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Data-driven Static Equivalence with Physics-informed Koopman Operators

CSEE Journal of Power and Energy Systems 2024年第1期10卷 432-438页

作者： Wei Lin Changhong Zhao Maosheng Gao C.Y.Chung Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Information Engineering The Chinese University of Hong KongHong Kong SARChina School of Electrical Engineering Chongqing UniversityChongqing 400044China

With deployment of measurement units,fitting static equivalent models of distribution networks(DNs)by linear regression has been recognized as an effective method in power flow analysis of a transmission *** volatility of measurements caused by variable distributed renewable energy sources makes it more difficult to accurately fit such equivalent *** tackle this challenge,this letter proposes a novel data-driven method to improve equivalency accuracy of DNs with distributed energy *** letter provides a new perspective that an equivalent model can be regarded as a mapping from internal conditions and border voltages to border power *** mapping can be established through 1)Koopman operator theory,and 2)physical features of power flow equations at the root node of a *** of the proposed method is demonstrated on the IEEE 33-bus and IEEE 136-bus test systems connected to a 661-bus utility system.

关键词： Data-driven distribution network Koopman operator theory static equivalent model

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An end-to-end network for co-saliency detection in one single image

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Science China(Information Sciences) 2023年第11期66卷 5-22页

作者： Yuanhao YUE Qin ZOU Hongkai YU Qian WANG Zhongyuan WANG Song WANG School of Cyber Science and Engineering Wuhan University School of Computer Science Wuhan University Department of Electrical Engineering and Computer Science Cleveland State University Department of Computer Science and Engineering University of South Carolina

Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.

关键词： saliency detection convolutional neural network regional feature mapping co-saliency detection deep learning

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Probabilistic Residential Load Forecasting with Sequence-to-sequence Adversarial Domain Adaptation Networks

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1559-1571页

作者： Hanjiang Dong Jizhong Zhu Shenglin Li Yuwang Miao Chi Yung Chung Ziyu Chen the School of Electric Power Engineering South China University of TechnologyGuangzhouChina the Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution *** data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and ***,this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning ***,we formulate the short-term probabilistic residential load forecasting ***,we propose a sequence-to-sequence(Seq2Seq)adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain(with massive consumption records of regular loads)to the target domain(with limited observations of new residential loads)and simultaneously minimize the domain difference and forecasting errors when solving the forecasting *** implementation,the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network,including the Seq2Seq recurrent neural networks(RNNs)composed of a long short-term memory(LSTM)encoder and an LSTM decoder,and quantile ***,this study conducts the case studies via multiple evaluation indices,comparative methods of classic machine learning and advanced deep learning,and various available data of the new residentical loads and other regular *** experimental results validate the effectiveness and stability of the proposed scheme.

关键词： Domain adaptation neural network residential load forecasting transfer learning probabilistic forecasting

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A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

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computers, Materials & Continua 2024年第6期79卷 5295-5312页

作者： Yin Ni Wu Zeng Peng Xia Guang Stanley Yang Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430023China School of Mathematics and Computer Science Wuhan Polytechnic UniversityWuhan430048China Paul G.Allen School of Computer Science and Engineering University ofWashingtonSeattleWA98195USA School of Computer Science and Engineering University of CaliforniaSanDiegoCA92093USA

Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.

关键词： Deepfake detector remote photoplethysmography fast fourier transform spatial attention mechanism

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534,I0011页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China Photonics Research Institute The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia

Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

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Coordinated Frequency Control for Isolated Power Systems with High Penetration of DFIG-Based Wind Power

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1399-1414页

作者： Xin Ding Wei Lin Jian Xu Yuanzhang Sun Liangzhong Yao Beilin Mao School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science Case Western Reserve UniversityClevelandOH 44106 USA

This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind *** proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power *** deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed *** effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.

关键词： Frequency control isolated power systems nonlinear feedback nonlinear regulator theory wind power

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Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable *** to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their *** tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible ***,since ADNs are mostly operated in a normal status,only very few historical samples are ***,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network *** address this issue,we propose an enhanced constraint learning ***,it leverages constraint learning to train a neural network as surrogate of ADN's ***,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical *** incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural ***,we establish a trust region to constrain and thereafter enhance reliability of the *** confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow

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Computing Riemann zeros with light scattering

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Physical Review B 2024年第22期110卷 L220201-L220201页

作者： Sunkyu Yu Xianji Piao Namkyoo Park Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul 02504 Korea Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea

Finding hidden order within disorder is a common interest in material science, wave physics, and mathematics. The Riemann hypothesis, stating the locations of nontrivial zeros of the Riemann zeta function, tentatively characterizes statistical order in the seemingly random distribution of prime numbers. This famous conjecture has inspired various connections with different branches of physics, recently with non-Hermitian physics, quantum field theory, trapped-ion qubits, and hyperuniformity. Here we develop the computing platform for the Riemann zeta function by employing classical scattering of light. We show that the Riemann hypothesis suggests the landscape of semi-infinite optical scatterers for the perfect reflectionless condition under the Born approximation. To examine the validity of the scattering-based computation, we investigate the asymptotic behaviors of suppressed reflections with the increasing number of scatterers and the emergence of multiple scattering. The result provides another bridge between classical physics and the Riemann zeros, exhibiting the design of wave devices inspired by number theory.

关键词： Light scattering

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Short-term Residential Load Forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1239-1249页

作者： Jizhong Zhu Yuwang Miao Hanjiang Dong Shenglin Li Ziyu Chen Di Zhang School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution ***,due to the scarcity of historical data for these new consumers,it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting *** paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this *** begin,this paper leverages the domain adversarial transfer *** employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target ***,a K-shape clustering method is proposed,which effectively identifies source domain data that align optimally with the target domain,and enhances the forecasting ***,a composite architecture is devised,amalgamating attention mechanism,long short-term memory network,and seq2seq *** composite structure is integrated into the domain adversarial transfer network,bolstering the performance of feature extractor and refining the forecasting *** illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method *** the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other comparative experimental results,underscores the reliability of the proposed *** findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecast-ing methods.

关键词： Load forecasting domain adversarial K-shape clustering long short-term memory network seq2seq network attention mechanism

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Voltage-Source Control for Green-Hydrogen Hybrid Energy Storage System with Operational Constraints and Current Rate Limiter

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IEEE Transactions on Smart Grid 2025年第4期16卷 2871-2883页

作者： Wang, Ziqiang Zhou, Lidan Xiong, Linyun Zhou, Quan Huang, Wentao Yang, Haosen Gao, Fei Chen, Yandong Department of Electrical and Electronics Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Electrical Engineering Chongqing University Chongqing China Department of Electrical and Information Engineering Hunan University Changsha China

Green hydrogen produced from renewable energy generation (RES) is facilitating the energy transition. Due to the complicated operational constraints of green-hydrogen hybrid energy storage system (GH-HESS), the existing two-layer power-based control architecture is prevalent, but it heavily relies on the communication infrastructure and the prediction accuracy of RES’s power. Therefore, this paper proposes a novel voltage-source hierarchical control framework for the grid-connected GH-HESS to solve the above problems. Considering the electricity price, the hot-standby current and safety of the hydrogen production of the electrolyzer, the current allocation between hydrogen energy storage and power grid is achieved for the continuous stable operation of GH-HESS even in the situation with potential communication failure. When the current rate limiter is adopted for the fuel starvation phenomenon of fuel cell, the current feed-forward loop and current-based secondary control are designed to solve the trade-off problem between the current overshoot and convergence speed of the tradition voltage-source control, where the large current overshoot will cause irreversible damage to hydrogen energy storage. Battery energy storage rapidly responses the power fluctuation from RES, while the steady-state power vacancy is undertaken by hydrogen energy storage. Finally, a 800V DC microgrid based on NI-PXI real-time platform is built to validate its advantages. © 2010-2012 IEEE.

关键词： Charge storage

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