Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Ensuring stable and optimal power system performance is paramount for reliable energy grid operation. This study comprehensively explores and compares four control strategies for Load Frequency Control (LFC) in interconnected power systems. Cases include operating without a controller (Case 1), employing Conventional Proportional-Integral-Derivative (PID) control (Case 2), utilizing Pole Placement (Case 3), and implementing Genetic Algorithm (GA)-optimized PID control (Case 4). Utilizing a dynamic system model with parameters from an isolated power station, the study observes system responses to sudden load changes and resulting frequency deviations. While Conventional PID (Case 2) demonstrates improvement, GA-optimized PID (Case 4) emerges as highly effective, show-casing minimal overshoot, rapid settling time, and enhanced peak values. The Pole Placement technique (Case 3) provides a comparative evaluation, revealing that GA-optimized PID excels in precise control. This analysis highlights trade-offs in control strategies, offering valuable insights for optimizing power system stability in interconnected grids and paving the way for further exploration of Intelligent control strategies.

In industrial contexts, optimizing power factor efficiency is of paramount importance. This work presents a comprehensive study that focuses on the enhancement of power factor efficiency in industrial systems through the implementation of an intelligent capacitor bank control strategy. The methodology employed utilizes real-world data collected from an industrial facility to assess power factor performance before and after the installation of capacitor banks. This approach encompasses motor modeling, algorithm development, and an economic feasibility evaluation. The research findings highlight the significant improvement in power factor, reduction in energy losses, and overall system performance optimization achieved through the proposed strategy, which includes the creation of different capacitor bank stages for achieving the desired KVAR and ensuring the optimal use of capacitor banks.

This study explores Load Frequency Control (LFC) through three distinct strategies, emphasizing the critical role of stable and optimal power system performance in ensuring reliable energy grid operation. In Case 1, without a PID controller, the system experiences characteristics such as a Rise Time of 0.4143 seconds, Settling Time of 6.9202 seconds, a notable Overshoot of 0.9709%, and a Peak Value of 60. Transitioning to Case 2 with Conventional PID control, improvements are evident in parameters like reduced Rise Time (2.3980 × 10 ⁻⁶ ), shorter Settling Time (4.6951 seconds), negligible Overshoot (0.0016%), and a Peak Value of 60.0001. In Case 3, employing Particle Swarm Optimization (PSO)-optimized PID control showcases exceptional performance with a remarkable Rise Time (3.0765 ×10 ⁻¹⁰ ), swift Settling Time (1.5093 seconds), minimal Overshoot (9.8068 ×10 ⁻⁵ %), and a Peak Value of 60.0001 at 2.9381 seconds. These outcomes highlight the efficacy of PSO-optimized PID control in achieving rapid response, minimal overshoot, and heightened stability, providing valuable insights for optimizing power system stability in interconnected grids.

Modern distribution networks are becoming increasingly active in nature owing to the rapid proliferation of Inverter-based-Resources (IBRs). Since these IBRs are capable of operating in multiple control modes, it is important for the utility operator to accurately identify the control modes and also estimate the associated parameters. This paper presents a threepronged data-driven approach for the real-time control mode identification of inverter-based resources (IBRs) and parameter estimation of IBRs and aggregated load. The proposed approach utilizes voltages, and power data at micro-PMU reporting rate at the node under observation for IBR mode identification and parameter estimation. A real-time implementation framework for the three-pronged approach having an adaptive window selection algorithm is developed to track the control mode along with parameters in real-time. The identified windows from an adaptive window selection algorithm are utilized by the threepronged approach for i) identification of control modes, ii) parameter estimation of IBRs, and iii) aggregated load parameter estimation. The paper also performs robustness analysis by considering different noise levels in the PMU measurements. Random forest, K-nearest neighbors, and Decision tree algorithms are utilized for each prong of the three-pronged approach with RF outperforming the other algorithms. The proposed approach developed in this work is tested in detail on the IEEE 13-node distribution test system to demonstrate its effectiveness.

This study delves into the integration of Machine learning (ML) methods with variable frequency drives (VFDs) to enhance speed control in single-phase and three-phase in-duction motors. Our innovative technology utilizes an Arduino microcontroller to govern the VFD circuit, integrating wireless communication with a Python-based ML face detector. By identi-fying specific faces, the system transmits pertinent speed control signals to the microcontroller for motor operation. This novel integration represents a pioneering approach to industrial op-timization, fostering increased energy efficiency through precise speed regulation and resultant cost savings. Furthermore, our automated methodology significantly extends the motor's lifespan by optimizing the power factor and alleviating mechanical stress. By seamlessly blending ML with VFD technology, our approach not only revolutionizes industrial motor control but also contributes to sustainable and efficient industrial practices. More-over, our study provides insights into the results obtained from the facial detection system. Each identified face corresponds to specific motor frequencies, enabling tailored speed adjustments. The mapped frequencies include 27 hertz, 120 hertz, and 160 hertz, each representing distinct motor speeds as illustrated by the associated waveform. This detailed analysis underscores the efficacy of our integrated approach in achieving dynamic and precise speed control based on facial recognition cues.