# Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Classification of Reactive Power Compensation Systems

_{0}= 50 V in time no longer than t

_{r}= 60 s. This means that it is necessary to connect resistors to the compensation circuits, thanks to which the charge accumulated inside the capacitor is discharged. In addition, a large number of switches negatively affects the contactors connecting the batteries to the network. There occurs a contact burning phenomenon that leads to direct damage to the device and more frequent maintenance. It is also connected with an increase in the costs of using the compensation system.

#### 2.2. Research on a Real Object

_{L}= 223–225 V and I

_{L}= 483–543 A. Based on the shape of the waveform, it can be noticed that there are asymmetries and higher harmonics of both voltages and phase currents in the network. On the basis of the collected measurement data, the reactive power waveform was determined, which was aggregated with the averaging period TAVG equals 1 s. The recorded time curve is shown in Figure 3.

_{1}= 70 kVAr and duration T

_{1}= 80 s, and the second quick-change with amplitude Q

_{2}= 100 kVAr and T

_{2}= 15 s. This type of reactive power load causes considerable difficulties in the shunt capacitor bank, in which it reaches instantaneous activation of compensating stages and is followed by long periods of discharging.

#### 2.3. Artificial Neural Networks

_{1}, …, x

_{n}) whose states (values) are multiplied by weighting factors (w

_{o}, …, w

_{n}). The partial products are then added together and transferred as the input value of the activation function, which on this basis calculates the neuron’s output state. The effects of a neuron action can be described mathematically as:

_{i}—i-th inputs of the neuron, y—output of the neuron, w

_{i}—weighting factors of i-th input, f—neuron activation function, N—total number of inputs.

## 3. Results, Implementation of Neural Networks

_{i}—real value, p

_{i}—projected value.

- Criterion I consists in determining the maximum number of consecutive samples (the so-called prediction horizon) for which E does not exceed 5%;
- Criterion II, as with criterion I, consists in determining the maximum prediction window for E not exceeding 2%;
- Criterion III consists in determining the number of samples for which E does not exceed 5%;
- Criterion IV consists in determining the number of samples for which E does not exceed 2%.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 8.**Simulation results for individual evaluation criteria: (

**a**)—criterion I, (

**b**)—criterion III, (

**c**)—criterion II, (

**d**)—criterion IV.

**Figure 9.**Simulation results for N = 7 neurons, n = 14 samples of the input vector: (

**a**)–measured and predicted, (

**b**)–prediction error, and (

**c**)–predicted vs. measured reactive power.

**Table 1.**The values of the assessment criteria for the structure with N = 7 neurons and G = 14 samples of the input vector.

Assessment Criteria | Value |
---|---|

Criterion I | 217 |

Criterion II | 2 |

Criterion III | 217 |

Criterion IV | 92 |

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**MDPI and ACS Style**

Błaszczok, D.; Trawiński, T.; Szczygieł, M.; Rybarz, M.
Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks. *Electronics* **2022**, *11*, 2005.
https://doi.org/10.3390/electronics11132005

**AMA Style**

Błaszczok D, Trawiński T, Szczygieł M, Rybarz M.
Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks. *Electronics*. 2022; 11(13):2005.
https://doi.org/10.3390/electronics11132005

**Chicago/Turabian Style**

Błaszczok, Damian, Tomasz Trawiński, Marcin Szczygieł, and Marek Rybarz.
2022. "Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks" *Electronics* 11, no. 13: 2005.
https://doi.org/10.3390/electronics11132005