A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Gu, Dexi; Gao, Yunpeng; Li, Yunfeng; Yanqing, Zhu; Wu, Cong

doi:10.1109/tii.2021.3115567

Cited by 20 publications

(4 citation statements)

References 23 publications

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“…The minimal occurrence frequency of the label set is specified by the PPT in order to prevent sampling of labels that do not occur frequently. A PPT-based multilabel feature selection strategy was developed by the author in [ 25 , 26 ], and it was eventually taken up by the community to improve the effectiveness of classification (PPT+MI).…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Almazroi,

Ayub

2024

PLoS ONE

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In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO’s efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Almazroi,

Ayub

2024

PLoS ONE

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“…By embracing a multiple sequence approach, it brings forth an enhanced detection mechanism and recognizes two concurrent disturbances within a singular window [8]. Additionally, with the rise of complex multilabel classification tasks, this research introduces LGAN, a deep learning marvel adept at extracting, directing, and predicting PQDs [9]. To ensure the interpretations of these classifiers remain lucid, an explainable artificial intelligence method is proposed, emphasizing comprehensible decision-making [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

Jiang,

Wu,

Zhong

et al. 2024

IEEE Access

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Addressing the limitations inherent in conventional Power Quality Disturbance (PQD) identification systems, particularly regarding the restricted information obtainable from single image features and the compromised noise immunity of single-channel networks, an innovative approach, integrating Uniform Relative Position Matrix-Continuous Wavelet Transform (URPM-CWT) and multi-channel feature fusion, is presented. This method capitalizes on the principle of feature fusion to enhance microgrid PQD identification. To begin with, each PQD signal undergoes processing through the URPM and CWT, followed by horizontal splicing to yield the URPM-CWT feature image. This is followed by the parallel deployment of three refined networks-MobileNetV2, ResNet50, and ShuffleNetV2-using the Self Fusion Module (SFM) to yield a multi-channel feature fusion classification model. The final stage involves feeding the URPM-CWT feature image into the multi-channel feature fusion classification model and applying a fully connected layer for training, leading to comprehensive perturbation recognition. Constructed using the PyTorch framework, the proposed model is evaluated on an exhaustive database of 28 distinct PQD types. In a 30db white noise environment, the method demonstrates an average classification accuracy of 99.35%, surpassing the performance of standalone deep learning recognition approaches. Simulation experiments corroborate the model's high classification accuracy, effective recognition, and robust resistance to noise when dealing with PQD signals. Thus, the model offers promising potential for practical applications in PQD identification and classification.

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“…25 Hence, many researchers have used recurrent neural network (RNN) architectures for disturbances classification. Gu et al 26 proposed a new label-guided attention network to classify PQDs types. It comprises the convolutional layers, attention mechanism, and bidirectional RNN.…”

Section: Introductionmentioning

confidence: 99%

A fused CNN‐LSTM model using FFT with application to real‐time power quality disturbances recognition

Cen

Kim²,

Lim

2023

Energy Science & Engineering

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With the progress of renewable energy generation and energy storage technologies, more and more renewable sources and devices are integrated into the power system. Due to the complexity of the power system, single and multiple power quality disturbances (PQDs) occur more frequently. Hence, real‐time detection of PQDs is the primary issue to mitigate the risk of distortions. This study presents the real‐time PQDs classification using fused convolutional neural networks (CNN) combined with long short‐term memory (fused CNN‐LSTM) architecture based on time and frequency domain features. The frequency‐domain features were obtained from time‐series data using fast Fourier transform. The original time‐domain and frequency‐domain features are extracted by respective CNN‐LSTM structures. The extracted time and frequency domain features are concatenated to classify the PQD through fully connected layers. Our proposed method was trained and tested using 16 types of synthetic noise PQDs data generated by mathematical models, in accordance with the standard IEEE‐1159. Moreover, to further verify the performance of our approach, a simulation distributed power system is carried out to detect various PQDs. We compared three advanced neural network approaches: Deep CNN, CNN‐LSTM, and multifusion CNN (MFCNN). The fused CNN‐LSTM model takes only 0.64 ms to classify each PQDs signal and achieves an accuracy of 98.95% and 98.89% in synthetic data and simulated power systems which indicates our proposed method outperformed compared methods.

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A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Cited by 20 publications

References 23 publications

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

A fused CNN‐LSTM model using FFT with application to real‐time power quality disturbances recognition

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