# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 5 Harmonic Estimation Models  
Current harmonic estimation models typically use techniques from signal processing, such as Fourier transforms and harmonic analysis, to estimate the harmonic content of a signal. This can include both the amplitude and phase of each harmonic component, as well as the fundamental frequency of the signal. Harmonic estimation is crucial in various fields, such as audio signal processing, power system analysis, and communications systems. In these applications, it is essential to accurately estimate the harmonic content of a signal to understand its underlying structure and to perform further signal processing tasks. In recent times, the parameters of power systems have been highly distorted, due to the increased application of non-linear loads. Monitoring harmonic and inter-harmonic distortion is an essential issue for delivering high-quality power. Modern power systems' devices generate a broad spectrum of harmonic components, especially vast converter systems that propagate non-characteristic harmonics and inter-harmonics. Many algorithms have been proposed to address harmonic estimation that eventually improve power quality performance, but to date, an accurate estimate is challenging. Researchers have developed different probabilistic and deterministic techniques, that vary in accuracy and complexity. [PERSON]'s approach of separating the output waveform into signal and noise subspaces is used to perform an estimation of the harmonics from the industrial converter in [64]; two estimation techniques, prony and min-norm, show better accuracy than the Fourier algorithm in most cases. Ref. [65] presents an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking. Figure 12 shows the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Ref.** & **Advantage** & **Disadvantage** & **Applications** \\ \hline Arithmetic Summation & [54] & Simple to implement & Does not account for phase variations & Harmonic assessment in simple systems \\ \hline Monte Carlo & [50] & Accounts for randomness and variability & Computationally intensive & Large-scale harmonic studies, renewable energy integration \\ \hline IEC 61400-Series & [55; 56; 57; 58; 59] & Widely adopted standard & Induces errors in summation & Wind farms and renewable energy grid integration \\ \hline Machine Learning & [62; 63] & High accuracy, can adapt to complex systems & Requires training, computationally intensive & Advanced grid systems with variable sources \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Harmonic aggregation models. flow chart of the adaptive filtering technique used. In this technique, the noise covariance matrix is not fixed but dynamic, as the adaptive filter is measurement-dependent. The authors in [66] propose an algorithm for estimating dynamic frequency and amplitude in unbalanced three-phase power systems with harmonics. This algorithm can accurately estimate an unbalanced power system's frequency and amplitude by using a non-linear power system voltage model that includes harmonic content, employing an unscented Kalman filter (UKF). Evaluation of this algorithm's performance, conducted using the Monte Carlo simulation, shows that it is able to track the grid voltage waveform accurately and rapidly when frequency and amplitude change, but only a limited number of harmonics can be estimated. The number of electric vehicles (EVs) is rising, leading to an increasing demand for EV charging stations, which in turn is causing a rapid rise in harmonic pollution from EV chargers. Ref. [67] proposes a harmonic analytical model to estimate the equivalent circuit parameters of chargers based on measured voltage and current data at the AC side and the circuit constraints during the charging process. This model is created by grouping similar types of chargers. Figure 13 shows the equivalent circuit used to develop the analytical model, the circuit charger uncontrolled converter.