A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

Garcia, Carlos Iturrino; Grasso, Francesco; Luchetta, Antonio; Piccirilli, Maria Cristina; Paolucci, Libero; Talluri, Giacomo

doi:10.3390/app10196755

Cited by 62 publications

(17 citation statements)

References 27 publications

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“…The flattening layer has the advantage of not affecting the parameter by converting the output of the extracted feature map into a 1D array, which allows reconstructing the feature maps as the input to the LSTM [ 24 ]. At this time, the input is transmitted through the hidden layer of the LSTM.…”

Section: Methodsmentioning

confidence: 99%

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

Kang

Shin

Jung

et al. 2021

Journal of Healthcare Engineering

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The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person’s stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.

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Section: Methodsmentioning

confidence: 99%

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

Kang

Shin

Jung

et al. 2021

Journal of Healthcare Engineering

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“…Load forecasting can be performed with convolutional neural networks as well, exploiting the different timescales of the features inherent in the time profile of the phenomenon as an advantage [30,31]. Power quality disturbances could sometime hider the load forecasting capabilities; for this reason, specific classification techniques are often employed [32]. Due to the complexity of the forecasting problem, deep convolutional networks can often benefit from an automated definition of the hyperparameters by means of metaheuristic or evolutionary optimization algorithms [33,34], or networks trained through derivative-free optimization algorithms [35].…”

Section: Machine Learning For Battery Energy Storage Systemsmentioning

confidence: 99%

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

et al. 2021

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In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

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“…In this case, almost all electronic devices, from industrial equipment to electronic household appliances, are adversely affected. Besides, energy providers are badly affected in this case [3]. Understanding the causes of these situations enables action to be taken.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit

Yiğit

Özkaya

Öztürk

et al. 2021

Mobile Information Systems

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Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.

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A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

Cited by 62 publications

References 27 publications

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit

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