Most cited article - PubMed ID 39609641
A novel artificial intelligence based multistage controller for load frequency control in power systems
Temperature control in continuous stirred tank heater (CSTH) systems is essential for ensuring energy efficiency, safety, and product quality in industrial processes. However, the nonlinear dynamics and external disturbances make conventional proportional-integral-derivative (PID) control inadequate for reliable operation. This study presents a novel two-degrees-of-freedom PID (2DoF-PID) controller optimized using the quadratic interpolation optimization (QIO) algorithm to enhance CSTH temperature regulation. The QIO-based approach allows independent tuning for setpoint tracking and disturbance rejection, overcoming the limitations of classical PID controllers. Extensive nonlinear time-domain simulations, reference tracking, and disturbance rejection tests demonstrate the superior performance of the proposed controller in terms of reduced overshoot, faster settling time, and minimal steady-state error. Furthermore, comparative evaluations with traditional tuning methods (Murrill and Rovira) and several state-of-the-art metaheuristic optimizers (DE, PSO, FLA, MGO) validate the effectiveness and robustness of the QIO-optimized strategy. This work introduces a pioneering application of the QIO algorithm in industrial temperature control, offering a scalable and cost-efficient solution for complex nonlinear systems.
The rapid growth in power demand, integration of renewable energy sources (RES), and intermittent uncertainties have significantly challenged the stability and reliability of interconnected power systems. The integration of electric vehicles (EVs), with their bidirectional power flow, further exacerbates the frequency fluctuation in the power system. So, to mitigate the frequency & power deviations as well as to stabilize the power system integrated with distributed generators (DGs) and EVs, robust & intelligent control strategies are indispensable. This study dedicates a novel Fuzzy-Sliding Mode Controller (FSMC) utilized for load frequency control (LFC). First, the dynamic response has been evaluated by using a Sliding Mode Controller (SMC), showcasing its robustness against external disturbances and parameter uncertainties. Second, to enhance the performance, fuzzy logic is integrated with SMC, leveraging its adaptability to create the FSMC controller. This FSMC has achieved the superiority by handling non-linearities, communication delays and parameter variations in the system. A significant contribution like the design and tuning of the controllers using a Modified Gannet Optimization Algorithm (MGOA) has been established. The potential of MGOA over GOA has been corroborated by convergence speed and precision through benchmark functions. Furthermore, the paper extensively analyzes the impact of EV integration to the frequency and tie-line power dynamics under varying regulation capacities and uncertain operating conditions. Comparative studies demonstrate that the MGOA-tuned FSMC achieves faster settling times, reduced overshoot, and improved stability metrics compared to conventional and state-of-the-art methods. Finally, the MATLAB-based simulation results are validated through real-time implementation on the OPAL-RT 4510 platform, confirming the robustness and practicality of the proposed methodology in addressing modern power system challenges involving high renewable penetration and EV integration.
In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO's superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.
- Keywords
- DC motor speed regulation, Liquid level control, Mountain Gazelle optimizer, PID controller, Parameter estimation,
- Publication type
- Journal Article MeSH
