Power quality disturbances (PQDs) have become an increasingly critical concern in modern power systems due to the rising integration of renewable energy resources, widespread use of power-electronic interfaced loads, and the growing complexity of cyber-physical grid infrastructures. These evolving conditions have introduced new sources of variability, uncertainty, and vulnerability into power networks, making it significantly more challenging to maintain voltage stability, waveform purity, and overall system reliability. Traditional deterministic methods for disturbance detection and classification are no longer sufficient to address the nonlinear, nonstationary, and high-dimensional nature of contemporary PQD phenomena. As the operational landscape grows more dynamic, there is a pressing need for analytical frameworks that can adaptively learn, generalize, and respond to diverse disturbance scenarios in real time. This study provides a comprehensive examination of recent advancements in PQD research, emphasizing the evolution toward hybrid analytical frameworks that integrate advanced signal processing, machine learning, and metaheuristic optimization. The literature demonstrates that hybrid models—such as continuous wavelet transform (CWT) combined with convolutional neural networks (CNNs), Stockwell transform integrated with kernel-based extreme learning machines (ELMs), and metaheuristic-optimized classifiers—significantly enhance detection accuracy, robustness against noise, and adaptability to varying operating conditions. These hybrid systems leverage the strengths of each component: signal transforms enrich the representational quality of PQD features, deep architectures facilitate automatic feature learning, and optimization algorithms refine model parameters to achieve optimal performance across complex and uncertain environments. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE), and hybrid variants have proven particularly effective for feature selection, classifier optimization, system-level enhancement, and mitigation strategies. Their ability to handle large, multimodal, and nonlinear search spaces makes them especially suitable for modern PQD challenges, where disturbance signatures may overlap, evolve over time, or be obscured by noise introduced by renewable energy fluctuations. Furthermore, recent work demonstrates the potential of ensemble-based and deep learning models optimized with metaheuristics to outperform conventional approaches in both accuracy and computational efficiency, thereby advancing the state-of-the-art in PQD detection technologies. Additionally, the convergence of PQD analysis with cybersecurity highlights an emerging and increasingly urgent research frontier. As smart grids become more interconnected and reliant on information and communication technologies, they face heightened risks from cyber-attacks capable of inducing, masking, or mimicking PQ disturbances. Such adversarial actions pose significant threats to grid stability, integrity, and operational safety. Metaheuristic-enhanced deep learning methods have shown promise for cyber-physical intrusion detection by enabling classification models to identify subtle, intentionally disguised anomalies within PQ data streams. This hybrid approach provides a pathway toward resilient PQ monitoring frameworks that are capable of learning and adapting to evolving attack strategies. Despite notable advancements, several key challenges persist. First, the lack of standardized real-world datasets limits the generalizability and reproducibility of PQD research, particularly within renewable-dominated or cyber-physical grid environments. Second, the high computational demands of hybrid models hinder their deployment in real-time or resource-constrained settings, calling for advancements in lightweight architectures, model compression, and edge-intelligent PQD systems. Third, the field lacks unified frameworks that integrate PQ detection, classification, and mitigation with operational decision-making, economic constraints, and regulatory requirements. Finally, adaptive cyber-physical intrusion detection frameworks remain underdeveloped, especially for zero-day attacks and data-limited conditions. Overall, this review underscores the necessity of holistic, intelligent, and scalable approaches to PQD management. It identifies critical directions for future research, including real-time system integration, computationally efficient hybrid architectures, multiobjective optimization strategies, and robust cybersecurity-aware PQD analytics. These innovations are essential for achieving resilient next-generation smart grids capable of maintaining high power quality under increasingly dynamic, decentralized, and uncertain operating conditions.
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Safa S. Abdul-Jabbar
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https://doi.org/10.54216/MOR.050202