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A high proportion of new energy has become a prominent feature of modern power systems. Due to the intermittency, volatility, and strong randomness in wind power generation, an accurate and reliable method for the prediction of wind power is required. This paper proposes a modified stacking ensemble learning method for short-term wind power predictions to reduce error and improve the generalization performance of traditional single networks in tackling the randomness of wind power. Firstly, the base learners including tree-based models and neural networks are improved based on the Bagging and Boosting algorithms, and a method for determining internal parameters and iterations is provided. Secondly, the linear integration and stacking integration models are combined to obtain deterministic prediction results. Since the modified stacking meta learner can change the weight, it will enhance the strengths of the base learners and optimize the integration of the model prediction to fit the second layer prediction, compared to traditional linear integration models. Finally, a numerical experiment showed that the modified stacking ensemble model had a decrease in MAPE from about 8.3% to 7.5% (an absolute decrease of 0.8%) compared to a single learner for the 15 min look-ahead tests. Changing variables such as the season and predicting the look-ahead time showed satisfactory improvement effects under all the evaluation criteria, and the superiority of the modified stacking ensemble learning method proposed in this paper regarding short-term wind power prediction performance was validated. [ABSTRACT FROM AUTHOR]

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved. [ABSTRACT FROM AUTHOR]

To assess the biological potential of an Er complex that contains a 2,2'-bipyridine ligand, various techniques such as multispectral and molecular modeling procedures were utilized to examine its DNA-binding ability, BSA binding affinity, antimicrobial effects, and anticancer properties. By analyzing fluorescent information and employing the vant' Hoff equation, important parameters such as the innate docking coefficient (Kb), Stern-Volmer coefficient (KSV), and thermodynamic properties including modifications in liberated energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) were determined. The trial findings suggest that the compound can bind to DNA, primarily through groove binding. Additionally, the engagement between the Er compound and the protein BSA was examined using emission spectroscopy technique, revealing a powerful binding affinity between the compound and BSA. The Er complex binds to BSA primarily via hydrogen links and van der Waals forces, as indicated by the adverse values of ΔH° and ΔS°. Through a static quenching process, the complex significantly reduces the intrinsic fluorescence of BSA. Molecular binding calculations and rivalrous binding trials confirm that this compound dock to hydrophobic remains found in site III of BSA. Additionally, the Er complex demonstrates promising results in terms of its anticancer and antimicrobial activities based on screening tests. [ABSTRACT FROM AUTHOR]

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge. [ABSTRACT FROM AUTHOR]

In the present work, the surface of glassy carbon electrode (GCE) was modified with graphitic carbon nitride (g-C3N4) prepared from the freeze-dried dicyandiamide. The physiochemical properties and electrocatalytic ability of the g-C3N4 nanosheets have been well investigated. Differential pulse voltammetry (DPV) method was employed to determine bisphenol A (BPA) using the modified GCE. To obtain the conditions for maximum DPV response, the effects of experimental parameters, such as the solution pH and nano-sized modifier concentration, were investigated and optimized. The proposed BPA sensing assay indicated a linear range of 0.01 to 75.0 µM under optimal conditions, with a detection limit of 9.0 nM. The anti-interference investigations revealed that the presence of various organic and inorganic species did not hinder the accurate measuring of BPA. In addition, the presented modified electrode exhibited satisfactory results in terms of stability and reproducibility. The obtained results also confirm the efficacy of the g-C3N4 nanocatalyst for determination of BPA in different real samples (drinking water, milk and orange juice) with satisfactory recovery values (95.6–104.0%). The suggested assay provides a powerful sensing platform for the fast and sensitive analysis of BPA in food samples. [ABSTRACT FROM AUTHOR]

In this contribution a methodology to diagnose transformer faults based on Dissolved Gas Analysis (DGA) by using a convolutional neural network (CNN) is proposed. The algorithm to transform the gas contents (resulting from the DGA analysis) into feature maps is introduced, and the resulting feature maps are the input of the CNN. In order to take into account the fact that the data set is imbalanced, the improved Synthetic Minority Over-Sampling Technique (SMOTE) is combined with the data cleaning technique to protect the CNN from training bias. The effect of the CNN architecture on the classification performance is also investigated to determine the optimal CNN parameters. All the above mentioned possibilities are tested and their performance investigated; in addition, a final test on the IEC TC 10 transformer fault database validates the accuracy and the generalization potential of the proposed methodology. [ABSTRACT FROM AUTHOR]