Due to improved efficiency of solar photovoltaic (PV) systems, this article proposes a modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The MPO algorithm in question adopts a tracking approach that divides the power-voltage curve into four operational regions based on the estimated open-circuit voltage. Additionally, this algorithm enhances the maximum power point (MPP) tracking method by reducing unnecessary step-size calculations, focusing only on a 10% section of the power-voltage curve that contains the MPP. Consequently, the two regions located far from the MPP, below 90% of the power-voltage range, utilize a large fixed step-size to ensure swift tracking speed. Furthermore, in the regions close to the MPP, the remaining areas employ a similar tracking strategy as the adaptive P&O algorithm, aiming to achieve minimal steady-state oscillations around the optimal MPP. The performance of the proposed MPO algorithm is demonstrated by validating it against sinusoidal, ramp irradiance, and one-day (10 hr.) irradiance profiles using MATLAB/SIMULINK. The simulation results confirm that the proposed algorithm outperforms recently published techniques in terms of convergence speed, achieving the shortest time of 15 ms, and slightly higher tracking efficiency of the PV system under uniform irradiation, reaching 99.8%.

Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied.

This study proposes a novel and computationally efficient method for real-time identification and localization of power quality (PQ) disturbances in microgrids using dynamic Lissajous patterns formed by voltage and current waveforms. Each power disturbance—such as sag, swell, harmonic distortion, and transients—induces a unique geometric deformation in the Lissajous figure, which serves as a visual signature of the event. Key geometric and statistical features, including area, skewness, kurtosis, and centroid deviation, are extracted from these dynamic patterns to construct robust indices for classification. Adaptive thresholds for each feature are determined dynamically, enabling accurate event detection without prior knowledge of the microgrid configuration. Numerical analysis demonstrates that the proposed method achieves high precision in detecting and distinguishing PQ disturbances, even under overlapping or noisy conditions. For instance, the method records Euclidean distance values of 0.7071 for swell and 0.5657 for harmonic distortion, while maintaining zero deviation for pure signals, validating its classification accuracy. The system exhibits strong resilience to unstable grid conditions and is computationally lightweight, making it suitable for real-time deployment in embedded devices within microgrids. Comparative evaluation reveals superior performance in terms of speed, accuracy, and false positive minimization, highlighting the potential of dynamic Lissajous patterns as a powerful tool for advanced PQ monitoring in smart grid infrastructures.

This paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT’s directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g., voltage sags/swells) and high-frequency distortions (e.g., harmonics, transients). SALSA-based sparse optimization achieves an average signal-to-noise ratio (SNR) improvement of 12.8 dB, preserving essential transient structures, while MCA isolates fault-relevant morphological components for better interpretability. Extensive simulations on both synthetic signals and the IEEE 14-bus test system demonstrate detection accuracies of 98.6% for PQDs and 97.2% for cyber intrusions, including False Data Injection (FDI), Denial of Service (DoS), and Command Injection attacks. Each intrusion exhibits unique time-frequency scalogram signatures, which are effectively visualized using high-resolution, denoised 2D/3D spectrograms generated via adaptive Q-Factor Wavelet Transform (AQWT) and Short-Time Fourier Transform (STFT). Compared to baseline methods like STFT-only and DWT-SVM pipelines, the proposed NSCT-SALSA-MCA framework improves detection precision by 14–18%, reduces false positives by 22%, and remains robust under 30 dB noise and 20% data loss. Incorporating AI-driven anomaly detection and resilient state estimation further enables early flagging of compromised measurements, securing applications such as Economic Dispatch and Optimal Power Flow (OPF). The resulting scalograms provide interpretable visual insights, marking a significant advancement in smart grid monitoring with potential for real-time deployment at the edge.