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Machine Learning Gaussian Process Regression based Robust H-Infinity Controller Design for Solar PV System to Achieve High Performance and Guarantee Stability^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Modeling of Solar PV System

#### 2.1. MPPT and MPC for the Solar PV System

#### 2.2. Solar I-V and P-V Characteristics

^{2}).

## 3. MLGPR based Robust H-infinity Controller Design

#### 3.1. Machine Learning GPR Model

_{i}, y

_{i})}, where i = (1,2,3,…,n) is a sequence of data values and x and y denote the input vector and scalar targets or ground truth, respectively. All the input vectors of n values are collected in X and scalar targets value y as Y, respectively. The Input Data X has with noisy or mismatch data values, while target or ground truth Y has the real values of the system, which are further given to the GPR model for the training process.

_{q(a)}[z(a)], which means expectation z(a) when x ~ q(a). From the above two Equations (1) and (2), the Gaussian Process ($\mathcal{G}\mathcal{P}$) can be written as follows [7]:

#### 3.2. Robust H-Infinity Controller

## 4. Simulation Results

_{pv}) and Maximum Power Point tracking comparison plot are given, in which the P

_{pv}has more variation in power, but even though it can attain maximum power, as shown in Figure 5a, the red color line represents the MPPT algorithm and the green line is the PV Power value. In Figure 5b, the DC Bus Voltage of the Solar Energy Source is in the range of 800 Volts. It could include some undershoot and overshoot at the initial of the DC bus voltage, and the voltage can reach 800 V in 0.8 s. This signal is also found with some fluctuations, which are reduced by the proposed method, as shown in Figure 6.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**(

**a**) Solar Irradiation in Watts per unit area (W/m

^{2}); (

**b**) PV Current (Iabc and Ref Current).

**Figure 5.**(

**a**) Solar PV and Maximum Power Point Tracking Power comparison; (

**b**) Solar PV DC bus voltage.

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## Share and Cite

**MDPI and ACS Style**

Se Pa, S.; Yakoob, M.B.; Maruthai, P.; Singaravelu, K.; Duraisamy, N.; Palaniappan, R.D.; Pithai, J.B.
Machine Learning Gaussian Process Regression based Robust H-Infinity Controller Design for Solar PV System to Achieve High Performance and Guarantee Stability. *Eng. Proc.* **2022**, *19*, 26.
https://doi.org/10.3390/ECP2022-12631

**AMA Style**

Se Pa S, Yakoob MB, Maruthai P, Singaravelu K, Duraisamy N, Palaniappan RD, Pithai JB.
Machine Learning Gaussian Process Regression based Robust H-Infinity Controller Design for Solar PV System to Achieve High Performance and Guarantee Stability. *Engineering Proceedings*. 2022; 19(1):26.
https://doi.org/10.3390/ECP2022-12631

**Chicago/Turabian Style**

Se Pa, Sureshraj, Mohamed Badcha Yakoob, Priya Maruthai, Karthikeyan Singaravelu, Nalini Duraisamy, Rathi Devi Palaniappan, and John Britto Pithai.
2022. "Machine Learning Gaussian Process Regression based Robust H-Infinity Controller Design for Solar PV System to Achieve High Performance and Guarantee Stability" *Engineering Proceedings* 19, no. 1: 26.
https://doi.org/10.3390/ECP2022-12631