Load Perturbation Research Articles

Frequency control of a multi-microgrid system using a muti-stage controller in an isolated mode

The interconnection of numerous standalone MGs leads towards the establishment of an isolated MMG system. The MMG system is a complex nonlinear system that creates functioning decline as a result of inadequate dampening under the unanticipated variability in the load demand and the generated power from sources of renewable energy catalogue. Nonlinear nature also comes in the system due to the variations in the system parameter and dynamically altering loading conditions. So, in the present work, a standalone MMG system with two areas system through renewable penetration, the operation of QOHSA aimed at the gain optimisation of MS-PID controller is exploited for limiting variation in power and frequency due to generation and load demand perturbation. The practicality of the considered controller (i.e. MS-PID) is unearthed by comparing the dynamic characteristics of isolated MMG systems along with other controllers like I, PI and PID controllers (i.e. classical controllers). The MS-PID controller configuration sustains the deviation of frequency under ±0.00249 Hz and ±0.0583 Hz for step and random change in load demand, respectively. The sensitivity analysis is executed to present the suitability for the extensive adaptations in the magnitude of MG parameters along with the circumstances of step/random load perturbation.

A novel fuzzy assisted sliding mode control approach for frequency regulation of wind-supported autonomous microgrid

Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.e., fuzzy logic-based sliding mode control (F-SMC) utilizing a proportional-integral-derivative (PID) type sliding surface to regulate the frequency of a wind-diesel generator-based ATMG system. A dynamic structure of the wind generator is designed to participate in the frequency support of the considered plant. The mastery of the F-SMC is analyzed over the conventional SMC (C-SMC) under load perturbation. This study used the artificial gorilla troop optimization (GTO) technique to tune the F-SMC parameters. The effectiveness of the GTO-tuned F-SMC frequency regulation (FR) scheme is compared with well-established particle swarm optimization (PSO) and grey wolf optimization (GWO) approaches under various scenarios such as load perturbations, governor dead band (GDB), generation rate constraint (GRC), higher/lower dimensions of ATMG, and wind speed variations. Finally, the proposed GTO-based F-SMC approach has been validated upon a standard IEEE-14 bus system and compared with recent techniques.

Topology synthesis of coupled inductor based four port DC-DC converter for multi-input PV-battery application with autonomous mode selection

Multiport converters are the most reliable and integral component for latest renewable source integration with multiple inputs. This article is one among the kind, which proposes a novel Coupled Inductor based Four Port topology Multiport Converter (CI-FP-MPC) for integrating multiple PV sources with different voltages. The adoption of coupled inductor contributes an increased voltage gain with reduced stress on the switches and diodes. In addition, reduced number of switches and diodes for a four-port topology provides an increase in efficiency due to its reduced switching and conduction losses. Also, soft switching techniques for zero-voltage-switching in switches and zero-current-switching in diodes transfers a smooth commutation between the mode transients and operating intervals. The proposed converter operates with two PV sources with battery as a source and load as well, paving to two modes of operation viz., TISO and DIDO mode respectively. The proposed topology is derived from the conventional high gain boost converter with an add-on of autonomous sensing and control logic to swap and control between the modes. To evaluate and validate its performance, the converter is developed in experimental setup with a power capability of 750 Watts. The control logic and duty cycle adjustments are supervised in Hardware-In-Loop (HIL) arrangement using dSPACE MicroLabBox. The results show increased efficiency, improved voltage gain and robust mode transition during load perturbations.

Implementation of machine learning tool for continued process verification of process chromatography unit operation.

Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R2 values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0.

A Hybrid Optimization Algorithm Based on Cascaded (1+PI)-PI-PID Controller for Load Frequency Control in Interconnected Power Systems

Electric power generation is crucial for electrical systems, and load frequency control (LFC) ensures system synchronization and reliability. Modern systems use well-designed controllers, with selecting the right type and design approach being essential for a good LFC controller design. This study introduces a cascaded (1+PI)-PI-PID controller optimized using the improved Artificial Bee Colony-Particle Swarm Optimization (IABC-PSO) algorithm for LFC in interconnected power systems, aiming to address complex challenges associated with LFC in modern power systems. The research examines a two-area power system model with hydro and thermal power plants and a capacitive energy storage (CES) device. It proposes a (1+PI)-PI-PID controller structure that integrates traditional PID controllers, enhancing performance and debugging capabilities. The study uses integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral time square error (ITSE) as objective functions. The IABC-PSO algorithm introduces a novel decision block to increase scout bee (SB) occurrences to 10% for each employed bee (EB), and a new control search phase to optimize SBs' exploration capabilities in a controlled manner, enhancing their efficiency. The algorithm's performance was assessed using four benchmark functions, revealing superior results compared to the Hybrid ABC-PSO algorithm. The study examined six experimental configurations of the controller, varying the sequence of (1+PI), PI, and PID components. The proposed strategy was tested under 1% and 5% step load perturbations in area-1 and area-2 to evaluate dynamic performance. This study conducts a sensitivity analysis by adjusting hydro parameters within a ±25% range, and considers generation rate constraint (GRC) and boiler dynamics (BD) in a two-area reheat thermal-hydro-power system model for reliability and credibility. The proposed IABC-PSO algorithm outperforms traditional methods (ABC, PSO, and hybrid ABC-PSO, WOAw, GWO, hSFS-LUS, and FA) in optimizing parameters, achieving shortest settling times and minimal ITAE values. Its performance is evaluated under random step load perturbations (SLP) and its internal robustness is demonstrated through stability analysis using the Lyapunov method. In conclusion, the hybrid IABC-PSO algorithm-based cascaded (1+PI)-PI-PID controller offers a robust and efficient solution for LFC in interconnected power systems. It ensures enhanced performance, stability, and adaptability, making it a valuable contribution to modern power system management.

Cascaded Integral Minus Tilt Integral Derivative With Filter for Frequency Stabilization of V2G/G2V Enabled Hybrid Microgrid

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid.

Validation of assessment methods for creep crack growth rates in irradiated components from Nimonic PE16 Tie-Bar tests

Creep crack growth is a life-limiting failure mechanism in high-temperature metallic components. The creep crack growth response is linked to the underlying creep deformation and failure properties, such as creep ductility. Irradiation effects such as helium (He) embrittlement in high-temperature reactor components trigger decreases in creep ductility. Creep crack growth (CCG) data obtained from unirradiated and irradiated Nimonic PE16 tie-bars confirm the accelerated crack growth rates under neutron irradiated conditions. The exact same data were also used to validate the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2, developed from unirradiated material data. To do so, the mechanics-based creep crack growth law of Nikbin-Smith-Webster (NSW) is adopted to estimate the crack growth rates in unirradiated PE16, and then adjusted through changes in bulk mechanical properties in the irradiated case to estimate the changes in crack growth rates. The estimated ȧ-C∗ trend was employed as part of the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2. Evaluation of results via the procedure and the estimated trends confirm the validity of the approach to predict increases in creep crack growth in neutron irradiated components, as observed in experimental data. The approach yields moderately conservative predictions for crack growth rates, and accurately captures the relative increase in creep crack growth rates for the irradiated case. The predictions were independently validated by predicting the C*-values expected in the loading configuration for a given recorded crack growth rate ȧ. The validation of the defect assessment method introduced in Code Case N-934 against both unirradiated and irradiated PE16 CCG data provides a practical path to its implementation in metallic components across high-temperature reactor designs. This is particularly useful where crack growth data in irradiated material may not be readily available. The technique also allows for propagating uncertainties in crack growth predictions which stems from compounding effects such as variability in unirradiated properties, degree of irradiation and corresponding embrittling effects, crack size and load perturbations. The relevance of mechanical properties accurately depicting aspects of the He embrittlement process for practical applications in plant and the importance of creep crack growth testing are discussed.

Robust topology optimization of multi-material structures with overhang angle constraints using the material field series-expansion method

This paper presents a novel topology optimization method to enhance the manufacturability and practicality of multi-material structures. Traditional topology optimization techniques excel at generating high-performance designs, but the performance of these designs is often compromised due to the load perturbations encountered in real-world applications or post-processing for additive manufacturing (AM). This work addresses this challenge by incorporating three key features directly into the optimization process. First, the method integrates overhang angle constraints, ensuring designs are inherently manufacturable with AM. Second, it utilizes the material field series-expansion method for a significant reduction in design variables while simultaneously providing a clear and efficient description of material distribution within the optimized designs. Finally, to enhance design robustness under real-world scenarios with uncertain loads, the method employs Monte Carlo simulation, which is decoupled from the optimization process to reduce the computational cost. Numerical examples demonstrate the efficiency and effectiveness of the proposed method in generating robust designs of multi-material structures well-suited for AM. The proposed framework offers a promising pathway for the realization of complex, high-performance composite structures.

Backstepping-Based Quasi-Sliding Mode Control and Observation for Electric Vehicle Systems: A Solution to Unmatched Load and Road Perturbations

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate.

Perturbation approaches to achieving diverse and competitive designs in topology optimisation

Utilising topology optimisation to generate diverse and competitive structures enables designers to create elegant and efficient designs according to their aesthetic intuition and functional needs. This study proposes load and support perturbation approaches based on the bi-directional evolutionary structural optimisation (BESO) method to achieve diverse designs. During the optimisation process, noise is introduced to the sensitivity calculations by randomly perturbing the load angles or material properties near the supports, leading to a variety of local optima. Numerical results demonstrate that the proposed approaches are capable of creating designs with similar stiffness but distinct geometries. In addition, the diversity of the obtained solutions can be controlled by utilising different perturbation functions. This work is of significant practical importance in both architecture and engineering, where multiple design options of high structural performance are in demand.

COMPARACIÓN DEL RENDIMIENTO DE VARIOS ENFOQUES DE SOFT COMPUTING EN EL CONTROL DE LA FRECUENCIA DE CARGA PARA SISTEMAS ELÉCTRICOS INTERCONECTADOS QUE COMBINAN FUENTES TÉRMICAS-HIDRÁULICAS Y TÉRMICAS-DIÉSEL

Interconnected power systems offer a solution for meeting the increasing demand for reliable and efficient electricity supply. However, maintaining stability in such networks, especially in the face of frequency fluctuations, presents a significant challenge. Effective load frequency control (LFC) optimization is vital for ensuring stability by mitigating the impact of these fluctuations resulting from various disturbances. This study introduces PID LFC optimization strategies applied to a thermal-hydro and thermal-diesel interconnected power system. Six optimization methods, including ACO, PSO, GA, TLBO, DE, and CIO, are explored using four cost functions. Random step load perturbations are applied to test the response consistency of the LFCs. Initial findings indicate that recently developed CIO outperforms other algorithms in achieving desired outcomes while maintaining competitive computational expenses. Specifically, the CIO-ITAE optimized LFC demonstrates improved resilience in addressing challenges in the interconnected power system. Keywords: Load Frequency Control, LFC, Ant Colony Optimization (ACO), Differential Evolution (DE), Teaching Learning based Optimization (TLBO),Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Cohort Intelligence Optimization (CIO).

A novel weighted exponential sliding mode controller with a modified reaching law for the frequency regulation of a renewable integrated isolated AC microgrid

This paper investigates the development and application of a novel hybrid controller through novel sliding surface and novel reaching law, Weighted Exponential Sliding Mode Control with Modified Reaching Law (WESMC-MRL), for frequency regulation in an isolated Microgrid (MG) with intermittent Renewable Energy Sources (RESs) such as solar and wind. Major contributions include the analysis of frequency regulation of isolated MG controlled by WESMC-MRL under various load perturbations. The performance of WESMC-MRL is compared with conventional Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Integral Sliding Mode Control (ISMC) controllers. Outcomes indicate that the WESMC-MRL effectively regulates the MG frequency with minimal chattering, and outperforms LQR SMC, and ISMC in terms of settling time, overshooting, and undershooting in frequency deviation. With the WESMC-MRL the maximum frequency deviation is found only 0.00724 Hz and undershoot is negligible. The proposed controller is also robust against RES integration variability, disturbances, and nonlinearities making it a promising solution for reliable and stable MG operation.

2DOF-PID-TD: A new hybrid control approach of load frequency control in an interconnected thermal-hydro power system

The Load Frequency Control (LFC) scheme, with its primary aim being the maintenance of uniform frequency, has been a heavily researched topic for decades. Achieving a consistent frequency necessitates a delicate balance between load demand and power generation. Researchers strive to find an optimal solution within the LFC domain—one that can effectively withstand drastic load fluctuations. Despite a plethora of efforts, the LFC dilemma remains unresolved, complicated by factors such as dwindling demand-supply and the rapid integration of renewables. Furthermore, the lack of innovation in controller structure design exacerbates the complexity of solving modern LFC problems. Consequently, a robust control approach capable of handling uncertainties while simultaneously regulating system frequency becomes crucial. In light of this, we propose a novel hybrid control architecture called 2DOF-PID-TD. This architecture combines Two Degrees Of Freedom Proportional-Integral-Derivative (2DOF-PID) and Tilt-Derivative (TD) controllers. To optimize the proposed controller, we employ a metaheuristic called the Artificial Gorilla Troops Optimizer (AGTO), which mimics the social behavior and intelligence of gorilla troops. The proposed approach is analyzed in a realistic multi-area multi-source hydro-thermal system, accounting for nonlinearities, random load perturbations, and system parametric uncertainties. Experimental results, when compared with current state-of-the-art optimization algorithms and traditional controller structures, demonstrate the prowess of our approach in terms of precision, robustness, and resilience.

A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm

This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the considered two-area power systems under variable perturbation in load and changing of system parameters in order to evaluate its effectiveness. The suggested hybrid algorithm-based fuzzy-PID controller is compared with various widely used control methods in the literature such as PID controller and algorithms such as PSO and GWO in order to evaluate its effectiveness and its robustness. Through the simulations carried out on MATLAB/Simulink, the proposed PSO-GWO fuzzy-PID and the objective function exhibit improved performance, achieving minimal objective values. The proposed technique proved to be quite powerful tool in the resolution of problems related to electrical power systems, particularly in load frequency control.

Pelican Optimization Algorithm-Based Proportional–Integral–Derivative Controller for Superior Frequency Regulation in Interconnected Multi-Area Power Generating System

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller’s robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, Tp, and Tij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively.

LMI-Based MPC Design Applied to the Single-Phase PWM Inverter with LC Filter under Uncertain Parameters.

This work proposes a design methodology for predictive control applied to the single-phase PWM inverter with an LC filter. In the design, we considered that the PWM inverter has parametric uncertainties in the filter inductance and output load resistance. The control system purpose is to track a sinusoidal signal at the inverter output. The designed control system with an embedded integrator uses the principle of receding horizon control, which underpinned predictive control. The methodology was described by linear matrix inequalities, which can be solved efficiently using convex programming techniques, and the optimal solution is obtained. MATLAB-Simulink and real-time FPGA-in-the-loop simulations illustrate the viability of the proposed control system. The LMI-based MPC reveals an effective performance for tracking of a sinusoidal reference signal and disturbance rejection of input voltage and load perturbations for the inverter subject to uncertainties.

Non-cooperative games to control learned inverter dynamics of distributed energy resources

We propose a control scheme via a non-cooperative linear quadratic differential game to coordinate the inverter dynamics of Distributed Energy Resources (DERs) in a microgrid (MG). The MG can provide regulation services in support to the upper-level grid, in addition to serving its own load. The control scheme is designed for the MG to track a power reference, while each DER seeks to minimize its individual cost function subject to learned inverter dynamics and load perturbations. We use a nonlinear high-fidelity model developed by Sandia National Laboratories to learn inverter dynamics. We determine a Nash strategy for the DERs that uses state estimation of a Loop Transfer Recovery. Results show that the control scheme enables savings up to 9.3 to 208 times in the DERs objective cost functions and a time-domain response with no oscillations with up to 3 times faster settling times relative to using droop and PI control.

Generation Control of Multi-Area Multi-Source Power System using Fire-Fly Algorithm

This article presents automatic generation control (AGC) of an interconnected three area power system having different production sources such as hydropower, thermal and gas power plants. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. An attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more robust and powerful evolutionary computational technique Firefly Algorithm (FFA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. The system dynamic performances are studied with 1% and 2% step load perturbation in Area1. Sensitivity analysis reveals that the FFA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters.

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

A hybrid optimization algorithm for improving load frequency control in interconnected power systems

The objective of this study is to develop an algorithm named Modified Artificial Bee Colony and Particle Swarm Optimization (MHABC-PSO) to address load frequency control (LFC) challenges in a two-area interconnected power system. The proposed MHABC-PSO algorithm is designed with two key modifications to enhance global exploration capability and improve convergence speed. Hence, a decision block is introduced in the employed bee (EB) phase incorporating a control parameter “limit” to allow each candidate solution (CS) to explore itself up to the “limit” value and boost local exploration. In addition, an introduction of a novel selection mechanism utilizing heuristic information (η) in EBs phase guides the onlooker bee (OB) phase to select better solutions based on success and failure history, thus promoting exploitation and reducing biased exploration. To address the efficacy of the proposed algorithm at the system level, three different two-area power systems are studied, incorporating various complexities, linearity, and non-linearity such as thermal-hydro, reheat thermal, and thermal-hydro-gas turbine configurations with HVDC link, SSSC, and CES. The algorithm is applied to optimize four objective functions (i.e. ITAE, IAE, ISAE, and ITE). The fitness function maximizes controller gains by utilizing the integral time multiplied absolute error (ITAE). Other objective functions like IAE, ISAE, and ITE are employed for a comprehensive analysis. Evaluation of MHABC-PSO effectiveness is conducted through ITAE values, peak deviations, and settling times of frequency and power deviations in different two-area systems. Results demonstrate that MHABC-PSO settles the system more quickly with zero steady-state error under step load perturbations (SLPs) of 1% and 2%. Comparative analysis with ABC, PSO, SFLA-TLBO, and OHABC-PSO using ITAE index and controller settling times shows the superiority of MHABC-PSO for LFC analysis. In conclusion, the proposed MHABC-PSO algorithm proves to be an efficient and effective solution for LFC, outperforming other algorithms in terms of the specified objective functions and exhibiting rapid convergence and optimal gains for controllers, effectively addressing LFC issues by combining exploration and exploitation techniques.