Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

Abstract

Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 Ki, 0.7741 Kp, and 0.1850 Kd. The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network.

Optimization of load frequency control using grey wolf optimizer in micro hydro power plants

“…The parameters examined consist of settling time, and peak overshoot as in work [20]. However, this work adds the peak undershoot parameter [21].…”

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

“…The inputs to the corrected values of the three parameters of the PID and the system deviation into the PID controller, and obtains the corrected values o the three parameters 𝐾 𝑝 , 𝐾 𝑖 , and 𝐾 𝑑 . In the iterative process, the fitness function is needed to calculate the fitness value o each individual, and then evaluate the advantages and disadvantages of the individual The Integral of the Time-weighted Absolute Error (ITAE) metric, which has the ad vantages of fast, smooth, and low overshooting, has been adopted by most of the litera tures [27][28][29], therefore, this paper introduces it into the performance evaluation of the precision fertilizer control system, which serves as an important reference index of the controller and as the fitness function of the BA optimization algorithm. The ITAE criterion In the iterative process, the fitness function is needed to calculate the fitness value of each individual, and then evaluate the advantages and disadvantages of the individual.…”

“…The ITAE criterion In the iterative process, the fitness function is needed to calculate the fitness value of each individual, and then evaluate the advantages and disadvantages of the individual. The Integral of the Time-weighted Absolute Error (ITAE) metric, which has the advantages of fast, smooth, and low overshooting, has been adopted by most of the literatures [27][28][29], therefore, this paper introduces it into the performance evaluation of the precision fertilizer control system, which serves as an important reference index of the controller and as the fitness function of the BA optimization algorithm. The ITAE criterion, i.e., the Time-Multiplied Absolute Error Integral Minimization criterion, can be expressed as follows:…”