# Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Induction Generator Wind Energy System

#### 2.1. Induction Generator Model

_{s}, L

_{r}, L

_{m}are the stator, rotor, and mutual inductances, respectively, R

_{s}, R

_{r}are the stator, rotor resistances, respectively, τ

_{r}= L

_{r}/R

_{r}is the rotor time constant, p is the number of poles pair, σ is the leakage coefficient, and ω is the rotor speed.

_{r}is the damping factor and T

_{r}is the wind turbine torque.

#### 2.2. Control Scheme

## 3. State Estimation

#### 3.1. Reference Voltage Model-Based Rotor Flux Estimation

#### 3.2. Kalman Filter Based Rotor Flux Estimation

**w**and

**v**are independent measurement noises with covariances: E{

**w**(k)

**w**(k)

^{T}} = Q, E{

**v**(k)

**v**(k)

^{T}} = R.

_{s}is the sampling time.

#### 3.3. Artificial Neural Network Speed Estimation

- Two external signals (estimated rotor flux from the reference voltage Model (3) and estimated rotor flux from the KF (5)).
- A feedback from the ANN output with a delay.

_{i}; i = 1$\cdots $N are the weights from the hidden layer to the output layer, and N is the number of neurons in the hidden layer.

## 4. Load Side Control

#### 4.1. Control Design

#### 4.2. Frequency Estimation

_{m}is the amplitude, ω

_{e}= 2πf is the angular frequency and $\varphi $ is the phase angle.

**w**and

**z**are independent noises.

_{s}/f is the number of samples in the fundamental component and f

_{s}is the sampling frequency.

**V**

_{L}using

**x**=

**V**

_{L},

**y**=

**v**, and

**w**and

**z**are independent measurement noises with covariance [37]. Then, the instantaneous phase angle is obtained by

## 5. Battery Storage System and Power Management

_{1}and s

_{2}are selected, based on the control system, to operate the converter for charge and discharge modes. The control system includes a PI controller to regulate the DC-link voltage (V

_{dc}) for tracking the voltage reference (V

_{dc}*), and the battery charge-discharge is operated through controlling its current (I

_{bat}) to track a current reference carried out from the powers: Wind generator power (P

_{w}), battery power (P

_{B}), and load power (P

_{L}); and the battery voltage (V

_{bat}), as shown in Figure 5b.

_{bat}is the battery current and Q is the battery capacity.

_{net}) and the battery $\mathrm{SOC}$. It follows the flowchart shown in Figure 6.

## 6. Experimental Results

- Three-phase squirrel-cage induction generator.
- Capacitor bank connected to the generator stator terminal for running as a self-started generator.
- Four-quadrant dynamometer, coupled with the induction generator, for wind turbine emulation.
- Back-to-back IGBT converters to connect the generator to the load.
- Bidirectional IGBT DC-DC converter and line inductor to connect the BSS to the DC-link.
- Battery bank based on lead acid batteries.
- Three-phase inductor as the filter to connect the DC-AC converter to the load.
- Variable switching resistor to vary the three-phase AC load.
- Data acquisition interface (OPAL-RT OP8660) for voltage-current measurements.
- Real-time digital simulator (OPAL-RT OP5600) for rapid control prototyping and Hardware-in-the-loop (HIL).

_{s}= 100 μs.

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

- State prediction:

- 2.
- Estimated error covariance:

- 3.
- Kalman filter gain calculation:

- 4.
- State correction:

- 5.
- Error covariance update:

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**Figure 1.**Wind energy conversion system: (

**a**) DC-microgrid based standalone self-excited induction generator with battery storage; (

**b**) generator side converter control scheme.

**Figure 9.**Control and estimation responses under variable step wind speed profile and variable load: (

**a**) Speed estimation and tracking; (

**b**) power responses; (

**c**) DC-link voltage regulation; (

**d**) battery current.

**Figure 11.**Control and estimation responses under random ramp wind speed profile and constant load: (

**a**) Speed estimation and tracking; (

**b**) power responses; (

**c**) DC-link voltage regulation.

**Figure 12.**Load side control: (

**a**) Controlled d-q load voltages, (

**b**) Frequency control and estimation.

Element | Characteristics | |
---|---|---|

Dynamometer | Four-quadrant, 0−3 Nm, 0−2500 rpm, 350 W | |

SCIG | Four-pole, 3 phases, 60 Hz, 208 V, 1670 rpm, 175 W | |

Battery | Lead acid, 48 V, 9 Ah, max charge current 2.7 |

Characteristics | Values |
---|---|

IGBT power converters | |

DC-link voltage | 220 V |

IGBT peak current | 12 A |

Switching control (voltage, frequency) | 0/5 V, 0−20 kHz |

Excitation capacitor bank | |

Power, voltage | 252 VAR, 120 V |

Capacitance | 8.8 μF |

Resistance | 300 Ω |

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**MDPI and ACS Style**

Tanvir, A.A.; Merabet, A.
Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid. *Energies* **2020**, *13*, 1743.
https://doi.org/10.3390/en13071743

**AMA Style**

Tanvir AA, Merabet A.
Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid. *Energies*. 2020; 13(7):1743.
https://doi.org/10.3390/en13071743

**Chicago/Turabian Style**

Tanvir, Aman A., and Adel Merabet.
2020. "Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid" *Energies* 13, no. 7: 1743.
https://doi.org/10.3390/en13071743