Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

The primary source of the various power-quality-disruption (PQD) concerns in smart grids is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart meters, measurement units, and computers that are linked by a large network. Because real-time data exchange via a network of various sensors demands a small file size without an adverse effect on the information quality, one measure of the power-quality monitoring in a smart grid is restricted by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0. The suggested layers, added between the pretrained base model and the classifier, modify the pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model. This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size, and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable and bandwidth-friendly data-packet transmission in WSNs.

The power quality disturbances (PQDs) has become an issue of essential importance in the world. The foundation to address the power quality problem is by implementing the PQDs identification and classification technique. This paper presented the I-dimensional deep convolutional neural network (DCNN) to identify and classify the power quality interferences. The dataset is generated based on the mathematical model of 14 types PQDs which refers to the IEEE-1159 standard. To enhance training time computation, the wavelet compression (WT) method is proposed in the data preprocessing stage. The deep learning architecture is composed of four 1-D convolutional layers, two pooling layers, a dropout layer, a fully connected layer, and a softmax layer. To introduce non-linearity in CNN, this architecture adopts the rectified linear unit (ReLU) function. To demonstrate the DCNN performance, the comparison between the model with the original dataset and the compression dataset is simulated. The experiment result indicates that this approach can successfully predict the PQDs data with more than 99,5 % classification performance, while the computation time improves on the training phase.

PM2.5 is ultra-light micro-grained particles and dangerous air pollution that threatens public health. A real-time wireless sensor network (WSN) that measure air pollution is a solution to increase public awareness about the long-term impact of PM2.5 exposure. However, in a massive-scale WSN-based air pollution monitoring system, there are numerous noisy and low-concentration periods in the raw PM2.5 dataset, which may lead to unreliable causality predictions. This paper addresses the problem of optimizing sensor acquisition of a wireless sensor network to reconstruct and predict spatiotemporal PM concentrations data using ConvLSTM network. This prediction model is built by combining convolution network and long short-term memory network. The dataset is gathered from air quality WSN s that are already installed across Taiwan. Using the last 48-hour records, the next hour PM2.5 concentration is predicted. RMSE is used to evaluate the prediction accuracy. The results reveal that the ConvLSTM network achieves better performance than those using the LSTM network and regression analysis with RMSE of 1.31, 2.59, and 16.34, respectively.

This research investigates the application of Machine Learning in the context of sentiment classification of user reviews on online transportation applications in Indonesia. This research aims to develop a model to accurately classify positive, negative, or neutral sentiments from customer reviews. Review data collected from the Gojek, Grab, and Maxim applications is utilized to train and test Machine Learning models, including natural language processing (NLP) techniques to extract significant features. Sentiment analysis results show that approximately 63.1% have negative sentiment reviews, 27.7% have positive sentiments, and 9.2% have neutral sentiments. Furthermore, the Support Vector Machine (SVM) algorithm achieved the highest level of accuracy at 92%, followed by Random Forest (RF) with an accuracy level of 91%, Naive Bayes (NB) at 79%, Decision Tree (DT) at 80%, and Logistic Regression (LR) with an accuracy level of 83%. In general, the SVM model performs better than others.