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In the proposed research article, the grey wolf optimization (GWO) technique is utilized to optimize the proportional (P) integral (I) derivative (D) (PID) controller/regulator gain parameters in three-area grid-connected power networks. The interconnected power plant covers thermal plants, hydro plants, and nuclear power plants. The proposed controller is used as a secondary controller in the power system to perform load frequency control (LFC). Under unforeseen load conditions, the system frequency deviates from the norm. To control and stabilize this oscillation, the LFC system is used. During the investigation, a step load perturbation of one percent (SLP 1%) is applied for the analysis of the thermal power plant. The response of the suggested optimization technique-designed regulator performance is equated with the genetic algorithm (GA)-tuned, particle swarm optimization (PSO)-tuned, and ant colony optimization (ACO) technique-tuned PID regulator response. The performance response is evidence that the GWO-based PID regulator provides a regulated response with minimal time-domain specification parameters (settling time, peak shoots) over other tuning methods. The effectiveness and robustness of the improved response of the suggested technique-optimized controller are verified with various load values (1%, 2%, and 10% SLP) and nominal parameter (R, Tp, and Tij) variations (± 25% & ± 50%) from its nominal value. [ABSTRACT FROM AUTHOR]

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learningbased optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral timesquared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multipower microgrid electrical framework, specifically in terms of effective load frequency control. [ABSTRACT FROM AUTHOR]

In this article, the optimal gain value of classical controller gain values is obtained by using different performance indices in Automatic Generation Control. These are interconnected by the three areas of a hydro-thermal power system. The thermal and hydro areas are incorporated with a reheat turbine and a mechanical governor, respectively. The current article was created to select a suitable technique for the tuning of controller gain, when 1% of a step load is given to the thermal area (Area 1). The performance of several controllers, such as Integral (I), Proportional-Integral (PI), and Integral- Derivative (ID) were evaluated and were compared to the cases of with and without Generation Rate Constraint (GRC) non-linearity. The classical controller gain value optimization was performed using the Integral Time Square Error (ITSE), Integral Square Error (ISE) and Integral Time Absolute Error (ITAE) performance indices. The minimum optimal value of controller gain normally offers better dynamic response. The obtained results established that the optimization based on the ITAE-PI controller always guarantees superior dynamic performances compared to other indices and controllers. [ABSTRACT FROM AUTHOR]

This work presents the performance evaluation of different commonly used objective functions in load frequency control (LFC)/automatic generation control (AGC) of single/multi-area non-reheat thermal power systems. The commonly used objective functions in LFC/AGC of power system are integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The objective functions are used to tune proportional-integral-derivative (PID) controller values in single-area, two-area, three-area and four-area power systems with one percent Step Load Perturbation (1% SLP) in area 1. The gain values of proportional controller gain (Kp), integral controller gain (Ki) and derivative controller gain (Kd) values are tuned by using artificial intelligence (AI)-based ant colony optimization (ACO) technique with aforementioned different objective functions. The cumulative performance of the investigated power systems with different objective function-based ACO-PID controller response reveals that the objective function performance is varied with the increase in the power system size. The performance of power systems is measured by considering time domain specification analysis, namely the settling time, undershoot and peak overshoot. The results established that the objective functions performance is diverse based on the power system size. In addition, the ITSE-based PID controller response guarantees minimum peak undershoot in all power system’s responses compared to ISE-, ITAE- and IAE-based controller response. [ABSTRACT FROM AUTHOR]

This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional-integral-derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain ( K ), integral gain ( K ) and derivative gain ( K )] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect. [ABSTRACT FROM AUTHOR]