# Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System

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## Abstract

**:**

## 1. Introduction

- Proposed a new way of VSG active power injection through optimizing both the input and output MF of ANFIS.
- Through the optimizations, real-time scheduling of values J and D is produced. It managed to cover a wide range of interruptions and able to give appropriate active power responses.

## 2. Optimized Adaptive Network-Based Fuzzy Inference System Using Genetic Algorithm

#### 2.1. VSG Active Power Controller

#### 2.2. Adaptive Network Fuzzy Inference System

_{i}and uB

_{j}, are the membership degrees of the linguistic label, A and B, as given in Equations (3) and (5). For this ANFIS design, I is set to 7 because each linguistic label has seven MF.

_{i}, of each rule are generated by using membership degrees in layer 1. w

_{i}values are calculated as the following.

_{i}to the total of all firing strengths as given in Equation (8).

#### 2.3. Genetic Algorithm

_{1}and OF

_{2}.

_{1}and OV

_{2}is the optimum value for OF

_{1}and OF

_{2}. For this work, OV

_{1}is equal to 60, and OV

_{2}is equal to 0.75. Objective function 1 (OF

_{1}) in (12) is the frequency difference produced during each iteration. ${f}_{c}$ is the reference frequency and ${f}_{i}$ is the maximum output frequency for each iteration. OF

_{1}aims to minimize the frequency difference at every response. In contrast, objective function 2 (OF

_{2}) in (13) is the damping ratio calculation of each response. The target for OF

_{2}is to obtain a response close to the optimal damping ratio, which is set at 0.75. ${x}_{n}$ is the peak response value at n period, ${x}_{0}$ is the peak response value for the first period. Once GA has optimized the ANFIS, GA-ANFIS will be used in the PSCAD simulation. A new data set from the PSCAD simulation will be used to improve the GA-ANFIS if the VSG response does not meet a certain minimum setpoint. A total of 70% of the PSCAD simulation data set is used as a training set, while 30% is used as a testing set. The number of iterations for GA optimization is set at 100 iterations. GA-ANFIS rules surface generated from MATLAB is shown in Figure 7.

## 3. Test System Structure and Scale

## 4. Results and Discussions

#### 4.1. Scenario 1: Solar Drop 100% for 0.2 s

#### 4.2. Scenario 2: Solar Variation (Irradiance Change)

^{2}generating 1.32 MW from PV farm. Solar irradiance changes from 900 W/m

^{2}to 100 W/m

^{2}at 8.16 s for 0.2 s while retaining the temperature of the solar panel, which was set at 40 °C. This scenario simulates the intermittent nature of a solar farm. As a result, power generated from PV farm dropped from 1.32 MW to 0.2 MW, subsequently causing an under-frequency event due to power deficit. At t = 8.36 s, the solar irradiance changes from 100 W/m

^{2}back to 900 W/m

^{2}. At this point, an over-frequency event occurs due to the sudden injection of solar power.

#### 4.3. Scenario 3: Islanding Event

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 10.**Zoom out view of Figure 8.

**Figure 14.**Zoomed out view of Figure 12.

Parameters | Value |
---|---|

SME power | 0.95 MW |

SME storage capacity | 0.95 MWh |

DC link voltage | 600 V |

Line voltage | 400 V |

Inverter switching frequency | 2 kHz |

LCL filter | 11 mH, 0.62 µF, 6.6 mH |

Line reactance, resistance | 0.0238 Ohm/km, 0.342 Ohm/km |

Parameters | Value |
---|---|

Rated power | 1 MVA |

Rated voltage | 11 kV |

Inertia constant | 0.2 s |

Neutral series resistance | 10,000 (pu) |

Neutral series reactance | 0 (pu) |

Iron loss resistance | 300 (pu) |

Parameters | Value |
---|---|

Number of modules connected in series per array | 20 |

Number of module strings in parallel per array | 20 |

Number of cells connected in series per module | 108 |

Number of cell strings in parallel per module | 4 |

Reference irradiation | 1000 W/m^{2} |

Reference cell temperature | 25 °C |

Effective area per cell | 0.01 m^{2} |

Series resistance per cell | 0.02 Ohm |

Shunt resistance per cell | 1000 Ohm |

Band gap energy | 1.103 eV |

Saturation current at reference conditions per cell | 1 × 10^{−12} kA |

Short circuit current at reference conditions per cell | 0.0025 kA |

Controllers | Method |
---|---|

VSG with fixed value of J and D (VF) | The value of J and D is fixed to two constants. Both constants are deduced from a unit pulse interruption response. |

VSG with fuzzy inference system for J and D (VFIS) | Value of J and D are scheduled in the form of a fuzzy inference system (FIS). The only input for the FIS is the ROCOF. The uniform triangle membership function is used for the FIS. The range of each MF is fixed. |

VSG with GA-ANFIS for J and D (VOFIS) | For the ANFIS, two input MF and two output MFs are set. Change in frequency and ROCOF as the input while constant J and D as the output. Both input and output MF are optimized using a genetic algorithm. From the optimization, input and output MF are heuristically designed to manage a wide range of interruptions and responses. |

Controller Strategies | Frequency Nadir, FN (Hz) | 1st Transition Damping Ratio, DR (%) | Frequency Peak, FP (Hz) | 2nd Transition Damping Ratio, DR (%) |
---|---|---|---|---|

NV | 53.68 | <0.01 | 67.31 | 3.7169 |

VF | 59.06 | 0.2536 | 63.91 | 5.3174 |

VFIS | 59.09 | 0.1858 | 62.12 | 2.8827 |

VOFIS | 59.40 | 0.8560 | 60.68 | 0.8133 |

Controller Strategies | Frequency Nadir, FN (Hz) | 1st Transition Damping Ratio, DR (%) | Frequency Peak, FP (Hz) | 2nd Transition Damping Ratio, DR (%) |
---|---|---|---|---|

NV | 58.93 | <0.01 | 63.82 | 5.606 |

VF | 58.64 | <0.01 | 63.27 | 4.771 |

VFIS | 59.03 | 0.0849 | 61.18 | 1.853 |

VOFIS | 59.56 | 0.5208 | 60.87 | 1.293 |

Controller Strategies | Frequency Nadir, FN (Hz) | 1st Transition Damping Ratio, DR (%) |
---|---|---|

VFIS | 48.87 | 9.163 |

VOFIS | 59.44 | 0.203 |

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**MDPI and ACS Style**

Othman, M.H.; Mokhlis, H.; Mubin, M.; Ab Aziz, N.F.; Mohamad, H.; Ahmad, S.; Mansor, N.N.
Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System. *Sustainability* **2022**, *14*, 10798.
https://doi.org/10.3390/su141710798

**AMA Style**

Othman MH, Mokhlis H, Mubin M, Ab Aziz NF, Mohamad H, Ahmad S, Mansor NN.
Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System. *Sustainability*. 2022; 14(17):10798.
https://doi.org/10.3390/su141710798

**Chicago/Turabian Style**

Othman, Mohd Hanif, Hazlie Mokhlis, Marizan Mubin, Nur Fadilah Ab Aziz, Hasmaini Mohamad, Shameem Ahmad, and Nurulafiqah Nadzirah Mansor.
2022. "Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System" *Sustainability* 14, no. 17: 10798.
https://doi.org/10.3390/su141710798