# A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- This paper presents a novel multitasking deep learning model for classifying and quantification multiple electrical disturbances.
- This study proves how deep multitasking learning is an excellent model for solving the challenge of quantitative analysis and classification of multiple electrical disturbances without the level of complexity or noise in the signal being a problem.
- Graphs are shown where the assessment of the quality of electrical power with electrical disturbance can be observed. This way, assessing the quality and impact of linear and linear loads is simpler.
- The extraction of characteristics is proposed using an adaptive oscillatory methodology. Due to the random nature of the electrical disturbances, this paper proves how using traditional strategies presented in other articles is ineffective.
- The development and testing were conducted with 29 electrical disturbances, from single disturbances to several simultaneous electrical disturbances, with noise levels ranging from 20 dB to 50 dB.
- The development of additional tests performed on an island network with a photovoltaic system and high-switching elements is described. In addition, electrical disturbances of all 29 levels with noise levels between 20 dB and 50 dB were constantly injected.

## 2. Related Works

## 3. Theoretical Background

#### 3.1. Empirical Mode Decomposition

- Initialize the signal $x(t$) to be decomposed into a set of intrinsic mode functions (IMF).
- For each IMF component $i(i=1,2,\dots ,N)$, repeat the following sifting process until convergence:
- (a)
- Identify all local maxima and minima of $x\left(t\right)$ to obtain the upper and lower envelopes, respectively.
- (b)
- Calculate the average of the upper and lower envelopes to obtain the mean envelope $h\left(t\right)$.
- (c)
- Subtract the mean envelope $h\left(t\right)$ from the signal $x\left(t\right)$ to obtain a “detrended” signal $d\left(t\right)$.
- (d)
- Check whether $d\left(t\right)$ is a valid IMF by verifying the following conditions:
- The number of zero-crossings and extrema must be equal or differ at most by one.
- The local mean of $d\left(t\right)$ is zero.

- (e)
- If $d\left(t\right)$ satisfies the above conditions, it is considered an IMF, and the sifting process for this IMF is complete.
- (f)
- If $d\left(t\right)$ does not satisfy the above conditions, it is added to the residual signal, and the sifting process is repeated on the residual signal.

- The residual signal obtained after sifting all IMF is the final trend component of the signal.

#### 3.2. Multitasking Deep Neural Network

#### 3.3. Performance Indices

- True positives (TP): Corresponds to the samples correctly predicted as positive.
- False positives (FP): Corresponds to the incorrectly predicted as positive.
- True negatives (TN): Corresponds to the correctly predicted as negative.
- False negatives (FN): Corresponds to the incorrectly predicted as negative.

## 4. Materials and Methods

#### 4.1. Synthesis of Electrical Disturbances and Database

#### 4.2. Description of Power System

- Distributed generators: A solar photovoltaic system generates a maximum power of 250 kW in STC (cell temperature of 25 °C with solar irradiance of 1000 $\frac{\mathrm{W}}{{\mathrm{m}}^{2}}$).
- Voltage controller for distributed generators: DC-DC Boost type charge controller and a DC-AC voltage source inverter (VSI).
- Voltage inverter: For the Voltage Source Inverter (VSI), a full bridge inverter works at 1000 Hz switching.
- Control methodology for voltage controller: The system also has an integrated maximum power point tracking (MPPT) controller with a DC-DC boost type voltage controller. The MPPT control helps to generate the proper voltage by extracting the maximum power and adjusting the duty cycle to avoid performance problems due to changes in temperature and solar irradiance that are simulated in the system.

#### 4.3. Deep Neural Network Multitasking Architecture

**Batch Normalization:**This layer is used to improve the training speed and stability of the model. The basic idea behind batch normalization is to normalize the input data of each layer [32]. This is accomplished by subtracting the batch mean from each input data point and dividing it by the batch standard deviation. The batch means and standard deviation are estimated using the input data of a batch rather than the entire data set [33]. Batch normalization helps to reduce the problem of internal covariate drift, which occurs when there is high variation in the input data. This can lead to slower convergence and overfitting [34]. Batch normalization is a powerful technique that improve the performance of deep neural networks [35].**Convolutional layer or conv layer:**This is a crucial building block of convolutional neural networks (CNNs). It is designed to perform feature extraction from input data such as images, video, or audio. The basic idea behind convolutional layers is to apply a set of learnable filters (kernels or weights) to the input data to extract essential features [36]. Each filter performs a convolution operation on the input data, which involves sliding the filter over the input and computing the dot product between the filter weights and the local input values at each position [37]. In a Convolutional Neural Network (CNN) used for regression, the convolutional layers will be designed to extract relevant features from the input data that help predict the target values. The convolutional layers are an essential part of the network architecture for regression problems because they allow the network to capture important local patterns in the input data, which can be highly relevant for predicting the target values. By stacking multiple convolutional layers with increasing filter sizes, the network can learn increasingly complex and abstract features from the input data, making more accurate predictions [38].**Polling:**This is used for down-sampling. The aim is to scale and map the data after feature extraction, reduce the dimension of the data, and extract the important information, thus performing feature reduction efficiently within the neural architecture. This avoids adding extra phases and and reduces computational consumption [39]. There are several types of pooling layers, but the most common ones are max pooling and average pooling. Max Pooling Layers reduce the spatial dimensions of the output from the convolutional layers by taking the maximum value within each pooling window [40].**Dropout:**This randomly drops out some of the neurons in the previous layer during training, which helps prevent overfitting and improves the network’s generalization ability. The main idea behind dropout is that the network learns to rely on the remaining neurons to make accurate predictions. This forces the network to learn more robust features that are not dependent on any specific set of neurons [41].**SoftMax:**This is a typical activation function used in neural networks, particularly in multi-class classification problems. The SoftMax function takes a vector of real-valued scores as input and normalizes them into a probability distribution over the classes [42].**The flattening layer:**This is a layer that converts multidimensional inputs into a one-dimensional vector. This is often performed to connect a convolutional layer to a fully connected layer, which requires one-dimensional inputs [43].**Fully connected layer or dense layer:**This is a layer where each neuron is connected to every neuron in the previous layer. Each neuron performs a weighted sum of the activations from the previous layer and then applies an activation function to the sum to produce an output. The weights and biases are learned during training using backpropagation, where the gradients are propagated backward from output layer to input layer [44].

## 5. Analysis and Results

#### 5.1. Signal Classification Performance Indices

#### 5.2. Performance Indices in Data Regression

#### 5.3. Analysis of Results

#### 5.4. Discussions and Applications

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

PQD | Power Quality Disturbance | MDL | Mul-titasking Deep Neural Network |

EMD | Empirical Mode Decomposition | Pst | Short Term Flicker Perceptibility |

THD | Total Harmonic Distortion | SVM | support Vector Machine |

D-CNN | Dimensional Convolution Neural Network | ||

LSTM | Long Short Term Memory | CNN-LSTM | Convolutional Neural Networks Long Short Term Memory |

CA | Cluster analysis | ELM | Extreme Learning Machine |

GPQIs | Global power quality indices | DWT | Discrete Wavelet Transform |

WT | Wavelet Transform | DFT | Discrete Fourier Transform |

STFT | Time Fourier Transform | LR | Logistic Regression |

LDA | algo | EMD | Empirical Mode Decomposition |

IMF | Intrinsic Mode Functions | MLD | Deep Learning models |

TP | True positives | FP | False Positrives |

TN | True Negatives | FN | False Negatives |

VSI | tage source inverter | IMF | Intrinsic Mode Functions |

MPPT | maximum power point tracking | PWM | Pulse Width Modulation |

## Appendix A

ID: 1 | PQD: Normal Signal | Synthesis parameters |

Model: | $Frecuency$ = 60 Hz | |

$$v\left(t\right)=180sin(wt-\varphi )$$
| $Amplitude$ = 180 V | |

$\omega =2\pi \ast 60\phantom{\rule{3.33333pt}{0ex}}\frac{\mathrm{rad}}{\mathrm{s}}$ | ||

ID: 2 | PQD: Sag | Synthesis parameters |

Model: | $v\left(t\right)=180(1-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $0.1\le \alpha \le 0.9$ |

ID: 3 | PQD: Swell | Synthesis parameters |

Model: | $v\left(t\right)=180(1+\beta (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $0.1\le \beta \le 0.9$ |

ID: 4 | PQD: Interruption | Synthesis parameters |

Model: | $v\left(t\right)=180(1+\rho (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $0.9\le \rho \le 1$ |

ID: 5 | PQD: Transient DIV Impulse DIV Spike | Synthesis parameters |

Model: | $v\left(t\right)=180\left[sin(\omega t-\varphi )-\psi \left({e}^{-750(t-{t}_{a})}-{e}^{-344(t-{t}_{a})}\right)(u(t-{t}_{a})-u(t-{t}_{b}))\right]$ | $0.222\le \psi \le 1.11$ |

ID: 6 | PQD: Oscillatory transient | Synthesis parameters |

Model: | $300\le {f}_{n}\le 900$ | |

$v\left(t\right)=180\left[sin(\omega t-\varphi )+\beta {e}^{-\frac{t-{t}_{I}}{\tau}}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{I}I)-u(t-{t}_{I}))\right]$ | 8 ms $\le \tau \le $ 40 ms | |

$0.1\le \beta \le 0.9$ | ||

ID: 7 | PQD: Harmonics | Synthesis parameters |

Model: | $0.05\le {\alpha}_{3}\le 0.15$ | |

$v\left(t\right)=180\left[sin(\omega t-\varphi )+{\sum}_{n=3}^{7}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]$ | $0.05\le {\alpha}_{5}\le 0.15$ | |

$0.05\le {\alpha}_{7}\le 0.15$ | ||

ID: 8 | PQD: Harmonics with Sag | Synthesis parameters |

Model: | $0.1\le \alpha \le 0.9{\alpha}_{3}$ | |

$v\left(t\right)=180(1-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))\left[sin(\omega t-\varphi )+{\sum}_{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]$ | $0.05\le {\alpha}_{5}\le 0.15$ | |

$0.05\le {\alpha}_{7}\le 0.15$ | ||

ID: 9 | PQD: Harmonics with Swell | Synthesis parameters |

Model: | $0.1\le \beta \le 0.9{\alpha}_{3}$ | |

$v\left(t\right)=180(1+\beta (u(t-{t}_{1})-u(t-{t}_{2})))\left[sin(\omega t-\varphi )+{\sum}_{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]$ | $0.05\le {\alpha}_{5}\le 0.15$ | |

$0.05\le {\alpha}_{7}\le 0.15$ | ||

ID: 10 | PQD: Flicker | Synthesis parameters |

Model: |
$$v\left(t\right)=180(1+\lambda sin\left({\omega}_{f}t\right))sin(wt-\varphi )$$
| $0.05\le \lambda \le 0.1$ |

$8\le f\le 25$ | ||

ID: 11 | PQD: Flicker with Sag | Synthesis parameters |

Model: | $0.05\le \lambda \le 0.1$ | |

$v\left(t\right)=180(1+\lambda sin\left({\omega}_{f}t\right)-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $8\le f\le 25$ | |

$0.1\le \alpha \le 0.9$ | ||

ID: 12 | PQD: Flicker with Swell | Synthesis parameters |

Model: | $0.05\le \lambda \le 0.1$ | |

$v\left(t\right)=180(1+\lambda sin\left({\omega}_{f}t\right)-\beta (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $8\le {f}_{f}\le 25$ | |

$0.1\le \beta \le 0.9$ | ||

ID: 13 | PQD: Sag with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& sin(\omega t-\varphi )(1-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I}))\right]\hfill \end{array}$$
| $0.1\le \alpha \le 0.9\theta $ |

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 14 | PQD: Swell with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& sin(\omega t-\varphi )\beta (u(t-{t}_{1})-u(t-{t}_{2}))\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{-t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{I}I)-u(t-{t}_{I}))\right]\hfill \end{array}$$
| $0.1\le \beta \le 0.9\theta $ |

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 15 | PQD: Sag with Harmonics | Synthesis parameters |

Model: | $v\left(t\right)=180\left[sin(\omega t+{\theta}_{1})+(-\alpha (u(t-{t}_{1})-u(t-{t}_{2}))){\sum}_{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]$ | $0.1\le \alpha \le 0.9{\alpha}_{3}$ |

ID: 16 | PQD: Swell with Harmonics | Synthesis parameters |

Model: | $0.1\le \beta \le 0.9{\alpha}_{3}$ | |

$v\left(t\right)=180\left[sin(\omega t+{\theta}_{1})+\beta (u(t-{t}_{1})-u(t-{t}_{2}))){\sum}_{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]$ | $0.05\le {\alpha}_{5}\le 0.15$ | |

$0.05\le {\alpha}_{7}\le 0.15$ | ||

ID: 17 | PQD: Notch | Synthesis parameters |

Model: | $v\left(t\right)=180\left[sin(\omega t-\varphi )-sin(\omega t-\varphi ){\sum}_{n=0}^{N\xb7c-1}k(u(t-({t}_{c}+s\xb7n))-u)(t-({t}_{d}+s\xb7n))\right]$ | $\phantom{\rule{2.em}{0ex}}c=1,2,4,6$ |

ID: 18 | PQD: Harmonics with Sag with Flicker | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180(1+\lambda sin\left({\omega}_{f}t\right))& \left[sin(\omega t-\varphi )+\sum _{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& (1-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))\hfill \end{array}$$
| $0.1\le \alpha \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25$ | ||

ID: 19 | PQD: Harmonics with Swell with Flicker | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180(1+\lambda sin\left({\omega}_{f}t\right))& \left[sin(\omega t-\varphi )+\sum _{n=3}^{5}{\alpha}_{n}sin(n\omega t+{\theta}_{n})\right]\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& (1+\beta (u(t-{t}_{1})-u(t-{t}_{2})))\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25$ | ||

ID: 20 | PQD: Sag with Harmonics with Flicker | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180(& sin(\omega t+{\theta}_{1})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& +(1+\lambda sin\left({\omega}_{f}t\right)(-\alpha (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}}))\hfill \end{array}$$
| $0.1\le \alpha \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25$ | ||

ID: 21 | PQD: Swell with Harmonics with Flicker | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180(& sin(\omega t+{\theta}_{1})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& +(1+\lambda sin\left({\omega}_{f}t\right)(\beta (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}}))\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25$ | ||

ID: 22 | PQD: Sag with Harmonics with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& sin(\omega t+{\theta}_{1})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& +(-\alpha (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I})\right]\hfill \end{array}$$
| $0.1\le \alpha \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 23 | PQD: Swell with Harmonics with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& sin(\omega t+{\theta}_{1})+(\beta (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{I}I)-u(t-{t}_{I})\right]\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 24 | PQD: Harmonics with Sag with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[1-& \alpha (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{I}I)-u(t-{t}_{I})\right]\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 25 | PQD: Harmonics with Swell with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[1& +\beta (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{I}I)-u(t-{t}_{I})\right]\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 26 | PQD: Harmonics with Sag with Flicker with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& 1-\alpha (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I})1+\lambda sin\left({\omega}_{f}t\right))\right]\hfill \end{array}$$
| $0.05\le {\alpha}_{3}\le 0.15$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.1\le \alpha \le 0.9\lambda $ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 27 | PQD: Harmonics with Swell with Flicker with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& 1+\beta (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I})1+\lambda sin\left({\omega}_{f}t\right))\right]\hfill \end{array}$$
| $0.05\le {\alpha}_{3}\le 0.15$ |

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.1\le \beta \le 0.9\lambda $ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25\theta $ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 28 | PQD: Sag with Harmonics with Flicker with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=180[& sin(\omega t+{\theta}_{1})+\alpha (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\left(\frac{t-{t}_{I}}{\tau}\right)}sin(\omega (t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I})1+\lambda sin\left({\omega}_{f}t\right))\right]\hfill \end{array}$$
| $0.1\le \alpha \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{3}\le 0.15$ | ||

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25\theta $ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ | ||

ID: 29 | PQD: Swell with Harmonics with Flicker with Oscillatory transient | Synthesis parameters |

Model: |
$$\begin{array}{cc}\hfill v\left(t\right)=& 180[sin(\omega t+{\theta}_{1}).\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& +(\beta (u(t-{t}_{1})-u(t-{t}_{2}))\sum _{{n}^{\u2033}}^{5}{\alpha}_{{n}^{\u2033}}sin({n}^{\u2033}\omega t+{\theta}_{{n}^{\u2033}})\hfill \\ \hfill \phantom{\rule{1.em}{0ex}}& \left.+\beta {e}^{-\frac{t-{t}_{I}}{\tau}}sin({\omega}_{n}(t-{t}_{I})-\theta )(u(t-{t}_{II})-u(t-{t}_{I})1+\lambda sin\left({\omega}_{f}\tau \right))\right]\hfill \end{array}$$
| $0.1\le \beta \le 0.9{\alpha}_{3}$ |

$0.05\le {\alpha}_{3}\le 0.15$ | ||

$0.05\le {\alpha}_{5}\le 0.15$ | ||

$0.05\le {\alpha}_{7}\le 0.15$ | ||

$0.05\le \lambda \le 0.1$ | ||

$8\le f\le 25\theta $ | ||

$300\le {f}_{n}\le 900$ | ||

8 ms $\le \tau \le $ 40 ms | ||

$0.1\le \beta \le 0.9$ |

## References

- Souza Junior, M.E.T.; Freitas, L.C.G. Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review. Sustainability
**2022**, 14, 3597. [Google Scholar] [CrossRef] - Prashant; Siddiqui, A.S.; Sarwar, M.; Althobaiti, A.; Ghoneim, S.S.M. Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM. Sustainability
**2022**, 14, 3305. [Google Scholar] [CrossRef] - IEEE Std 1159-2019; IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Standards Association: New York, NY, USA, 2019.
- Masetti, C. Revision of European Standard EN 50160 on power quality: Reasons and solutions. In Proceedings of the 14th International Conference on Harmonics and Quality of Power-ICHQP 2010, Bergamo, Italy, 26–29 September 2010; pp. 1–7. [Google Scholar] [CrossRef]
- Ma, C.T.; Gu, Z.H. Design and Implementation of a GaN-Based Three-Phase Active Power Filter. Micromachines
**2020**, 11, 134. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ma, C.T.; Shi, Z.H. A Distributed Control Scheme Using SiC-Based Low Voltage Ride-Through Compensator for Wind Turbine Generators. Micromachines
**2022**, 13, 39. [Google Scholar] [CrossRef] - Wan, C.; Li, K.; Xu, L.; Xiong, C.; Wang, L.; Tang, H. Investigation of an Output Voltage Harmonic Suppression Strategy of a Power Quality Control Device for the High-End Manufacturing Industry. Micromachines
**2022**, 13, 1646. [Google Scholar] [CrossRef] - Yoldaş, Y.; Önen, A.; Muyeen, S.; Vasilakos, A.V.; Alan, I. Enhancing smart grid with microgrids: Challenges and opportunities. Renew. Sustain. Energy Rev.
**2017**, 72, 205–214. [Google Scholar] [CrossRef] - Jumani, T.A.; Mustafa, M.W.; Rasid, M.M.; Mirjat, N.H.; Leghari, Z.H.; Saeed, M.S. Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm. Energies
**2018**, 11, 3191. [Google Scholar] [CrossRef] [Green Version] - Samanta, H.; Das, A.; Bose, I.; Jana, J.; Bhattacharjee, A.; Bhattacharya, K.D.; Sengupta, S.; Saha, H. Field-Validated Communication Systems for Smart Microgrid Energy Management in a Rural Microgrid Cluster. Energies
**2021**, 14, 6329. [Google Scholar] [CrossRef] - Banerjee, S.; Bhowmik, P.S. A machine learning approach based on decision tree algorithm for classification of transient events in microgrid. Electr. Eng.
**2023**, 1–11. [Google Scholar] [CrossRef] - Mahela, O.P.; Shaik, A.G.; Khan, B.; Mahla, R.; Alhelou, H.H. Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree. IEEE Access
**2020**, 8, 173530–173547. [Google Scholar] [CrossRef] - Liu, H.; Hussain, F.; Yue, S.; Yildirim, O.; Yawar, S.J. Classification of multiple power quality events via compressed deep learning. Int. Trans. Electr. Energy Syst.
**2019**, 29, 2010. [Google Scholar] [CrossRef] - Wang, S.; Chen, H. A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl. Energy
**2019**, 235, 1126–1140. [Google Scholar] [CrossRef] - Thirumala, K.; Pal, S.; Jain, T.; Umarikar, A.C. A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM. Neurocomputing
**2019**, 334, 265–274. [Google Scholar] [CrossRef] - Shen, Y.; Abubakar, M.; Liu, H.; Hussain, F. Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems. Energies
**2019**, 12, 1280. [Google Scholar] [CrossRef] [Green Version] - Garcia, C.I.; Grasso, F.; Luchetta, A.; Piccirilli, M.C.; Paolucci, L.; Talluri, G. A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM. Appl. Sci.
**2020**, 10, 6755. [Google Scholar] [CrossRef] - Jasiński, M.; Sikorski, T.; Kostyła, P.; Leonowicz, Z.; Borkowski, K. Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation. Energies
**2020**, 13, 2050. [Google Scholar] [CrossRef] [Green Version] - Subudhi, U.; Dash, S. Detection and classification of power quality disturbances using GWO ELM. J. Ind. Inf. Integr.
**2021**, 22, 100204. [Google Scholar] [CrossRef] - Radhakrishnan, P.; Ramaiyan, K.; Vinayagam, A.; Veerasamy, V. A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network. Measurement
**2021**, 175, 109025. [Google Scholar] [CrossRef] - Chamchuen, S.; Siritaratiwat, A.; Fuangfoo, P.; Suthisopapan, P.; Khunkitti, P. Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification. Appl. Sci.
**2021**, 11, 5670. [Google Scholar] [CrossRef] - Saxena, A.; Alshamrani, A.M.; Alrasheedi, A.F.; Alnowibet, K.A.; Mohamed, A.W. A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines. Mathematics
**2022**, 10, 2780. [Google Scholar] [CrossRef] - Cuculić, A.; Draščić, L.; Panić, I.; Ćelić, J. Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network. J. Mar. Sci. Eng.
**2022**, 10, 1190. [Google Scholar] [CrossRef] - Sun, J.; Chen, W.; Yao, J.; Tian, Z.; Gao, L. Research on the Roundness Approximation Search Algorithm of Si3N4 Ceramic Balls Based on Least Square and EMD Methods. Materials
**2023**, 16, 2351. [Google Scholar] [CrossRef] [PubMed] - Eltouny, K.; Gomaa, M.; Liang, X. Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review. Sensors
**2023**, 23, 3290. [Google Scholar] [CrossRef] - Xu, D.; Shi, Y.; Tsang, I.W.; Ong, Y.S.; Gong, C.; Shen, X. Survey on Multi-Output Learning. IEEE Trans. Neural Netw. Learn. Syst.
**2020**, 31, 2409–2429. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tien, C.L.; Chiang, C.Y.; Sun, W.S. Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques. Micromachines
**2022**, 13, 1409. [Google Scholar] [CrossRef] [PubMed] - Liu, W.; Xu, D.; Tsang, I.W.; Zhang, W. Metric Learning for Multi-Output Tasks. IEEE Trans. Pattern Anal. Mach. Intell.
**2019**, 41, 408–422. [Google Scholar] [CrossRef] - Van Amsterdam, B.; Clarkson, M.J.; Stoyanov, D. Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; pp. 1380–1386. [Google Scholar] [CrossRef]
- Yang, X.; Sun, H.; Sun, X.; Yan, M.; Guo, Z.; Fu, K. Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network. IEEE Access
**2018**, 6, 50839–50849. [Google Scholar] [CrossRef] - Igual, R.; Medrano, C.; Arcega, F.J.; Mantescu, G. Integral mathematical model of power quality disturbances. In Proceedings of the 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, Slovenia, 13–16 May 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Kaur, R.; GholamHosseini, H.; Sinha, R.; Lindén, M. Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images. BMC Med. Imaging
**2022**, 22, 103. [Google Scholar] [CrossRef] - Zhao, R.; Wang, S.; Du, S.; Pan, J.; Ma, L.; Chen, S.; Liu, H.; Chen, Y. Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning. Micromachines
**2023**, 14, 502. [Google Scholar] [CrossRef] - Xu, S.; Zhou, Y.; Huang, Y.; Han, T. YOLOv4-Tiny-Based Coal Gangue Image Recognition and FPGA Implementation. Micromachines
**2022**, 13, 1983. [Google Scholar] [CrossRef] [PubMed] - Halbouni, A.; Gunawan, T.S.; Habaebi, M.H.; Halbouni, M.; Kartiwi, M.; Ahmad, R. CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System. IEEE Access
**2022**, 10, 99837–99849. [Google Scholar] [CrossRef] - Naseer, S.; Saleem, Y.; Khalid, S.; Bashir, M.K.; Han, J.; Iqbal, M.M.; Han, K. Enhanced Network Anomaly Detection Based on Deep Neural Networks. IEEE Access
**2018**, 6, 48231–48246. [Google Scholar] [CrossRef] - Li, C.; Qiu, Z.; Cao, X.; Chen, Z.; Gao, H.; Hua, Z. Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification. Micromachines
**2021**, 12, 545. [Google Scholar] [CrossRef] [PubMed] - Sundaram, S.; Zeid, A. Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines
**2023**, 14, 570. [Google Scholar] [CrossRef] - Marey, A.; Marey, M.; Mostafa, H. Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels. Micromachines
**2022**, 13, 1533. [Google Scholar] [CrossRef] - Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw.
**2015**, 61, 85–117. [Google Scholar] [CrossRef] [Green Version] - Devaraj, J.; Ganesan, S.; Elavarasan, R.M.; Subramaniam, U. A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes. Appl. Sci.
**2021**, 11, 4129. [Google Scholar] [CrossRef] - Nguyen, H.D.; Cai, R.; Zhao, H.; Kot, A.C.; Wen, B. Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme. Micromachines
**2022**, 13, 565. [Google Scholar] [CrossRef] - He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar] [CrossRef] [Green Version]
- Huang, S.; Wang, L. MOSFET Physics-Based Compact Model Mass-Produced: An Artificial Neural Network Approach. Micromachines
**2023**, 14, 386. [Google Scholar] [CrossRef] - Nagata, E.A.; Ferreira, D.D.; Bollen, M.H.; Barbosa, B.H.; Ribeiro, E.G.; Duque, C.A.; Ribeiro, P.F. Real-time voltage sag detection and classification for power quality diagnostics. Measurement
**2020**, 164, 108097. [Google Scholar] [CrossRef] - Sekar, K.; Kanagarathinam, K.; Subramanian, S.; Venugopal, E.; Udayakumar, C. An improved power quality disturbance detection using deep learning approach. Math. Probl. Eng.
**2022**, 2022, 7020979. [Google Scholar] [CrossRef] - Cao, H.; Zhang, D.; Yi, S. Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation. Sol. Energy
**2023**, 251, 77–85. [Google Scholar] [CrossRef]

**Figure 2.**Comparison of architectures between deep learning supervised learning and deep learning multitasking learning.

**Figure 5.**Photovoltaic power generator system for additional tests with a multiple electrical disturbance injector system.

**Figure 6.**The architecture of a Novel Multitasking Deep Neural Network for Classification and Analysis of Multiple Electrical Disturbances.

**Figure 7.**Feature extraction with EMD algorithm: (

**left**) Spike without noise; (

**right**) Spike with noise.

**Figure 8.**Feature extraction with EMD algorithm: (

**left**) Notch without noise; (

**right**) Notch with noise.

**Figure 9.**Feature extraction with EMD algorithm: (

**left**) Oscillatory transient without noise; (

**right**) Oscillatory transient with noise.

**Figure 10.**Model training performance evaluation: (

**left**) Precision of Multitasking Deep Neural Network—Classification; (

**right**) Loss of Multitasking Deep Neural Network—Classification.

**Figure 11.**Model training performance evaluation: (

**left**) Test Data Segment Confusion Matrix—Part 2; (

**right**) Test Data Segment Confusion Matrix—Part 2.

**Figure 12.**Model training performance evaluation: (

**left**) Test confusion matrix in photovoltaic power generating system—Part 1; (

**right**) Test confusion matrix in photovoltaic power generating system—Part 2.

**Figure 13.**Model training performance evaluation: (

**Left**) Precision curve; (

**Right**) Loss Learning curve.

**Figure 14.**Analysis of voltage per unit in the electrical disturbance: (

**left**) Voltage p.u in 0.12 s without dB disturbance or noise; (

**right**) Voltage p.u in 0.12 s with Noise disturbance.

**Figure 15.**Analysis of crest factor in the electrical disturbance: (

**left**) No dB disturbance or noise; (

**right**) Noise disturbance.

Paper | Year | Feature Extraction Methodology | Signal Classification Methodology |
---|---|---|---|

[13] | 2018 | Initial layers in the neural network | Deep neural network |

[14] | 2019 | One-dimensional convolutional, pooling, and batch normalization layers to capture multi-scale features | Closed-loop deep-learning method |

[15] | 2019 | Empirical wavelet Transform-based adaptive filtering technique | Multiclass support vector machine (SVM) |

[16] | 2019 | Root Mean Square, Skewness, Range, Kurtosis | Improved Principal Component Analysis (IPCA) and 1-Dimensional Convolution Neural Network (1-D-CNN) |

[17] | 2020 | Initial layers in the neural network | Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Convolutional Neural Networks Long Short Term Memory (CNN-LSTM), and CNN-LSTM |

[18] | 2020 | Global power quality indices (GPQIs) | Cluster analysis (CA) |

[19] | 2021 | Stockwell Transform | Extreme Learning Machine (ELM) |

[20] | 2021 | Discrete Wavelet Transform | Model assembled with Logistic Regression (LR), Naïve Bayes, and J48 decision tree |

[21] | 2021 | Discrete Wavelet Transform and adaptive salp swarm algorithm | Probabilistic neural network |

[22] | 2022 | Hilbert Transform and Wavelet Transform | Support Vector Machine |

[23] | 2022 | Discrete Wavelet Transform | Artificial Neural Network |

Performance Indices | Formula | Meaning of Symbology |
---|---|---|

Mean absolute error | $MAE=\frac{1}{N}{\sum}_{i=1}^{N}|{y}_{i}-{y}_{p}|$ | ${y}_{p}$ is the predicted value ${y}_{i}$ is real value N is the total number of data |

The absolute mean percentage error | $MAPE=\frac{1}{N}{\sum}_{i=1}^{N}100\frac{|{y}_{i}-{y}_{p}|}{{y}_{i}}$ | ${y}_{p}$ is the predicted value ${y}_{i}$ is real value N is the total number of data |

Performance Indexes | Formula |
---|---|

Accuracy | $Accuracy=\frac{TP+TN}{TP+TN+FP+FN}$ |

Recall | $Recall=\frac{TP}{TP+FN}$ |

Specificity | $Specificity=\frac{TN}{TN+FP}$ |

Parameter | Value |
---|---|

sample rate | 16 kHz |

Peak Voltage | 180 V |

Frequency | 60 Hz |

**Table 5.**Fragment of the table in Appendix A of mathematical models of electrical disturbances.

Power Quality Disturbance | Identifier | Mathematical Model | Synthesis Parameters |
---|---|---|---|

Normal Signal | 1 | $v\left(t\right)=180sin(\omega t-\varphi )$ | $\omega =2\pi \ast 60\frac{rad}{s}$ |

Sag | 2 | $v\left(t\right)=180(1-\alpha (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $0.1\le \alpha \le 0.9$ |

Swell | 3 | $v\left(t\right)=180(1$+$\beta (u(t-{t}_{1})-u(t-{t}_{2})))sin(\omega t-\varphi )$ | $0.1\le \beta \le 0.9$ |

Reference | [17] | [45] | [14] | [22] | Current | |
---|---|---|---|---|---|---|

Characteristics | ||||||

Accuracy percentage | 79.14–83.66% | 90% | 88–98% | 91.3–99% | 98–99% | |

Feature extraction methodology | short time Fourier Transform | Higher-Order Statistics | 1-D convolutional | Wavelet Transform | Empirical Mode Decomposition (EMD) | |

Classification methodology | Convolutional Neural Networks Long Short-Term Memory | Multi-layer perceptron (MLP) Support Vector Machine (SVM) | Deep convolutional neural network | Support Vector Machine (SVM) | Multitasking Deep Neural Network | |

Electrical disturbance levels | 7 levels | 2 levels, Sags, and swells | 15 levels | 5 levels | 28 levels | |

Noise levels | Without noise | 40 dB to 60 dB | 40 dB to 60 dB | Without noise | 10 dB to 50 dB | |

Qualitative analysis of electrical disturbance | Does not perform quantification or analysis | Does not perform quantification or analysis | Does not perform quantification or analysis | Does not perform quantification or analysis | Quantitative analysis of the different electrical disturbances |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Guerrero-Sánchez, A.E.; Rivas-Araiza, E.A.; Garduño-Aparicio, M.; Tovar-Arriaga, S.; Rodriguez-Resendiz, J.; Toledano-Ayala, M.
A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. *Technologies* **2023**, *11*, 82.
https://doi.org/10.3390/technologies11040082

**AMA Style**

Guerrero-Sánchez AE, Rivas-Araiza EA, Garduño-Aparicio M, Tovar-Arriaga S, Rodriguez-Resendiz J, Toledano-Ayala M.
A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. *Technologies*. 2023; 11(4):82.
https://doi.org/10.3390/technologies11040082

**Chicago/Turabian Style**

Guerrero-Sánchez, Alma E., Edgar A. Rivas-Araiza, Mariano Garduño-Aparicio, Saul Tovar-Arriaga, Juvenal Rodriguez-Resendiz, and Manuel Toledano-Ayala.
2023. "A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks" *Technologies* 11, no. 4: 82.
https://doi.org/10.3390/technologies11040082