检索结果-内蒙古大学图书馆

Islanding detection of synchronous distributed generators using data mining complex correlations

IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第17期12卷 3935-3943页

作者： Gomes, Eduardo A. P. Vieira, Jose C. M. Coury, Denis V. Delbem, Alexandre C. B. Univ Sao Paulo Sao Carlos Sch Engn Dept Elect & Comp Engn Ave Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Ave Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil

This study proposes a novel method based on data mining for islanding detection of synchronous distributed generators. The method uses a new technique called data mining of code repositories (DAMICORE), which is a powerful data mining tool for detecting patterns and similarities in various kinds of datasets. In addition, a trip logic was developed in this proposal in order to detect islanding and disconnect the distributed generator. One of the most relevant features of the proposed method is its capability to generalise, which reduces the need of big datasets for training purposes. This approach has been tested with islanding, load switching and fault simulations, presenting promising results concerning performance, as well as detection time. General results showed a better performance of the method if compared to traditional anti-islanding protection schemes, such as frequency-based relays.

关键词： distributed power generation data mining fault diagnosis synchronous generators power engineering computing correlation methods power generation faults

来源：评论

学校读者我要写书评

暂无评论

APPLICATION OF HIGHER-ORDER SPECTRAL-ANALYSIS TO CUBICALLY NONLINEAR-SYSTEM IDENTIFICATION

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第7期42卷 1746-1765页

作者： NAM, SW powerS, EJ UNIV TEXAS ELECTR RES CTR AUSTIN TX 78712 USA UNIV TEXAS DEPT ELECT & COMP ENGN AUSTIN TX 78712 USA

In this study, digital higher-order spectral analysis and frequency-domain Volterra system models are utilized to yield a practical methodology for the identification of weakly nonlinear time-invariant systems up to third order. The primary focus is on consideration of random excitation of nonlinear systems and, thus, the approach makes extensive use of higher-order spectral analysis to determine the frequency-domain Volterra kernels, which correspond to linear, quadratic, and cubic transfer functions. Although the Volterra model is nonlinear in terms of its input, it is linear in terms of its unknown transfer functions. Thus, a least squares approach is used to determine the optimal (in a least squares sense) set of linear, quadratic, and cubic transfer functions. Of particular practical note, is the fact that the approach of this paper is valid for non-Gaussian, as well as Gaussian, random excitation. It may also be utilized for multitone inputs. The complexity of the problem addressed in this paper arises from two principal causes: (1) The necessity to work in a 3-D frequency space to describe cubically nonlinear systems, and (2) the necessity to characterize the non-Gaussian random excitation by computing higher-order spectral moments up to sixth order. A detailed description of the approach used to determine the nonlinear transfer functions, including considerations necessary for digital implementation, is presented.

关键词： Spectral analysis Nonlinear systems Transfer functions power engineering computing Frequency domain analysis Least squares methods Frequency measurement Signal processing power system modeling Kernel

来源：评论

学校读者我要写书评

暂无评论

Intelligent system applications for power system control and management

引用

computing & CONTROL engineering JOURNAL 2001年第2期12卷 85-91页

作者： McDonald, JR McArthur, SDJ Burt, GM Univ Strathclyde Ctr Elect Power Engn Glasgow Lanark Scotland

Engineers within the electrical power industry are becoming increasingly challenged by the volume of data that must be analysed. In reality, they are interested in the significant information that can be interpreted from the data. Intelligent systems are seen as a solution to this problem, as they are able to interpret data to provide information, thereby offering decision support. This article discusses the use of intelligent systems for decision support applied to power system control and management.

关键词： decision support systems power engineering computing decision support electrical power industry power system control intelligent system applications data mining Decision support systems data analysis Control of electric power systems Expert systems and other AI software and techniques power system management Data handling techniques power system analysis computing power system management, operation and economics intelligent control data interpretation power system control Control engineering computing significant information

来源：评论

学校读者我要写书评

暂无评论

Knowledge-based neural network for voltage contingency selection and ranking

引用

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1999年第6期146卷 649-656页

作者： Srivastava, L Singh, SN Sharma, J Madhav Inst Sci & Technol Dept Elect Engn Gwalior 474005 India Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Univ Roorkee Dept Elect Engn Roorkee 247667 Uttar Pradesh India

Voltage contingency screening and ranking is performed to choose the contingencies that cause the worst voltage problems. A knowledge-based conceptual neural network (KBCNN) is developed for fast voltage contingency selection and ranking. A recognised shortcoming of the ANN based approach is that the problem solving knowledge of ANN is represented in the connection weights, and hence is difficult for a human user to comprehend. One way to provide an understanding of the behaviour of neural networks is to extract rules that can be provided to the users. The rules extracted are used to build a knowledge-based connectionist network for learning and revision of rules. The knowledge-based neural network is applied for voltage contingency selection and ranking in an IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the KBCNN gives accurate selection and ranking for unknown patterns. At the same time, the system user is able to validate the output of the ANN under all possible input conditions. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

关键词： power system security neural nets power engineering computing problem solving knowledge power transmission, distribution and supply Knowledge engineering techniques line-voltage voltage contingency ranking 75-bus Indian system knowledge-based conceptual neural network Security aspects Neural computing techniques power system analysis computing knowledge-based connectionist network knowledge-based neural network load flow learning (artificial intelligence) IEEE 30-bus system connection weights rule revision voltage contingency selection knowledge based systems power system control rule learning

来源：评论

学校读者我要写书评

暂无评论

Online blend-type identification during co-firing coal and biomass using SVM and flame emission spectrum in a pilot-scale furnace

引用

IET RENEWABLE power GENERATION 2019年第2期13卷 253-261页

作者： Zhou, Hao Li, Yuan Cen, Kefa Zhejiang Univ Inst Thermal Power Engn State Key Lab Clean Energy Utilizat Hangzhou 310027 Zhejiang Peoples R China Rundian Energy Sci & Technol Co Ltd Zhengzhou 450052 Henan Peoples R China

Co-firing coal and biomass has been applied in existing coal-fired power stations recently. Online blend-type identification was investigated by support vector machine (SVM) using flame emission spectrum for combustion optimisation. A spectrometer was used to capture the flame emission spectrum during co-combustion in a 0.3 MW furnace. A total of 22 flame features were defined and extracted from the flame emission spectrum for blend-type identification. ReliefF was applied to calculate the important weights of the extracted fame features. Alkali metals atomic excitation spectral intensities and the means of spectral signals show obviously higher important weights than the other flame features. Ultraviolet signal is more important than visible and infrared signals for blend-type identification. SVM was adopted to identify the blend types. The method of 'ReliefF + SVM' was proposed to obtain the optimum feature vector. The number of optimum features can be reduced from 22 to 17 if only the prediction accuracy is considered. The optimum sampling number is 12. At the optimum feature vector (17 features) and the optimum sampling number (12), the average prediction accuracy of the five fuels is 99.67%. The results demonstrate that combining SVM and flame emission spectrum is suitable for online blend-type identification during co-combustion.

关键词： support vector machines power engineering computing bioenergy conversion furnaces coal feature extraction steam power stations online blend-type identification cofiring coal biomass ReliefF plus SVM method support vector machine flame emission spectrum pilot-scale furnace coal-fired power stations combustion optimisation flame feature extraction alkali metals atomic excitation spectral intensities spectral signals ultraviolet signal optimum sampling number optimum feature vector

来源：评论

学校读者我要写书评

暂无评论

A study on the effects of electromagnetic coupling mechanisms in the event of an indirect lightning strike near photovoltaic arrays

引用

IET RENEWABLE power GENERATION 2023年第4期17卷 935-950页

作者： Hernandez, Jesus De La Casa Sutil, Francisco Jose Sanchez Petrarca, Carlo Formisano, Alessandro Univ Jaen Dept Elect Engn Campus Lagunillas s-nEdificio A3 Jaen Spain Univ Napoli Federico II Dept Elect Engn & Informat Technol Naples Italy Univ Campania Luigi Vanvitelli Dept Engn Aversa Caserta Italy Univ Campania Luigi Vanvitelli Dept Engn I-81031 Aversa Caserta Italy

Indirect lightning strikes hitting near photovoltaic installations are much more frequent than direct hits, and can generate overvoltage in their electric circuitry. The authors developed a method to estimate the induced overvoltage on single photovoltaic modules. Using a model of the whole array also including Inter-Module Coupling Mechanisms (IMCMs), a new approach to estimate overvoltage induced on full arrays is presented in this paper. The procedure first estimates lumped parameters, modelling either the coupling between the lightning channel and each module in the array or between each couple of modules in array. Several circuital simulations are then run using suitable equivalent circuits. The aim is to consider different array configurations and groundings schemes. This allows extracting the most relevant configuration parameters influencing overvoltage/overcurrent. Results show that the groundings scheme impacts negligibly, but the single module electric stress is decreased by 35% in the case of ungrounded configurations. Among the considered array configurations, the honey-comb and total cross-tied showed the smallest impact on overvoltage. Statistical analysis on random LC tortuous geometry leads to a non-negligible rise of overvoltage peak values, up to 24%. Finally, the relevance of different IMCMs is analysed, showing a strong impact just on frame-ground voltages and currents.

关键词： indirect lightning strike lightning Solar power stations and photovoltaic power systems different array configurations lightning channel groundings schemes equivalent circuits direct hits ungrounded configurations power system protection lightning protection relevant configuration parameters overvoltage peak values induced overvoltage considered array configurations overvoltage protection Inter-Module Coupling Mechanisms statistical analysis overvoltage earthing different IMCMs single module electric stress suitable equivalent circuits single photovoltaic modules photovoltaic arrays groundings scheme power engineering computing circuital simulations circuit simulation electric circuitry lumped parameters parameters, modelling Overhead power lines electromagnetic coupling mechanisms electromagnetic coupling photovoltaic installations

来源：评论

学校读者我要写书评

暂无评论

NATURAL-LANGUAGE INTERFACE AND DATABASE ISSUES IN APPLYING EXPERT SYSTEMS TO power-SYSTEMS

引用

PROCEEDINGS OF THE IEEE 1992年第5期80卷 758-764页

作者： RUMPEL, D KROST, G Elektrische Anlagen und Netze Univ. of Duisburg Gesamthochschule

Applying expert systems (ES's) to power system operation means imbedding them in an existing environment which consists of computational facilities (SCADA, EMS) on one side, and the operating staff on the other side. To that aim, an interface between process data and ES data is required. Further, for an unambiguous and clear information exchange with, and good acceptance by, the operators an approximate natural language dialog of the ES is desirable. Several approaches to these problems are described.

关键词： EMS ES data SCADA computational facilities database issues expert systems expert systems natural language dialogue natural languages power engineering computing power system operation power systems power systems process data user interfaces

来源：评论

学校读者我要写书评

暂无评论

Deep learning model to detect various synchrophasor data anomalies

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5739-5745页

作者： Deng, Xianda Bian, Desong Wang, Weikang Jiang, Zhihao Yao, Wenxuan Qiu, Wei Tong, Ning Shi, Di Liu, Yilu Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

关键词： learning (artificial intelligence) phase measurement power system measurement phasor measurement power grids power engineering computing pattern classification convolutional neural nets synchrophasor data anomaly detection high-density synchrophasors communication instability hardware failure data quality convolutional neural network model synchrophasor network abnormal synchrophasor measurements deep learning Jiangsu grid China synchrophasor data anomaly classification

来源：评论

学校读者我要写书评

暂无评论

A PARALLEL COMPUTATION ALGORITHM FOR STATIC STATE ESTIMATION BY MEANS OF MATRIX-INVERSION LEMMA

引用

IEEE TRANSACTIONS ON power SYSTEMS 1987年第3期2卷 624-632页

作者： SASAKI, H AOKI, K YOKOYAMA, R TOKYO METROPOLITAN UNIV DEPT ELECT ENGNTOKYO 158JAPAN

This paper presents an effective parallel computation algorithm for the static state estimation of a power system. The proposed algorithm makes use of the matrix inversion lemma and assumes the use of a MIMD type computer system. The optimal block-partitioning algorithm which tears a power system into several blocks is presented and its clear difference from that for the load flow is clarified. Simulation results on systems of up to 135 nodes have demonstrated the algorithm is very promising to the speed up of the state estimation.

关键词： Concurrent computing State estimation power engineering computing power system simulation Load flow power system measurements Hardware power systems Equations IEEE members

来源：评论

学校读者我要写书评

暂无评论

Development of a DSO support tool for congestion forecast

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2021年第23期15卷 3345-3359页

作者： Srivastava, Ankur Steen, David Tuan, Le Anh Carlson, Ola Bouloumpasis, Ioannis Tran, Quoc-Tuan Lemius, Lucile Chalmers Univ Technol Div Elect Power Engn Dept Elect Engn Gothenburg Sweden Univ Grenoble Alpes Dept Solar Technol CEA LITENINES Le Bourget du Lac France Atos Worldgrid Dept Smart Util Grenoble France

This paper presents a novel DSO support tool with visualisation capability for forecasting network congestion in distribution systems with a high level of renewables. To incorporate the uncertainties in the distribution systems, the probabilistic power flow framework has been utilised. An advanced photovoltaic production forecast based on sky images and a load forecast using an artificial neural network is used as the input to the tool. In addition, advanced load models and operating modes of photovoltaic inverters have been incorporated into the tool. The tool has been applied in case studies to perform congestion forecasts for two real distribution systems to validate its usability and scalability. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and is able to visualise the cumulative probabilities of nodes voltage deviation and network components (branches and transformers) congestion for a variety of forecast horizons as desired by the DSO. The results have also shown that explicit inclusion of load-voltage dependency models would improve the accuracy of the congestion forecast. For demonstrating the applicability of the tool, it has been integrated into an existing distribution management system via the IoT platform of a DMS vendor, Atos Worldgrid.

关键词： Other topics in statistics power system management, operation and economics power system planning and layout Solar power stations and photovoltaic power systems Other topics in statistics Graphics techniques power engineering computing DC-AC power convertors (invertors) Neural nets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：