Comparative Performance Analysis of Metaheuristic-Tuned Fopid Controllers for Load Frequency Control in Multi-Source Power Systems

This paper presents a Fractional Order Proportional-Integral-Derivative (FOPID) controller optimized using Social Spider Optimization (SSO) algorithm to Load Frequency Control (LFC) of a two-area multi-source power system in the presence of Distributed Generations (DGs). Here, the effect of DGs consist of wind and diesel power plants for damping frequency deviation of two-area multi-source power system including thermal, hydro and gas units considering nonlinearities such as generation rate constraint and governor dead band has been investigated. The FOPID controller has three main control parameters as known gain, fractional orders of integrator and derivative part that provide the better performance in comparison with the PID controller. On the other hand, to achieve the good performance in the power system, tuning of these parameters is very important. Therefore, the SSO algorithm has been used to optimal tuning of the FOPID based LFC controller. One of the important specifications of the employed algorithm is that it can balance the local and global solutions in the search space that cause to overcome the local optimal and premature convergence. Also, a High Voltage Direct Current link (HVDC) is used to improve the dynamic performance of the LFC task. Finally, the proposed controller has been applied on a two case studies and analyzed based on investigated performance criteria. Results show the good effect of DGs for the solution of LFC task and enhancing system performance characteristics under different operating conditions and considering system nonlinearities.

Direct Current (DC) motors are extensively used in various applications due to their versatile and precise control capabilities. However, they face operational challenges such as speed instability and sensitivity to load variations and external disturbances. This study compares the performance of several advanced control methods—Proportional Integral Derivative (PID), Fractional Order PID (FOPID), Integral State Feedback (ISF), Sliding Mode Control (SMC), and Fuzzy Logic Controller (FLC) for DC motor control. Particle Swarm Optimization (PSO) is employed to optimize the tuning parameters of PID, FOPID, ISF, and SMC controllers, while FLC is implemented without optimization. The simulation results indicate that the PSO-FOPID controller exhibits the best overall performance, characterized by the fastest rise and settling times and the lowest ITSE, despite a minor overshoot. The PSO-PID controller also performs well, with fast response times, although it is less efficient in terms of settling time and ITSE compared to PSO-FOPID. The OBL/HGSO-PID controller, while stable and overshoot-free, has a slower response. The PSO-ISF controller shows the highest stability with the lowest SSE values, making it suitable for applications requiring high stability. The PSO-SMC controller demonstrates good stability but is slightly slower than PSO-ISF. The FLC controller, however, performs the worst, with significant overshoot and long recovery times, making it unsuitable for fast and precise control applications. The robustness analysis under varying motor parameters further confirms the superiority of the PSO-FOPID controller, which outperforms OBL/HGSO and OBL-MRFO-SA optimizations across both PID and FOPID controllers, making it the most effective solution for applications requiring high precision and rapid response.

A fractional-order proportional integral derivative (FOPID)controller has replaced the classical PID controller used in industries for process control application. FOPID is less sensitive to the change of control parameters than PID Controller, due to which better results are obtained. The FOPID provides a robust and stable system for a higher-order system as of iso- damping property. This study aims to find a stable and controlled structure by tuning the FOPID controller with the Particle Swarm Optimization (PSO) algorithm, fuzzy-based logic approach, and Adaptive Neuro-fuzzy inference system (ANFIS). The tuning of FOPID is done to overcome deficits of PID controller using the different types of optimization method to overcome large overshoot and large settling time. This paper presented useful techniques based on PSO, fuzzy logic, and ANFIS to optimize FOPID controlled high-performance drilling machines. On the analysis and comparisons of simulation findings, it is observed that the FOPID-PSO approach provides better performance over the Ziegler-Nichols (ZN)-FOPID and the above-mentioned intelligent techniques in terms of less settling time (ts=0.823sec.) & optimized peak overshoot (Mp=2.44%) for the mentioned target.

The rapid advancement of electric consumer demand enforces challenging situations to manage high penetration of Solar PV and Wind Power into the hydrothermal system resulting in load frequency control (LFC) problem. This article introduces an advanced strategy to manage the LFC issue in an integrated power system (PS) with the impact of hydrogen aqua equalizer (HAE)-based Fuel cells (FC). A transformative fractional-order fuzzy-based PID (FOFPID) controller regulated by the Sunflower optimizing (SFO) technique has been proposed for frequency regulation of the proposed integrated PS. The preeminence of the FOFPID controller has been validated over PID, Fuzzy-PID (FPID), and fractional order PID (FOPID) controller. The effectiveness of the SFO technique has been established over teaching learning-based optimization (TLBO) and Genetic Algorithm (GA). The supremacy of the SFO approach authenticated by some recent frequency regulation research outcomes. The influence of HAE-FC for frequency regulation in an integrated PS has been studied with the proposed SFO-tuned FOFPID controller. From the observation, it is perceived that the values of reduction in ITAE with suggested SFO-tuned FOFPID controller as compared to GA & TLBO are 84.61% & 56.04% and further 35.16% is reduced with the occurrence of HAE-FC element. It is also perceived that the improvement in settling time for ∆f1, ∆f2, ∆Ptie12 with SFO approach related to GA are 78.07%, 76%, and 83.87% and compared to TLBO are 9.97%, 38.25%, and 68.23%. Furthermore, 26.45%, 18.64%, and 31.21% are reduced with the presence of HAE-FC unit. Lastly, the sturdiness of the anticipated strategy was authenticated by a sensitivity test and a real-time validation test using OPAL-RT.

This study provides an efficient torque and speed control method for brushless DC (BLDC) motor utilizing a fractional order proportional integral derivative (FOPID) controller based on the multiverse optimization algorithm and salp swarm optimization (SSO). When combined with a solar photovoltaic (PV) system and a voltage boosting architecture, standard PID controllers regulated the BLDC drive parameters due to their simplicity and improved steady-state performances. A solar PV system with a specific power range is chosen for the best output solution on the output side. The MPPT method employed is the incremental conductance approach for tracking maximum power from the source. However, it has a problem with unpredictability owing to load changes. The PID controller tuning also relates to the structure’s parametric uncertainties. Therefore, precise control methods may be offered with the aid of the FOPID controller to address the aforementioned issues. Simulation results of the proposed MVO and SSO-based FOPID controller for BLDC speeds run in the Matlab Simulink platform. To confirm the adaptability of the suggested control scheme for BLDC motor, the outcomes are interrelated using genetic algorithm, gray wolf, and PSO-based FOPID controllers.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Load frequency control (LFC) is vital for balancing power generation with fluctuating power demand. It ensures that the system frequency and tie-line power are maintained at their desired levels. The design of an LFC system is crucial for the automation of power systems. This article introduces a Fuzzy Proportional-Integral-Derivative (FPID) control approach for LFC in an interconnected reheat thermal power system, utilizing a Particle Swarm Optimization (PSO) method and incorporating the Generation Rate Constraint (GRC) as nonlinearities. The proposed controller offers the advantage of effectively managing system nonlinearities while being better than classical controllers. The response of the FPID controller is contrasted with both the conventional Proportional-Integral (PI) controller and the Fractional Order PID (FOPID) controller, in scenarios both including and excluding the Generation Rate Constraint (GRC). The response of the system is analyzed by considering load disturbances in each region of the integrated power network.

The introduction of electrolytic aluminum load in a large-scale power system with complex power station structure is essential for load frequency control (LFC). In this paper, we propose a control strategy for electrolytic aluminum load to participate in the frequency modulation of the power grid based on the IEEE standard two-region LFC model. Besides, a fractional-order PID (FOPID) controller is designed and compared with traditional PID, and the parameters of the FOPID controller are optimized by PSO algorithm. In addition, a two-region LFC model was built to verify the integrated control strategy for electrolytic aluminum load participation in frequency modulation, which show that the FOPID controller optimized by the PSO algorithm has stronger robustness in the LFC.

The main objective of this paper is to solve the inverse convection heat transfer problems with particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortcoming of earlier convergence of standard PSO algorithms. The performance of EPSO is tested by some benchmark functions; it is shown that EPSO has a strong antilocal trap capability especially for high dimensional multimodal optimization problems. At last, EPSO is used to identify the unknown boundary heat flux in a channel flow. According to the computational results of four test problems, it is clear that the proposed EPSO algorithm is able to estimate the unknown heat flux accurately even when the input data contain measurement error.

An optimal control technique is proposed to address the problem of path tracking of an autonomous ground vehicle. This technique utilizes a Fractional Order Proportional Integral Derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the vehicle's inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a Particle Swarm Optimization (PSO) algorithm is used to optimize the FOPID controllers' parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLABSIMULINK software package. The newly obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.

The main objective of this paper is to implement the Fractional Order Proportional Integral Derivative (FOPID) controller for output voltage control of DC - DC Zeta converter. Implementing a distinct control technique for DC-DC converters has also been introduced with the commercial availability of integrated circuits (ICs) to carry out this task. The simulation of Classical Proportional Integral Derivative (PID) and Fractional Order PID (FOPID) control techniques for the control of Zeta converter and its characteristics of a dc-dc converter, where aspects such as the output voltage, required peak overshoot and rise time specifications are investigated using MATLAB/ Simulink model. Through simulation it is confirmed that the propounded FOPID controller gives better the transient response of the feedback system by using a significantly smaller control effort (i.e., less on-off switches), the Internal Absolute Error (IAE) of the variations of the output voltage is minimized and improves the strength of the DC - DC ZETA converter to variations in the input voltage. Artificial Bee Colony (ABC) algorithm offers fine tuning the factors of the FOPID controller.

The paper proposes a comparative study of the performance of the fractional-order PID(FOPID) controller with the conventional controllers. The parameters of the FOPID controller is estimated using particle swarm optimization (PSO) algorithm, and the values are compared with the values obtained through conventional optimization techniques. This paper aims to find out optimal settings for a fractional PIαDβ controller in order to achieve five different design specification for a closed system and also presents identification of fractional-order system using PSO. By obtaining optimal settings, fractional-order PID controller can enhance system control performance and robustness.

In the present work, the Differential Evolution (DE) algorithm optimized Fractional Order PID (FOPID) controller is proposed for Automatic Generation Control (AGC) of multi area multi source power system with parallel AC-DC link. The non-linearity such as time delay is included in the system model. The gains of the PID and FOPID controllers are optimized using an ITAE objective function. The superiority of FOPID controller over PID and Optimal controller is demonstrated by comparing dynamic performance of the same interconnected power system. Then sensitivity analysis is performed by changing the system parameters and loading condition from their nominal values. Finally proposed system is investigated under randomly varying load disturbances.

A pressurized heavy water reactor (PHWR) is a nuclear power reactor,that commonly uses unenriched natural uranium as its fuel and heavy water as its coolant and moderator. The PHWR has become an important component of the present day power generation industry. Working of a PHWR may often require reduction of nuclear power within a small finite time interval in accordance to the varying load conditions. This phenomenon is referred to as step back condition. Therefore, for the efficient working of the PHWR it is very important to have a control algorithm that has the capability to handle the step back condition very precisely. The fractional order control strategy has emerged as one of the very versatile and accurate control strategies in the recent times. The fractional order PID (FOPID) controllers are known to give very robust performance both in nominal as well as varying operating conditions. However, most of the design techniques available for the FOPID controllers are mathematically very lengthy and complex. Therefore, in this paper an FOPID controller design technique has been suggested using the concept of internal model control (IMCFOPID) for the PHWR under step back conditions. Further, the performance of the proposed controller has been compared with the conventional FOPID controller using the various integral error criterions. The proposed IMC-FOPID controller is found to give an improved performance as compared to the conventional FOPID controller.

Load frequency control (LFC) is a very critical problem of modern interconnected power systems in the new restructured scenario. It is further aggravated in the presence of communication time delay since communication channels while transferring the control signals between control center and power system are prone to time delay. In view of this, this paper presents a maiden approach for designing of fractional order proportional integral derivative (FOPID) controller to tackle the LFC problem of power systems in the presence of communication time delay. In this new proposed approach, PID gain parameters are obtained by using stability boundary locus (SBL) technique in the presence of communication time delay and fractional parameters are tuned utilizing Big Bang-Big Crunch (BB-BC) optimization algorithm. The proposed designed controller is implemented on the single-area power system consisting of non-reheated thermal turbines. The efficacy of the designed FOPID controllers is further investigated realistically in the presence of system parametric uncertainties. The thorough simulation results demonstrate the effectiveness and superiority of the designed FOPID controllers over other schemes available in the literature.