Modelling, Analysis, And Integrated Control Of Electrical Complexes Under Variable Operating And Load Conditions

Automatic Generation Control Optimization of an Isolated Power System Including Renewable Energy Penetration

This paper presents an enhanced version of controllers of frequency and voltage of an isolated power system. The optimal tuning includes the load‐frequency control system integrator loop, the governor, and the automatic voltage regulator that includes the PID parameters and amplifier gain. With such parameters, two objective functions shall be minimized, which are the frequency deviation and voltage deviation. Modern power systems embed renewable energy sources in generation and/or demand sides. Therefore, two real‐life signals of wind and photovoltaic power sources have been included as external disturbances affecting the main quality variables, the frequency and voltage, of the conventional thermal unit. With such assumptions, the problem of load‐frequency control (LFC) and automatic voltage regulator (AVR) becomes more challenging since the power system is assumed to be isolated and without interconnection to release the excess renewable energies to neighboring regions. In addition, it is rare in the literature to include the LFC and AVR in the same system model and that was based on little coupling sensitivity between AVR and LFC. With all these considerations, an optimization problem has been successfully solved via two metaheuristic optimizers, the political optimizer (PO) and Gray‐Wolf Optimizer (GWO). The improvements have been notable by dynamic simulations over sufficient time to allow abrupt system conditions. There are also some remarkable differences between PO and GWO that have been discovered and reported during research.

Comparative Analysis of Conventional and Modern Control Techniques in PMSM Systems

Permanent Magnet Synchronous Motors (PMSMs) are indispensable components of modern industrial systems, electric vehicles and renewable energy infrastructures due to their high efficiency, power density, wide speed range and superior torque to inertia ratio. However, maximizing the dynamic performance and energy efficiency of these machines requires advanced control strategies that offer robust stability against variable loads, parameter uncertainties and external disturbances. Furthermore, the inherent nonlinearities of PMSMs, such as magnetic saturation and cross-coupling effects, complicate the design of precise tracking controllers. To address this, this study provides a comprehensive theoretical review of both fundamental and contemporary control techniques for PMSMs. Conventional methods, including Scalar Control (V/f), Field-Oriented Control (FOC) and Direct Torque Control (DTC), are systematically classified alongside recent adaptive, intelligent and robust control approaches. These methodologies are comparatively evaluated based on critical performance criteria, including dynamic response, torque ripple mitigation, computational burden, implementation complexity and parameter sensitivity. By synthesizing these existing frameworks, this research establishes a solid theoretical foundation for future control strategy development, ultimately guiding researchers in designing high-performance, next-generation motor drive architectures.

e-Prime Nexus of Electrical, Electronic, and Intelligent Engineering

Optimizing Control Performance and Reliability of Hybrid Renewable Systems with Fuzzy Logic Controller

The growing distribution of hybrid renewable energy systems (HRES) comprising photovoltaic (PV) and wind energy sources has raised serious challenges for power quality and system reliability due to irregular and nonlinear nature of renewable generation. The conventional Atom Search Optimization (ASO)-based controllers have shown effective optimization capability; however, their practical implementation is limited by high computational complexity, sensitivity to initialization, and limited compliance in rapidly changing operating situations. This paper presents an improved Fuzzy Logic Controller (FLC) for better control performance and reliability of grid-connected HRES. The controller is formulated on a rule-based fuzzy inference scheme to control the system dynamics without employing computationally expensive optimization procedures, thus enabling fast real-time response and robust operation under uncertain environmental and load conditions. The controller is validated in detailed MATLAB/Simulink simulations in different irradiance, wind speed and load disturbance. The simulation shows that the proposed FLC enhances the voltage stability, reduces the harmonic distortion, improves the dynamic response and reduces the computational burden when compared with the conventional ASO based controller. So, the presented approach is capable and computationally feasible for enhancing the operational reliability and power quality of grid-connected HRES.

Solutions Based on Active Disturbance Rejection Control Applied for Electric Drives—A Review

Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions.

Adaptive Hybrid Control Strategy for Grid-Connected VSCs Based on Model Predictive Control

With the widespread adoption of voltage-source converters for grid-connected new energy power generation, the transient stability of VSCs under high penetration of new energy has become a key issue in the construction of modern power systems. Grid-following (GFL) and grid-forming (GFM) controls each have their applicable scenarios and inherent limitations. Traditional hybrid synchronous control uses fixed parameters, making it difficult to adapt to complex and variable grid conditions. Therefore, this paper proposes a control method suitable for an adaptive hybrid synchronous architecture with grid-following and grid-forming dual characteristics, employing a model predictive control strategy to achieve dynamic adjustment of GFL and GFM control ratios in both the synchronization loop and the voltage loop. A simulation model was built in PSCAD/EMTDC to verify the effectiveness and adaptability of the proposed control method under various transient conditions, such as load changes and voltage sags. The simulation results indicate that the method can effectively suppress system frequency and voltage fluctuations, enhance the transient stability of VSCs, and provide a feasible control reference for practical engineering applications.