e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献_第9页

Neural Prophet driven day-ahead forecast of global horizontal irradiance for efficient micro-grid management 神经先知驱动的全球水平辐照度日前预测，用于高效微电网管理

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-20 DOI: 10.1016/j.prime.2024.100817

Stephen Oko Gyan Torto , Rupendra Kumar Pachauri , Jai Govind Singh

This study introduces an innovative approach to day-ahead solar irradiance forecasting, utilizing the NeuralProphet model—a deep learning-based extension of the Prophet tool—to effectively manage the complexities of time-series data in solar energy prediction. Recognizing the critical role of accurate solar irradiance predictions in optimizing the operation of multi-vectored energy hubs, this research integrates NeuralProphet's advanced neural network components, including its trend and seasonality modules, to enhance forecasting accuracy. The innovative integration of NeuralProphet's trend, seasonality, and autoregressive components allows for superior performance in forecasting compared to traditional models. When the model's performance is compared to historical solar irradiance data, it is evident how well it captures underlying trends in comparison to more conventional approaches. In contrast, Dataset 1 has a daily forecast MAE for the model that is about 38.6 % lower than Dataset 2, but Dataset 1 has weekly and monthly forecast MAEs that are 6.25 % and 5.6 % higher, respectively. Better day ahead accuracy is also shown by the daily forecast MAPE for Dataset 1 being 45.1 % lower than for Dataset 2. Furthermore, Dataset 1 has a daily R² value of 99.5 %, while Dataset 2 has a value of 99.0 %. This suggests that Dataset 1 has 0.5 % more accurate day ahead forecasts. There is a 0.1 % increase in accuracy as evidenced by the weekly R² values for Dataset 1, which is 98.4 %, while Dataset 2 is 98.3 %. The R² for monthly projections shows that Dataset 1 has a 0.5 % poorer accuracy over longer time horizons, with 95.6 % for Dataset 1 and 96.1 % for Dataset 2. These results demonstrate the model's potential to optimize the operation of energy hubs by accurately forecasting GHI, contributing to more efficient micro-grid management and a reduction in dependency on fossil fuels. The findings demonstrate that deep learning techniques can be integrated into renewable energy forecasting, offering substantial benefits for the design and management of future energy systems..

本研究介绍了一种创新的日前太阳辐照度预测方法，利用 NeuralProphet 模型--基于深度学习的 Prophet 工具扩展--有效管理太阳能预测中复杂的时间序列数据。由于认识到准确的太阳辐照度预测在优化多辐照能源枢纽运行中的关键作用，这项研究集成了 NeuralProphet 的先进神经网络组件，包括其趋势和季节性模块，以提高预测的准确性。与传统模型相比，NeuralProphet 的趋势、季节性和自回归组件的创新整合使其预测性能更加卓越。将该模型的性能与历史太阳辐照度数据进行比较，可以明显看出，与传统方法相比，该模型能很好地捕捉潜在趋势。相比之下，数据集 1 的模型日预测 MAE 比数据集 2 低约 38.6%，但数据集 1 的周和月预测 MAE 分别高出 6.25% 和 5.6%。数据集 1 的日预报 MAPE 比数据集 2 低 45.1%，这也表明数据集 1 的日预报精度更高。此外，数据集 1 的日 R2 值为 99.5%，而数据集 2 为 99.0%。这表明数据集 1 的前一天预测准确率高出 0.5%。数据集 1 的每周 R2 值为 98.4%，而数据集 2 为 98.3%，这表明数据集 1 的准确率提高了 0.1%。月度预测的 R2 值显示，在更长的时间跨度内，数据集 1 的准确率比数据集 2 低 0.5%，数据集 1 为 95.6%，数据集 2 为 96.1%。这些结果表明，该模型具有通过准确预测 GHI 优化能源枢纽运行的潜力，有助于提高微电网管理效率，减少对化石燃料的依赖。研究结果表明，深度学习技术可以集成到可再生能源预测中，为未来能源系统的设计和管理带来巨大好处。

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引用次数: 0

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors 增强电力变压器热建模：考虑外部环境因素的键合图和 IEEE 方法比较

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-20 DOI: 10.1016/j.prime.2024.100812

Vinit Mehta , Jayashri Vajpai

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

电力系统的优化和安全运行在很大程度上受到电力变压器在运行和经济方面可靠性的影响。对电力变压器的持续监控对于确保电力供应质量至关重要。因此，电力变压器的热建模对于防止其故障至关重要。本文旨在通过考虑太阳辐照和风量这两个外部影响参数，设计运行中的电力变压器热模型。本文通过与基于 IEEE 标准的热模型所获得的结果以及实际读数的比较，介绍了基于邦德图 (BG) 方法的运行状态下电力变压器热模型设计。这些模型在焦特布尔 Sardarpura 33 千伏变电站运行的一台 5 兆伏安、33/11 千伏电力变压器上实现，最初考虑的是输入的每小时负荷和环境温度数据集。随后，通过整合太阳辐射和风对电力变压器表面的影响，对设计的热模型进行了更新。对所有设计的热模型进行了比较研究，结果表明，如果将太阳辐射和风流的影响考虑在内，模型的精确度会有所提高。此外，基于 BG 的热模型在准确预测系统行为方面的有效性验证了其在实际工程应用中的适用性。

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引用次数: 0

Integrating renewable energy communities and Italian UVAM project through renewable hydrogen chain 通过可再生氢链整合可再生能源社区和意大利 UVAM 项目

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-19 DOI: 10.1016/j.prime.2024.100819

Giulio Raimondi , Giuseppe Spazzafumo

Renewable energy communities (RECs) in Italy could play an important role in increasing the stock of electric renewable generation in the coming years. Their impact on the electric grid could be non-negligible. At the same time with increased electric generation from renewables, a greater need for electric national grid balancing is expected, preferably based on zero-emission energy storage. Based on currently existing entities in Italian regulation framework (i.e. REC and UVAM project), the present work proposes an original business model to create a storage of dispatchable renewable hydrogen to be used for balancing National electric grid. Hydrogen production is from excess of renewables in RECs constituted in the same area. UVAM project allows to access the ancillary services market with the minimum capacity of 1 MW that appears appropriate to the proposed scenario. Based on current technical-economic constraints of the technologies involved (electrolysis and both fuel cell and internal combustion engine fed by hydrogen), proposed business model is not day-present economic feasible: the return on investment is never positive. The cost forecasts available for 2035 indicate that the business model will be likely sustainable, with a net present value becoming positive for configurations with more than 3000 people involved in the RECs, with a photovoltaic penetration condition of 1.8 kWp/capita. This research work suggests an original business model for managers of RECs with an excess of renewables generation.

未来几年，意大利的可再生能源社区（RECs）将在增加可再生能源发电存量方面发挥重要作用。它们对电网的影响不容忽视。同时，随着可再生能源发电量的增加，预计国家电网平衡的需求也会增加，最好是基于零排放的能源存储。基于意大利监管框架中的现有实体（即 REC 和 UVAM 项目），本研究提出了一种独创的商业模式，以创建可调度的可再生氢存储，用于平衡国家电网。氢气生产来自于同一地区 REC 中过剩的可再生能源。UVAM 项目允许以 1 兆瓦的最小容量进入辅助服务市场，这似乎与拟议的方案相匹配。基于目前相关技术（电解技术、燃料电池和氢气内燃机）的技术经济限制，所提议的商业模式在当前经济条件下并不可行：投资回报率永远不会是正数。对 2035 年的成本预测表明，该商业模式很可能是可持续的，在参与可再生能源中心的人数超过 3000 人、光伏渗透率达到 1.8 千瓦/人的条件下，净现值将为正值。这项研究工作为可再生能源发电量过剩的可再生能源中心的管理者提出了一种独创的商业模式。

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引用次数: 0

Forecasting thermoelectric power generation through utilization of waste heat from building cooling systems based on simulation 基于仿真的建筑冷却系统余热利用热发电预测

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-18 DOI: 10.1016/j.prime.2024.100821

Catur Harsito , Riyadi Muslim , Eki Rovianto , Yudi Kurniawan , Fathin Muhammad Mahdhudhu

The transformation of sustainable energy use is one of the main challenges facing the world today. Waste heat from industrial processes and conventional power plants is one form of energy waste that is often wasted. In this context, research on thermoelectric energy conversion systems is a very relevant and important topic to research. This research investigates about the potential for utilizing thermoelectric elements with stacked materials that utilize waste heat from building cooling systems. Numerical simulation was chosen to carry out further analysis regarding the potential energy produced and the efficiency of the materials used. Three-dimensional design and ANSYS is used as a three-dimensional simulation analysis tool. The research results show that thermoelectric systems with stacked materials have the potential to produce energy from waste heat. Material (Cu₂Se⁺, BiTe⁺) (Bi₂S₃^-, CuFeS^-) with 10 mm legs has the highest output power of 32.82 mW whereas (PbSe⁺, BiTe⁺) (Bi₂S₃^-, AgInSe^-) with a leg length of 20 mm has the highest efficiency value of 25.97%, and a power value of 6 .92mW. Full system research can produce values that more closely resemble actual conditions.

转变能源的可持续利用方式是当今世界面临的主要挑战之一。工业流程和传统发电厂产生的余热是一种经常被浪费的能源废物。在这种情况下，热电能源转换系统研究是一个非常相关和重要的研究课题。本研究调查了利用建筑冷却系统废热的叠层材料热电元件的利用潜力。我们选择了数值模拟来进一步分析所产生的潜在能量和所用材料的效率。三维设计和 ANSYS 被用作三维模拟分析工具。研究结果表明，使用叠层材料的热电系统具有从废热中产生能量的潜力。材料（Cu2Se+、BiTe+）（Bi2S3-、CuFeS-）的支脚长度为 10 毫米，输出功率最高，达到 32.82 mW；而材料（PbSe+、BiTe+）（Bi2S3-、AgInSe-）的支脚长度为 20 毫米，效率最高，达到 25.97%，功率值为 6.92mW。全系统研究可以产生更接近实际条件的数值。

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引用次数: 0

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks 用于中压配电网络故障检测和特征描述的卷积神经网络方法

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-18 DOI: 10.1016/j.prime.2024.100820

Atefeh Pour Shafei , J.Fernando A. Silva , J. Monteiro

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

停电严重影响了电力行业，破坏了社会福利和经济稳定。然而，由于负载和网络拓扑结构、条件以及安装的设备等原因，现有的故障检测方法仍面临挑战。然而，人工智能（AI）的最新进展使研究人员能够创造出故障检测和定位策略的替代方法。因此，本文介绍了一种通过电压波形分析检测、分类和定位电力系统中故障的新方法，该方法使用卷积神经网络 (CNN) 与片断函数拼合 (PFPT) 算法相结合，用于配电网络中的故障检测和故障区定位。利用 Park 变换、降噪 PFPT 正弦拟合和 CNN，所提出的方法可区分 "健康 "和 "故障 "状态。仿真结果表明，虽然健康系统的电压帕克矢量时间行为保持稳定，但在故障条件下，它表现出循环或混合模式。通过分析网络总线上的三维电压帕克波形，这些模式能够识别四种类型的短路故障--单线对地（LG）、线对线（LL）、线对线对地（LLG）和三线（3L）故障。该研究通过观察基于时间的电压波形正弦拟合得到的旋转帕克矢量，验证了故障类型的识别。通过将三维电压波形转换为高分辨率图像，该方法利用 CNN 进行故障识别，准确率达到 93.1%。这种创新方法强调了传统电气工程技术与现代人工智能相结合的稳健性和精确性。

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引用次数: 0

Biomedical device powered by triboelectric nanogenerator 由三电纳米发电机驱动的生物医学设备

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-17 DOI: 10.1016/j.prime.2024.100811

Guru Prasad Murugan , Abiudh Durairaj R , Sharan Kishore R , Dr. Manjula Devi R , Dr. Jeyalakshmi Velusamy

Biomedical devices play vital roles in health monitoring. Operability of these devices is hindered by their limited battery life. In vivo monitoring, diagnosis, and treatment have become challenging. The proposed Triboelectric Nanogenerator helps in overcoming the shackles of battery life. This article describes the process involved in the development of a healthcare device powered by triboelectric effect. In this work a contact-separation mode based triboelectric nanogenerator (TENG) has been used to power the device. TENG uses triboelectric phenomenon to transform mechanical energy into electrical energy. A contact-separation mode TENG operates through the interaction of two triboelectric materials. These materials act as an anode and a cathode, respectively, and develop opposite charges when brought into contact. Upon separation, the charged surfaces retain their individual charges, creating a potential difference between the materials. This difference generates an electrostatic field that drives the flow of electrons from one electrode to the other. As the electrons return, the field collapses, and the materials come back into contact, repeating the cycle. The device has been used to power a heart rate monitoring system. Experimental results demonstrate the output performance and long-term durability of the TENG device. Furthermore, future research, challenges and opportunities have been elaborated.

生物医学设备在健康监测方面发挥着重要作用。由于电池寿命有限，这些设备的可操作性受到阻碍。体内监测、诊断和治疗已成为一项挑战。拟议的三电纳米发电机有助于克服电池寿命的桎梏。本文介绍了利用三电效应驱动医疗设备的开发过程。在这项工作中，基于接触分离模式的三电纳米发电机（TENG）被用来为设备供电。TENG 利用三电现象将机械能转化为电能。接触分离模式 TENG 通过两种三电材料的相互作用而工作。这些材料分别作为阳极和阴极，在接触时会产生相反的电荷。分离时，带电表面保留各自的电荷，在材料之间产生电位差。这种电位差会产生一个静电场，推动电子从一个电极流向另一个电极。当电子返回时，静电场崩溃，材料重新接触，重复循环。该装置已用于为心率监测系统供电。实验结果证明了 TENG 设备的输出性能和长期耐用性。此外，还阐述了未来的研究、挑战和机遇。

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引用次数: 0

Effect of temperature and magnetic field on the density of surface states in semiconductor heterostructures 温度和磁场对半导体异质结构表面态密度的影响

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-15 DOI: 10.1016/j.prime.2024.100815

U.I. Erkaboev, N.Yu. Sharibaev, M.G. Dadamirzaev, R.G. Rakhimov

In this article, the physical properties of the surface of the CdS/Si(p) material under the influence of a magnetic field were studied . The dependence of the density of surface states of the p-type Si(p) semiconductor on the magnetic field and temperature has been studied. For the first time, a mathematical model has been developed to determine the temperature dependence of the density of surface states of a semiconductor under the influence of a strong magnetic field. Mathematical modeling of processes was carried out using experimental values of the continuous energy spectrum of the density of surface states, obtained at various low temperatures and strong magnetic fields, in the band gap of silicon. The possibility of calculating discrete energy levels is demonstrated.

本文研究了磁场影响下 CdS/Si(p)材料表面的物理特性。研究了 p 型 Si(p)半导体表面态密度对磁场和温度的依赖性。首次建立了一个数学模型来确定强磁场影响下半导体表面态密度的温度依赖性。利用在不同低温和强磁场条件下获得的硅带隙表面态密度连续能谱的实验值，对这一过程进行了数学建模。证明了计算离散能级的可能性。

引用次数: 0

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks 用于电网智能分区的多层约束频谱 K 嵌入聚类方法，以增强输电网络的复原力

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-13 DOI: 10.1016/j.prime.2024.100813

E Priya, J Preetha Roselyn

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

通过对电网进行智能分区来实现有意控制的孤岛化，对于保护电网免受连锁事件、故障和高影响低概率（HILP）事件的影响至关重要。为了增强电网的恢复能力、稳定性和安全性，本文提出的模型有意将受影响的电网划分为若干个孤岛。本文提出了一种智能分区方法，通过使用频谱聚类的多层图来创建孤岛解决方案。受控孤岛算法采用多标准目标函数，该函数考虑了母线频率之间的相关系数以及实际和无功功率中的最小干扰。建议的控制技术分两个阶段实施。第一阶段利用母线频率之间的相关系数和模块化聚类来识别一致性母线群。在第二阶段，使用多级约束频谱聚类（ML-CSC）将所有节点归类，以确定有意控制孤岛的解决方案，该方案既能满足总线一致性，又能将跨边界的实际和无功功率流的干扰程度降至最低。通过在 DIGSILENT Powerfactory 2023 版中开发的 IEEE 39 总线输电测试系统上进行仿真实验，证明了所提出的解决发电机一致性问题的算法和智能孤岛解决方案。MATLAB R2023a 版本用于构建 ML-CSC 控制方法。结果表明，所提出的 ML-CSC 算法对电力系统的运行产生了重大影响，能够在异常情况下形成智能孤岛。此外，结果还清楚地表明，与使用单层频谱聚类相比，多层频谱聚类能产生更好的智能孤岛解决方案，将功率流失配降至最低，从而增强孤岛的暂态稳定性。

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引用次数: 0

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis 协调智能逆变器支持的分布式能源资源，以优化现代配电网络中的光伏-BESS 集成和电压稳定性：系统综述和文献计量分析

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-12 DOI: 10.1016/j.prime.2024.100800

Olufunke Abolaji Balogun, Yanxia Sun, Peter Anuoluwapo Gbadega

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

在现代配电网络中整合光伏（PV）和电池储能系统（BESS）既是机遇也是挑战，尤其是在保持电压稳定和优化能源资源方面。本系统综述和文献计量分析研究了智能逆变器支持的分布式能源资源（DER）的协调问题，以加强光伏与电池储能系统（BESS）的整合，确保电压稳定。该研究综合了智能逆变器技术的最新进展，这些技术可提供电压/电压升压控制等电网支持功能，以及它们在 DER 协调中的应用。研究对文献进行了全面回顾，以确定该领域的流行趋势、研究空白和新兴技术。采用文献计量分析法对研究状况进行量化，突出关键出版物、引文、每个国家的出版物以及合作网络。研究结果表明，智能逆变器在缓解配电网中的电压违规和提高光伏系统的承载能力方面发挥着至关重要的作用。此外，最佳逆变器设置、光伏-BESS 的战略布局以及先进的控制算法被认为是有效整合 DER 的关键因素。研究最后提出了未来的研究方向，包括探索智能逆变器与传统电网管理系统的互动，以及开发用于动态和自适应 DER 协调的强大算法。本综述是研究人员和从业人员的宝贵资源，旨在通过先进的 DER 管理策略提高配电网络的稳定性和效率。

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引用次数: 0

Development of an intelligent-based telemetry hexapod robotic system for surveillance of power system components 开发基于智能的遥测六足机器人系统，用于监控电力系统组件

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-10-12 DOI: 10.1016/j.prime.2024.100806

Oluwaseun O. Tooki , Abdurrhman A. Aderinto , Olawale M. Popoola

A robot is an artificial system that performs specific programmed tasks to aid day-to-day human activities. Robot finds applications in industries for large-scale production, healthcare for computer-aided surgeries, security for surveillance of electrical power system equipment, and the military for carrying out dangerous reconnaissance. Most robotic systems employed for different operations use a wheeled mode of locomotion which has limitations over rough terrains and the issue of communication setup of the system. This work developed a hexapod, a six-legged robotic system, to overcome these identified challenges. The robot was developed in tandem with a telemetry system for surveillance. Although, automating the surveillance process using robotic systems has been in the works for some time. However, this work developed a telemetry hexapod robotic system for enhanced surveillance for the security of power systems equipment and other critical infrastructures using a Raspberry Pi for fast, secure data transmission, and precise system synchronization. An inverse kinematics approach was used to determine joint configurations for better positions of each endpoint. A digital camera was integrated into the robot to relay real-time images of adversaries of power system components intelligently. In addition, the system incorporates data processing capability. In the result obtained, the developed telemetry hexapod robotic system receives instructions over a distance slightly above 300 m. It establishes effective control of the telemetry system without any hindrance of a limited range as observed in Bluetooth-controlled systems. The system's performance includes an average efficiency of 98.2 %, latency of 0.48 s at the peak distance, and an endurance of 4 h. Further analysis of the system shows that the corresponding increase in the latency with the distance is negligible. The system performed better than other related systems considered in the literature. The correct implementation of the developed telemetry hexapod robotic system further enhances mobility, stability in rough terrain, reliable communication system. Also, it brings about a notable reduction in component theft in electrical power systems.

机器人是一种人工系统，可执行特定的编程任务，辅助人类的日常活动。机器人可应用于工业领域的大规模生产、医疗保健领域的计算机辅助手术、安全领域的电力系统设备监控，以及军事领域的危险侦察。大多数用于不同作业的机器人系统都使用轮式运动模式，这种运动模式在崎岖地形上有局限性，而且还存在系统通信设置的问题。这项研究开发了一种六足机器人系统（hexapod），以克服上述挑战。该机器人是与用于监控的遥测系统一起开发的。虽然使用机器人系统实现监控过程自动化已有一段时间。但是，这项工作开发了一个遥测六足机器人系统，利用树莓派（Raspberry Pi）实现快速、安全的数据传输和精确的系统同步，以加强对电力系统设备和其他关键基础设施的安全监控。该系统采用逆运动学方法确定关节配置，以便更好地确定每个端点的位置。机器人中集成了数码相机，可智能传输电力系统组件对手的实时图像。此外，该系统还具有数据处理能力。结果表明，所开发的遥测六足机器人系统可在略高于 300 米的距离内接收指令，并对遥测系统进行有效控制，而不会像蓝牙控制系统那样受到有限范围的阻碍。该系统的性能包括 98.2% 的平均效率、0.48 秒的峰值距离延迟和 4 小时的续航时间。该系统的性能优于文献中的其他相关系统。正确实施所开发的遥测六足机器人系统可进一步提高移动性、在崎岖地形中的稳定性和可靠的通信系统。此外，它还显著减少了电力系统中的元件失窃现象。

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