using marine predators algorithm

The fractional-order proportional-integral-derivative (FOPID) controller stands as a widely embraced choice for the task of automatic voltage regulation (AVR) when it comes to maintaining the voltage output of synchronous generators. Nevertheless, fine-tuning the FOPID controller presents a formidable challenge, mainly because it possesses five tuning gains, in contrast to the conventional PID controller, which has three gains. Consequently, this paper introduces a novel tuning tool tailored to the AVR system by utilizing the marine predators algorithm (MPA). To gauge the effectiveness of the proposed approach, two key evaluation criteria are employed: step response analysis and trajectory tracking analysis. The results of this research reveal that the MPA-FOPID controller demonstrates exceptional performance criteria, notably enhancing the AVR transient response in comparison to other FOPID controllers optimized through recent metaheuristic algorithms

An Efficient Optimal Fractional Emotional Intelligent Controller for an AVR System in Power Systems

In this paper, a high-performance optimal fractional emotional intelligent controller for an Automatic Voltage Regulator (AVR) in power system using Cuckoo optimization algorithm (COA) is proposed. AVR is the main controller within the excitation system that preserves the terminal voltage of a synchronous generator at a specified level. The proposed control strategy is based on brain emotional learning, which is a self-tuning controller so-called brain emotional learning based intelligent controller (BELBIC) and is based on sensory inputs and emotional cues. The major contribution of the paper is that to use the merits of fractional order PID (FOPID) controllers, a FOPID controller is employed to formulate stimulant input (SI) signal. This is a distinct advantage over published papers in the literature that a PID controller used to generate SI. Furthermore, another remarkable feature of the proposed approach is that it is a model-free controller. The proposed control strategy can be a promising controller in terms of simplicity of design, ease of implementation and less time-consuming. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. In order to design BELBIC controller for AVR system a multi-objective optimization problem including overshoot, settling time, rise time and steady-state error is formulated. Simulation studies confirm that the proposed controller compared to classical PID and FOPID controllers introduced in the literature shows superior performance regarding model uncertainties. Having applied the proposed controller, the rise time and settling time are improved 47% and 57%, respectively

Modeling and fuzzy FOPID controller tuned by PSO for pneumatic positioning system

A pneumatic cylinder system is believed to be extremely nonlinear and sensitive to nonlinearities, which makes it challenging to establish precise position control of the actuator. The current research is aimed at reducing the overshoot in the response of a double-acting pneumatic actuator, namely, the IPA positioning system’s reaction time. The pneumatic system was modeled using an autoregressive with exogenous input (ARX) model structure, and the control strategy was implemented using a fuzzy fractional order proportional integral derivative (fuzzy FOPID) employing the particle swarm optimization (PSO) algorithm. This approach was used to determine the optimal controller parameters. A comparison study has been conducted to prove the advantages of utilizing a PSO fuzzy FOPID controller over PSO fuzzy PID. The controller tuning algorithm was validated and tested using a pneumatic actuator system in both simulation and real environments. From the standpoint of time-domain performance metrics, such as rising time (tr), settling time (ts), and overshoot (OS%), the PSO fuzzy FOPID controller outperforms the PSO Fuzzy PID controller in terms of dynamic performance

An improved marine predators algorithm-tuned fractional-order PID controller for automatic voltage regulator system

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers