In a grid, the choice of the point of common coupling (PCC) does not solely rely on the voltage level alone but also conjointly depends on the grid strength for many explicit purposes. Nowadays, the affinity of low SCR grid connections has become a crucial thought once it involves the integration of wind generation plants (WPPs). Since the quality of wind resources is a critical issue, these plants are usually placed in remote areas with a sophisticated potential of wind flow. These remote areas are typically less inhabited, where the grid does not perpetually always have to be sturdy. Moreover, the exceeded power demand loading and higher wind penetration affect the generation, transmission, and distribution utilities by permitting the flow of unbalanced voltages and currents in the power system. Therefore, the quality of transmitted power is becoming a crucial facet of distributed energy generation units. In this paper, a permanent-magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. It discusses a solution, which provides the low voltage ride through (LVRT) provision by the suppression of DC link overvoltage and active power limitation during an asymmetrical grid fault. With improved back-to-back converter control, the machine side converter (MSC) is employed to control the DC-link voltage. Furthermore, the grid side converter (GSC) is used to implement the active/reactive current injection according to the outlined limits. The need for external hardware is eventually avoided, which is typically required to dissipate the additional energy generated during a grid fault. Hence, it is proven to be an affordable solution.

Modern power distribution networks are becoming cyber physical systems due to the addition of more advanced metering infrastructure (AMI). This has introduced new vulnerabilities to cyber threats, particularly false data injection (FDI) attacks. These attacks compromise the integrity of power consumption data, leading to financial losses, operational inefficiencies, and grid instability. Rule-based techniques and traditional machine learning models are two examples of traditional anomaly detection methods that often have problems. Often, these methods generate an excessive number of false alarms, struggle to adapt to new attack patterns, and perform poorly in large-scale deployments. This research suggests a robust anomaly identification framework (AIF) that uses an autoencoder (AE) for feature transformation and a multi-layer perceptron (MLP) to identify anomalies in AMI integrated with smart grids. The proposed approach first applies synthetic features extraction inspired by real-world smart meter capabilities and transforms the dataset using a denoising AE. MLP assisted in the classification to detect multiple FDI attack types with improved accuracy and reliability. Numerous experiments have been performed, and the results indicate that the suggested method works better than popular methods like correlation analysis, techniques based on clustering, and standard outlier identification algorithms. Compared to baseline methods, the proposed technique improves detection accuracy by up to approximately 25%, reduces false positives, and enhances the system’s ability to generalize across different cyberattack strategies. The proposed work computes seven different types of criterion matrices to verify the effectiveness of finding anomalies. The overall average results include mean squared error (0.0793), accuracy (92%), F1-Score (92%), recall (91%), specificity (94%), area under the curve (97%), and mean average precision (96%). These findings accentuate the potential of the proposed AIF performance in fortifying smart grid cybersecurity.

This paper is based on improving power flow management to attain flexibility in distributed ac transmission using interconnected power systems. A photovoltaic source-based thermal connected power system is developed to meet the energy demand. In northern areas during heavy snowfall seasons to maintain continuity and reliability of power flow in transmission lines many variables/constants are essential to regulate line power. Line dicing is the major constraint in northern areas which can increase the losses in the transmission lines. Furthermore, this can cause breakage of line conductors. Therefore, it is the need of the hour to get rid of line dicing, for this invention of FACTS devices has allowed us to control the switching of the bundled transmission lines. These devices are electromechanical and also composed of passive (power electronic) elements which allow us to manage the power flow in a line by varying the impedance of the line. In this paper impedance modulator (LIM) is implemented, in which back to backline impedance modulator was configured to regulate line impedance for the stability of power flow, line monitoring for line dicing, and voltage/current regulation during critical scenarios.