# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
###### Abstract  
The rapid growth of power electronics-based devices, such as electric vehicles and renewable energy systems, has introduced nonlinear components into power systems, generating high-frequency harmonics that distort current and voltage waveforms. These distortions pose significant risks to the stability of power grids, potentially leading to equipment malfunctions, reduced efficiency, and even system failures. To address these challenges, advanced harmonic detection, suppression, and estimation techniques are required to ensure the reliable operation of modern power systems. This paper comprehensively reviews the most widely used methods for managing harmonic distortions, focusing on recent harmonic detection, suppression, and estimation advancements. Key techniques, such as Fourier analysis and wavelet transforms, are compared alongside emerging machine learning-based approaches and adaptive filtering methods, which offer enhanced accuracy in real-time and dynamic environments. Additionally, advancements in harmonic suppression technologies, including passive, active, and hybrid filtering, are discussed for their effectiveness in mitigating harmonic impacts. Furthermore, the paper explores harmonic aggregation techniques that assess the cumulative impact of multiple harmonic sources and innovative estimation models that improve harmonic quantification under complex grid conditions. With the growing integration of renewable energy and electric vehicles, this review highlights the importance of advanced harmonic management strategies to ensure the safety, efficiency, and long-term stability of power systems. harmonic detection; harmonic suppression; harmonic sensitivity; estimation models +
Footnote †: journal: Applied sciences  
it possible to analyze harmonic content more effectively, allowing for better system monitoring and protection. Harmonic suppression focuses on reducing or eliminating the effects of harmonic distortion. In modern power grids, hybrid filter technologies, which combine passive and active filters, offer a more effective approach [7; 8]. Passive filters have long been used for harmonic suppression but have limitations, particularly in their adaptability to changing conditions. Active filters, on the other hand, can adjust dynamically, and when combined with passive filters, they provide more efficient suppression solutions for complex and evolving power systems. Harmonic aggregation assesses the cumulative impact of multiple harmonic sources. The complexity of modern power systems, with various renewable energy sources and electric vehicles contributing to harmonic distortion, requires more precise prediction tools [9]. Data-driven models and AI-based optimization techniques have improved the ability to predict overall harmonic distortion in these systems, providing engineers with better insight into the performance and stability of power grids. Harmonic sensitivity analysis evaluates a system's response to variations in harmonic content. As power systems grow more interconnected and diverse, this analysis has evolved to incorporate probabilistic methods that account for uncertainties. This is particularly relevant in future smart grids, where unpredictability in energy demand and supply can introduce new harmonic challenges. By considering the probabilistic nature of these variations, engineers can better design systems that are robust and adaptable. Harmonic estimation, which quantifies the harmonic content, has also advanced significantly. Traditional estimation methods have been supplemented by techniques like Kalman filtering and neural networks, which offer higher accuracy in dynamic grid conditions. These advanced estimation methods allow for more precise forecasting of harmonic behav  
Figure 1: Organization needs and methods for modern power system. ior, improving system reliability and enabling proactive measures to mitigate harmonic effects before they impact grid stability.