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Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

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Journal of engineering and Applied Science 2024年第1期71卷 94页

作者： Kumawat, Manoj Onaolapo, Adeniyi Sharma, Gulshan Barutcu, Ibrahim Cagri Adefarati, Temitope Bansal, Ramesh Department of Electrical Engineering National Institute of Technology Delhi Delhi India Department of Electrical Engineering Technology University of Johannesburg Johannesburg2006 South Africa Department of Electricity and Energy Çölemerik V.H.S. Hakkari University Hakkari30000 Turkey Department of Electrical and Electronics Engineering Federal University Oye Ekiti Oye Ekiti Nigeria Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. © The Author(s) 2024.

关键词： Smart power grids

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Thai Winograd Schemas: A Benchmark for Thai Commonsense Reasoning 2

Thai Winograd Schemas: A Benchmark for Thai Commonsense Reas...

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2nd Workshop in South East Asian Language Processing, SEALP 2025

作者： Artkaew, Phakphum Department of Electrical and Computer Engineering Tandon School of Engineering New York University United States

ISBN: (纸本)9798891762190

Commonsense reasoning is one of the important aspects of natural language understanding, with several benchmarks developed to evaluate it. However, only a few of these benchmarks are available in languages other than English. Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a better understanding of different languages. This research introduces a collection of Winograd Schemas in Thai, a novel dataset designed to evaluate commonsense reasoning capabilities in the context of the Thai language. Through a methodology involving native speakers, professional translators, and thorough validation, the schemas aim to closely reflect Thai language nuances, idioms, and cultural references while maintaining ambiguity and commonsense challenges. We evaluate the performance of popular large language models on this benchmark, revealing their strengths, limitations, and providing insights into the current state-of-the-art. Results indicate that while models like GPT-4 and Claude-3-Opus achieve high accuracy in English, their performance significantly drops in Thai, highlighting the need for further advancements in multilingual commonsense reasoning. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Faulty Sensors Detection in Planar Array Antenna Using Fish School Search Algorithm 5

Faulty Sensors Detection in Planar Array Antenna Using Fish ...

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5th International Conference on electrical, Communication and computer engineering, ICECCE 2024

作者： Nasir, Ammar Iqbal, Shahid Khan, Shafqat Ullah Zaman, Fawad Waseem, Athar University of Gujrat Department of Electrical Engineering Gujrat Pakistan University of Buner Department of Electronics Buner Pakistan COMSATS University Department of Electrical & Computer Engineering Islamabad Pakistan International Islamic University Department of Electrical & Computer Engineering Pakistan

ISBN: (纸本)9798331529437

Antenna arrays are integral to high-performance systems such as radar, wireless communications, satellite communications, radio astronomy, and medical imaging. However, sensor failure within PAA can severely affect the radiation pattern, which may cause signal gain reduction and compromised directionality, especially when central elements are not working. This paper presents a new approach in antenna arrays for detecting faulty sensors using the Fish School Search Algorithm (FSSA). The detection problem is framed as an optimization of the Mean Square Error (MSE) between the expected and actual radiation patterns for single and multiple elements faults, enabling early identification of these faults. Simulations on both 3x3 and 9x9 planar arrays are performed to evaluate the method's accuracy, considering different fault scenarios, including central and edge element failures. Simulation results show that FSSA accurately detects faulty sensors, with central element failures having a significant impact on system performance. The proposed method enhances system reliability by detecting faults promptly and minimizing performance downtime in critical applications. © 2024 IEEE.

关键词： Mean square error

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Integrating cycleGAN and BERT for Chinese text style transfer

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Multimedia Tools and Applications 2024年 1-20页

作者： Chou, Chien-Hsing Chou, Cheng-Hou Hsieh, Yi-Zeng Yang, Tzu-Shien Department of Mathematics and Information Education National Taipei University of Education Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei Taiwan

In this study, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model with the Cycle Generative Adversarial Network (CycleGAN) to create a system for Chinese text style transfer. Natural language processing (NLP) involves converting human languages into data interpretable by computers, enabling applications like text classification, chatbots, and dialogue systems. Recent advancements, such as Google's transformer model and the BERT technique, have significantly improved NLP capabilities through self-attention mechanisms and unsupervised pretraining. Text style transfer modifies the style of texts without altering their semantics. Previous methods like StyIns and models based on disentangled representation learning highlight the challenges of retaining text meaning during style transfer. Our system leverages CycleGAN’s unsupervised learning to convert unpaired data between wuxia and fantasy styles while preserving semantics. Using the pretrained BERT model from the Chinese Knowledge and Information Processing (CKIP) Lab, our experimental results demonstrate successful style conversion, maintaining the original meanings of texts. This integration of BERT and CycleGAN shows promise for further advancements in NLP applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Utilizing Deep Reinforcement Learning for Enhanced Microgrid Voltage Regulation under Fluctuating Load Conditions 4

Utilizing Deep Reinforcement Learning for Enhanced Microgrid...

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4th International Conference on electrical engineering and computer Science, ICECOS 2024

作者： Dinata, Noer Fadzri Perdana Jambak, Muhammad Irfan Ramli, Makbul Anwari Muhammad Sidik, Muhammad Abu Bakar Universitas Sriwijaya Faculty of Engineering Department of Electrical Engineering Palembang Indonesia King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350368253

This study explores the application of Deep Reinforcement Learning (DRL) in controlling a microgrid, with a focus on evaluating the performance of a DRL-based inverter voltage regulator compared to a traditional PID-based controller. The microgrid is subjected to fluctuating load conditions, simulating real-world scenarios where load demand varies significantly. Detailed specifications for the microgrid components are provided, and both control algorithms are tested for their effectiveness in maintaining voltage stability and synchronization with the main grid. Simulation results reveal that the DRL-based controller outperforms the PID controller in tracking the main grid voltage and stabilizing the voltage profile under dynamic conditions. While islanded, the DRL controller achieves a mean absolute deviation of 0.02 from the setpoint, compared to 0.06 for the PID. In grid synchronization, the DRL maintains a synchronization error of less than 6Vrms, while the PID controller exhibits a larger error of up to 15 Vrms. The DRL controller's adaptive learning capabilities allow it to respond more effectively to rapid load changes, reducing deviations and enhancing overall performance. These findings suggest that DRL offers a promising alternative to conventional control methods for microgrids, providing improved reliability and efficiency in environments characterized by high variability. Further research is recommended to optimize DRL algorithms for better voltage stability and synchronization. © 2024 IEEE.

关键词： Deep reinforcement learning

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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Fractional Processing Based Adaptive Beamforming Algorithm

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computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： Syed Asghar Ali Shah Tariqullah Jan Syed Muslim Shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

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Distribution Systems Clustering Through Voltage Unbalance Sensitivity to Photovoltaic Presence 50

Distribution Systems Clustering Through Voltage Unbalance Se...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Leite, Rafael M. Oleskovicz, Mário Barbosa, Daniel Costa, Flavio B. Department of Electrical and Computer Engineering University of São Paulo São Carlos Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil Electrical and Computer Engineering Department Michigan Technological Univesity Houghton United States

ISBN: (纸本)9781665464543

Distributed photovoltaic micro and mini-generators are gaining prominence as they become more accessible to end consumers. These systems promise to reduce expenses related to electrical energy and offer potential financial returns. One way to enhance the application and planning of the distribution network and its hosting capacity is by partitioning these systems based on the impact of distributed generation on power quality. This study aims to develop a generic network partitioning method using the Spectral Clustering algorithm, enhanced with a balancing algorithm to ensure similar cluster sizes. The input data for this method come from modeling actual standardized electrical systems and simulating the individual impact of photovoltaic generation on each low-voltage feeder connected to the primary grid. The unbalance sensitivity coefficient, which quantifies the change in the voltage unbalance factor at one node due to the variation of active power at another, is used as a metric. Considering all simulation states, the ratios of these coefficients per node form the elements of the Sensitivity matrix. By applying Balanced Spectral Clustering to this Sensitivity matrix, the method can identify continuous electrical regions that highlight the optimal partitioning of the tested distribution system. This proposed method may serve as an initial framework for defining the hosting capacity of smaller regions, taking into account the characteristics of the entire electrical system while managing its restrictive factors locally. © 2024 IEEE.

关键词： Distributed energy

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Coordinating Systematic Grid-Forming Control of Hybrid Photovoltaic Plants in Weak Grids

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 81-89页

作者： Yu, Shiwen He, Lina the Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States

—With the anticipated integration of numerous hybrid photovoltaic (PV) plants into subtransmission and distribution grids, managing a mix of inverter-based energy resources such as PV systems and battery energy storage systems (BESS) becomes crucial. These resources are required to effectively coordinate for primary frequency (f) and voltage (V) control and participate in power sharing, particularly in weaker grids. Currently, inverter-based energy resources are predominantly coordinated by droop-based control, which proves inadequate for hybrid PV plants in more resistive subtransmission and distribution grids due to the tightly coupled active power (P) and reactive power (Q). To overcome this challenge, this paper proposes an innovative coordinating systematic primary control strategy for grid-forming inverters in hybrid PV plants based on the multiple-input and multiple-output (MIMO) decoupling control. This method adaptively decouples the connected subtransmission or distribution grids during operation, with the aim of achieving effective, coordinated, and independent primary f and V regulation and accurate power sharing. For verification, comparative case studies are conducted in Simulink between the proposed control strategy and a conventional droop control scheme. The findings indicate that our proposed control method facilitates autonomous and independent primary f and V control, along with precise power sharing without relying on communication links. This results in markedly enhanced steady-state and dynamic performance. The decentralized primary controller offers simplicity, robustness, and cost-effectiveness, contributing to the stability and resilience of utility grids. © 2024 IEEE.

关键词： Energy resources

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Decentralized Demand Management Based on Alternating Direction Method of Multipliers Algorithm for Industrial Park with CHP Units and Thermal Storage

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 120-130页

作者： Jingdong Wei Yao Zhang Jianxue Wang Lei Wu Peiqi Zhao Zhengting Jiang School of Electrical Engineering Xi’an Jiaotong UniversityXi’anChina Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenUSA

This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly ***,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be *** thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP *** heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial *** proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming *** alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high *** case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.

关键词： Alternating direction method of multipliers(ADMM) combined heat and power(CHP)unit demand management industrial park integrated demand response(IDR) thermal storage

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