# Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid

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## Abstract

**:**

## 1. Introduction

## 2. Structure and Control of Microgrid

#### 2.1. Structure of the System

#### 2.2. Control of PV

#### 2.3. Control of Ultra-Capacitor/Battery

#### 2.4. Control of Inverter

- First Layer:

- Second Layer:

- Third Layer:

_{in}operator is used to find the output value.

- Fourth Layer:

- Fifth Layer:

- Sixth Layer:

- Seven Layer:

_{d}− y

## 3. Energy Management and Supervisory Control System

- All of the control signals are generated, i.e., P
_{PV}, P_{G}, P_{L}, P_{B}, P_{U}, S_{B}, and S_{U}. - Check ${P}_{L}={P}_{\mathit{PV}}\pm {P}_{G}\pm {P}_{U}\pm {P}_{B}$, go to 1 if this condition is true, and if not then follow next step.
- Check P
_{L}> ${P}_{\mathit{PV}}$, if it is true, go to step 9, and if not then check the next condition. - Check S
_{B}> 20%, if it is true, then discharge the battery, and go to next step, otherwise go to step 2. - Check the condition ${P}_{L}-{P}_{\mathit{PV}}-{P}_{B}>0$, if this is true, then go to the next step, otherwise go to step 6.
- Check S
_{U}> 20%, if it is true, then discharge the UC, and go to the next step, otherwise go to step 8. - Check the condition ${P}_{L}-{P}_{\mathit{PV}}-{P}_{B}-{P}_{U}>0$, if it is true, then go to the next step, otherwise go to step 1.
- Using all of the remaining deficient power reference to the grid and go to step 1.
- Check S
_{B}> 90%, if it is not true, then charge the battery and go to the next step, otherwise go to step 11. - Check the condition ${P}_{L}-{P}_{\mathit{PV}}-{P}_{B}<0$, if true, then go to the next step, otherwise go to step 1.
- Check S
_{U}> 90%, if it is not true, then charge the UC and go to the next step, otherwise go to step 13. - Check the condition ${P}_{L}-{P}_{\mathit{PV}}-{P}_{B}-{P}_{U}<0$, if true, then go to the next step, otherwise step 1.
- Provide all of the net surplus power to the utility grid and go to step 1.

## 4. Simulations

^{2}), taken at Islamabad, Pakistan. Both parameters were recorded on an hourly basis, as presented in Figure 9. The intensity of irradiance fluctuated during the day, depending on the sunrise and sunset. From Figure 9, the sun appeared at 07:00 a.m. and set at 17:20 p.m. The average solar irradiance during the daytime was 990 (W/m

^{2}). Likewise, the average temperature during the daytime reached 40 °C, while at night, it went down to 19 °C.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ANFJW | Adaptive neuro-fuzzy Jacobi wavelet |

EMSCS | Energy management and supervisory control system |

FLC | Fuzzy logic controller |

GMF | Gaussian membership function |

IAE | Integral absolute error |

IC | Incremental conductance |

ISE | Integral square error |

ITAE | Integral time absolute error |

ITSE | Integral time square error |

MPPT | Maximum power point tracking |

MRE | Mean relative error |

NF | Neuro-fuzzy |

NN | Neural network |

PV | Photovoltaic |

RES | Renewable energy sources |

THD | Total harmonic distortion |

UC | Ultra-capacitor |

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**Figure 17.**Power efficiency comparison of different controllers: (

**a**) real power and (

**b**) reactive power.

**Figure 18.**Dynamic performance comparison using active power: (

**a**) integral absolute error (IAE), (

**b**) integral time absolute error (ITAE), (

**c**) integral square error (ISE), and (

**d**) integral time square error (ITSE).

**Figure 19.**Dynamic performance comparison using reactive power: (

**a**) IAE, (

**b**) ITAE, (

**c**) ISE, and (

**d**) ITSE.

Symbol | Description |
---|---|

${P}_{L}$ | Local Load Power |

${P}_{G}$ | Grid Power |

${P}_{B}$ | Battery Power |

${P}_{U}$ | Ultra-capacitor Power |

${P}_{PV}$ | PV Power |

${S}_{B}$ | SoC of Battery |

${S}_{U}$ | SoC of UC |

${P}_{BDR}$ | Discharging Reference Power of battery |

${P}_{BCR}$ | Charging Reference Power of Battery |

${P}_{UDR}$ | UC Discharging Reference Power |

${U}_{UCR}$ | UC Charging Reference Power |

${P}_{GR}$ | Grid Reference Power |

Controllers | Output Power | ${\mathit{\eta}}_{\mathit{I}\mathit{N}}(\%\mathbf{Age})$ | THD (% Age) | IAE (p.u) | ITAE (p.u) | ISE (p.u) | ITSE (p.u) |
---|---|---|---|---|---|---|---|

ANFJW | Active | 99.05 | 2.37 | 0.00017 | 0.00166 | 0.00166 | 0.00052 |

Reactive | 99.08 | 0.00012 | 0.00128 | 0.00093 | 0.00003 | ||

NFC | Active | 92.17 | 3.63 | 0.0102 | 0.1169 | 0.0413 | 0.1052 |

Reactive | 92.25 | 0.0076 | 0.0879 | 0.0233 | 0.0597 | ||

FLC | Active | 89.11 | 6.54 | 0.0329 | 0.3758 | 0.1526 | 0.8016 |

Reactive | 89.18 | 0.0247 | 0.2829 | 0.0864 | 0.4573 | ||

PID | Active | 86.94 | 8.96 | 0.0386 | 0.4526 | 0.1619 | 1.078 |

Reactive | 87.04 | 0.0290 | 0.3394 | 0.0917 | 0.6097 |

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**MDPI and ACS Style**

Kamal, T.; Karabacak, M.; Perić, V.S.; Hassan, S.Z.; Fernández-Ramírez, L.M. Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid. *Energies* **2020**, *13*, 4721.
https://doi.org/10.3390/en13184721

**AMA Style**

Kamal T, Karabacak M, Perić VS, Hassan SZ, Fernández-Ramírez LM. Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid. *Energies*. 2020; 13(18):4721.
https://doi.org/10.3390/en13184721

**Chicago/Turabian Style**

Kamal, Tariq, Murat Karabacak, Vedran S. Perić, Syed Zulqadar Hassan, and Luis M. Fernández-Ramírez. 2020. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid" *Energies* 13, no. 18: 4721.
https://doi.org/10.3390/en13184721