Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model.

Fault diagnosis of power transformers based on t-SNE and ECOC-TEWSO-SVM

To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient ( $\text{E}_{\mathrm {ns}}$ ) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09999365.pdf

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Precursor chemical mixture gases (PMGs) analysis plays a significant role in combatting drug-related crimes. However, existing intelligent methods for analyzing PMG components are constrained by weak signals, as well as signal coupling issues related to semiconductor gas sensors. To address this issue, a variational mode decomposition (VMD) adversarial autoencoder joint multitask convolutional neural network (VAE-MTCNN) algorithm is proposed. The proposed algorithm uses a semiconductor gas sensor MQ4, to collect signals in square wave temperature modulation, which are then processed by VMD to minimize signal coupling. The sub-signals are then augmented using the adversarial autoencoder through adversarial training. The construction of RGB digital portraits (RDPs) is used to enhance the validity of characteristic expressions by converting sub-signals to 2-D images. Finally, the MTCNN is designed to achieve PMG component recognition and quantification using two parallel task channels. The performance of the proposed method is validated using PMG samples, including acetone, ether and acetone and ether. Experimental results show that the VAE-MTCNN approach outperforms current state-of-the-art methods, with a PMG component recognition precision of 96.3% and a concentration quantification root mean square error (RMSE) of 3.50 ppm. Therefore, this method has great potential to improve drug-related crime detection.

Precursor Chemical Mixtures Analysis Using Joint VMD Adversarial Autoencoder and Multi-Task CNN Algorithm via Gas Sensor

To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{E}_{\mathrm {ns}}$ </tex-math></inline-formula> ) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding. 

Abstract Cost estimation in construction engineering is a crucial topic of study. Low precision in construction engineering cost prediction results from traditional models' inability to adequately capture the evolving trend of construction engineering costs. As a result, a descriptive data mining-based cost prediction model for construction engineering is suggested. The database of engineering costs is first built using several characteristic indices, and the aberrant data are found and filtered using the K-means clustering approach. Second, the mathematical model is established, and its solution is carried out using the LSSVM. Finally, the model is optimized using the PSO technique. The experimental results demonstrate that, in comparison to the conventional engineering cost prediction models, our model's predicted value of the single cost is the closest to the actual cost of one party, and the predicted difference is all within 30 yuan/m2. This finding has significant implications for engineering investment decisions because it can achieve higher accuracy and stability in engineering cost prediction. 

/pdf/multidimensional-analysis-of-engineering-cost-database-based-31f9tjx3.pdf

Multidimensional Analysis of Engineering Cost Database Based on Descriptive Data Mining

Dissolved gas analysis in oil is an effective method for early fault diagnosis of transformers. Predicting the concentration of future characteristic gases in the transformer can assist operation and maintenance personnel in judging the operation trend of the transformer and ensure stable operation. In order to improve the prediction accuracy of dissolved gas in transformer oil based on a small number of samples, this paper proposes a VMD‐SMA‐LSSVM combined prediction model by using variational modal decomposition and least square support vector machine optimized by slime mold algorithm. First, use variational modal decomposition to decompose the gas signal. For each subsequence, a combined algorithm based on slime mold optimization and least square support vector machine is used to predict separately. Then the prediction results of each sub‐sequence are superimposed and reconstructed to obtain the final prediction value. The research results show that the prediction results obtained based on this method have better prediction effects than other models of machine learning models, other decomposition methods and optimization methods. The proposed method has good fitting characteristics when predicting seven characteristic gases, which verifies the effectiveness. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Prediction Model of Dissolved Gas in Transformer Oil Based on VMD‐SMA‐LSSVM

In view of the problem of high energy consumption of ordinary hydraulic brick presses, the scheme of energy-saving transformation is proposed to replace the asynchronous motor with AC permanent magnet synchronous servo motor. The simulation models of the ordinary hydraulic brick press and the reformed servo direct drive pump-controlled hydraulic brick press are established in AMESim, and the energy consumption simulation and analysis are carried out. The results show that the modified servo direct drive pump-controlled hydraulic brick press is 20.7% more energy-saving than before the transformation. The reasons for energy saving lies in that, compared with the ordinary hydraulic brick press, the servo direct drive pump-controlled hydraulic brick press saves overflow loss in the pressure holding stage and reduces the dormant loss in the dormant stage.

Research on energy saving of hydraulic system of hydraulic brick press

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Prediction Model of Dissolved Gas in Transformer Oil Based on VMD‐SMA‐LSSVM

Research on energy saving of hydraulic system of hydraulic brick press