Efficient pressure regulation in nonlinear shell-and-tube steam condensers via a Novel TDn(1 + PIDn) controller and DCSA algorithm.

Steam condensers are vital components of thermal power plants, responsible for converting turbine exhaust steam back into water for reuse in the power generation cycle. Effective pressure regulation is crucial to ensure operational efficiency and equipment safety. However, conventional control strategies, such as PI, PI-PDn and FOPID controllers, often struggle to manage the nonlinearities and disturbances inherent in steam condenser systems.

Advanced control parameter optimization in DC motors and liquid level systems.

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.

Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization.

Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers and their modified versions are commonly used to maintain temperature stability by reacting quickly to deviations. In this study, the real PID plus second-order derivative (RPIDD) controller is introduced for the first time for industrial temperature control applications, which is a novel alternative that has not yet been investigated in the literature.

Multi-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modules.

Harnessing the potential of solar photovoltaic (PV) technology relies heavily on accurately estimating the model parameters of PV cells/modules using real current-voltage (I-V) data. Achieving optimal parameter values is essential for the performance and efficiency of PV systems, necessitating the use of advanced optimization techniques. In our endeavor, we introduce a multi-strategy improvement approach for the Runge Kutta (RUN) optimizer, a cutting-edge tool used for tackling this critical task in both single-diode and double-diode PV unit models.

A novel artificial intelligence based multistage controller for load frequency control in power systems.

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant.