Search Results (24)

Due to the difficulty in measuring intangible effects and the reliance on experts for benefit evaluation, actual project data evidence of the impact of BIM is insufficient. To this end, this study collected total project cost data from 164 power substation projects and techno-economic statements from 34 power substation projects from SGCC to capture data evidence of the impact of BIM on project costs and explore its patterns. Algorithms such as hierarchical clustering based on improved DTW and feature selection based on QDA were designed for data mining. The findings demonstrate that the distribution of the CV% of total project costs and the CV% of some specific cost items became more concentrated after the application of BIM, which indicates that BIM enhanced the ability to predict and control project costs. Moreover, five cost items and five shape patterns of cost items were identified as key to the impact of BIM. It is therefore recommended that the related cost items should be controlled with focus during the application of BIM. Full article

The ever-growing penetration of renewable energy with substantial uncertainties and stochastic characteristics significantly affects the modern power grid’s secure and economical operation. Nevertheless, coordinating various types of resources to derive effective online control decisions for a large-scale power network remains a big challenge. To tackle the limitations of existing control approaches that require full-system models with accurate parameters and conduct real-time extensive sensitivity-based analyses in handling the growing uncertainties, this paper presents a novel data-driven control framework using reinforcement learning (RL) algorithms to train robust RL agents from high-fidelity grid simulations for providing immediate and effective controls in a real-time environment. A two-stage method, consisting of offline training and periodic updates, is proposed to train agents to enable robust controls of voltage profiles, transmission losses, and line flows using a state-of-the-art RL algorithm, soft actor–critic (SAC). The effectiveness of the proposed RL-based control framework is validated via comprehensive case studies conducted on the East China power system with actual operation scenarios. Full article

This paper aims to explore the impact of different arrangements of new steel-glass FRP composite connectors (SGCCs) on the bending and composite performance of sandwich wall panels. Through monotonic loading bending tests on six full-size specimens, aspects such as their failure modes, load-deflection curves, load-strain relationships, slip between the thermal insulation layer and concrete, and composite action were analyzed. The results show that all sandwich wall panels experienced bending and ductile failure, and exhibit partial composite performance, with P4 having the best composite performance and P1 the worst. The degree of composite action is positively correlated with the flexural bearing capacity. The bending capacity mainly depends on the layout rather than the total number of SGCCs. Arranging connectors along the short side of the panel has a more significant impact, and the number of connectors at the panel’s ends has a greater influence on the composite performance. Except for P1, the theoretical value of the composite degree of the other sandwich wall panels exceeds 70%, and P4 reaches 85%. The theoretical calculations are in good agreement with the experimental results. This study provides theoretical and data support for the rational configuration of connectors in sandwich wall panels and is of great significance for building engineering applications. Meanwhile, suggestions for configuring connectors in actual engineering are also given. Full article

To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device. Full article

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model’s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids. Full article

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method’s effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. Full article

Direct-current (DC) microgrids have gained worldwide attention in recent decades due to their high system efficiency and simple control. In a self-sufficient energy system, voltage control is an important key to dealing with upcoming challenges of renewable energy integration into DC microgrids, and thus energy storage systems (ESSs) are often employed to suppress the power fluctuation and ensure the voltage stability. In this paper, the performances of three voltage control strategies for DC microgrids are compared, including the proportion integration (PI) control, the fuzzy PI control and particle swarm optimization (PSO) PI control. Particularly, two kinds of ESSs including battery and advanced adiabatic compressed air energy storage (AA-CAES) with different operational characteristics are installed in the microgrid, and their impacts on voltage control are investigated. The control performances are comprehensively compared under different control schemes, various scenarios of renewable energy fluctuations, participation in the control of the two ESSs or not, and different fault conditions. Additionally, the dynamic performances of the ESSs are exhibited. The results verify the validity of the control schemes and the feasibility of the configuration of the ESSs into the DC microgrid. Full article

Treatment-resistant depression (TRD) is a severe disorder characterized by high relapse rates and decreased quality of life. An effective strategy in the management of TRD is deep brain stimulation (DBS), a technique consisting of the implantation of electrodes that receive a stimulation via a pacemaker-like stimulator into specific brain areas, detected through neuroimaging investigations, which include the subgenual cingulate cortex (sgCC), basal ganglia, and forebrain bundles. In this context, to improve our understanding of the mechanism underlying the antidepressant effects of DBS in TRD, we collected the results of diffusion tensor imaging (DTI) studies exploring how WM microstructure is associated with the therapeutic effects of DBS in TRD. A search on PubMed, Web of Science, and Scopus identified 11 investigations assessing WM microstructure in responders and non-responders to DBS. Altered WM microstructure, particularly in the sgCC, medial forebrain bundle, cingulum bundle, forceps minor, and uncinate fasciculus, was associated with the antidepressant effect of DBS in TRD. Overall, the results show that DBS targeting selective brain regions, including the sgCC, forebrain bundle, cingulum bundle, rectus gyrus, anterior limb of the internal capsule, forceps minor, and uncinate fasciculus, seem to be effective for the treatment of TRD. Full article

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%. Full article

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC. Full article

Several approaches have been proposed to detect any malicious manipulation caused by electricity fraudsters. Some of the significant approaches are Machine Learning algorithms and data-based methods that have shown advantages compared to the traditional methods, and they are becoming predominant in recent years. In this study, a novel method is introduced to detect the fraudulent NTL loss in the smart grids in a two-stage detection process. In the first stage, the time-series readings are enriched by adding a new set of extracted features from the detection of sudden Jump patterns in the electricity consumption and the Autoregressive Integrated moving average (ARIMA). In the second stage, the distributed random forest (DRF) generates the learned model. The proposed model is applied to the public SGCC dataset, and the approach results have reported 98% accuracy and F1-score. Such results outperform the other recently reported state-of-the-art methods for NTL detection that are applied to the same SGCC dataset. Full article

Fucoxanthin is a natural marine xanthophyll and exhibits a broad range of biological activities. In the present study, a simple and efficient two-step method was used to purify fucoxanthin from the diatom, Phaeodactylum tricornutum. The crude pigment extract of fucoxanthin was separated by silica gel column chromatography (SGCC). Then, the fucoxanthin-rich fraction was purified using a hydrophile–lipophile balance (HLB) solid-phase extraction column. The identification and quantification of fucoxanthin were determined by high-performance liquid chromatography (HPLC) and electrospray ionization mass spectrometry (ESI-MS). This two-step method can obtain 92.03% pure fucoxanthin and a 76.67% recovery rate. In addition, ¹H and ¹³C NMR spectrums were adopted to confirm the identity of fucoxanthin. Finally, the purified fucoxanthin exhibited strong antioxidant properties in vitro with the effective concentration for 50% of maximal scavenging (EC50) of 1,1-Dihpenyl-2-picrylhydrazyl (DPPH) and 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) free radicals being 0.14 mg·mL⁻¹ and 0.05 mg·mL⁻¹, respectively. Full article

This paper introduces a new model of the customer-centric, two-product split delivery vehicle routing problem (CTSDVRP) in the context of a mixed-flow manufacturing system that occurs in the power industry. Different from the general VRP model, the unique characteristics of our model are: (1) two types of products are delivered, and the demand for them is interdependent and based on a bill of materials (BOM); (2) the paper considers a new aspect in customer satisfaction, i.e., the consideration of the production efficiency on the customer side. In our model, customer satisfaction is not measured by the actual customer waiting time, but by the weighted customer waiting time, which is based on the targeted service rate of the end products. We define the targeted service rate as the ratio of the quantity of the end product produced by the corresponding delivery quantities of the two products to the demand of the end product. We propose a hybrid ant colony-genetic optimization algorithm to solve this model with actual data from a case study of the State Grid Corporation of China. Finally, a case study is explored to assess the effectiveness of the CTSDVRP model and highlight some insights. The results show that the CTSDVRP model can improve customer satisfaction and increase the average targeted service rate of the end products effectively. Full article

This paper describes a smart fault tackling strategy based on power flow transfer entropy (PFTE) for AC three-phase-to-ground (TPG) faults in the multi-terminal HVDC (MTDC) wind power integration system. The fault characteristics and transient energy transfer of different positions and properties are analyzed. Then, a double integral discrimination method based on PFTE is proposed to further distinguish the fault property. Considering the power flow balance, an adaptive coordination strategy of wind farms and energy dissipation resistors is proposed to deal with different AC faults. Finally, a smart fault-tackling strategy based on PFTE for AC three-phase-to-ground (TPG) faults in the MTDC wind power integration system is proposed. Under the proposed smart fault-tackling strategy, the MTDC wind power integration system achieves uninterrupted operation during any AC TPG fault at the receiving end. The experiment results confirm the applicability of the proposed fault-tackling strategy. Full article

Depletion of fossil fuel deposits is the main current issue related to the world’s power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development. Full article