2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)最新文献_第6页

Predictive Maintenance of Air Conditioning Systems Using Supervised Machine Learning 使用监督机器学习的空调系统预测性维护

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065995

Shrishti Trivedi, Sahil Bhola, Archit Talegaonkar, P. Gaur, Shreya Sharma

引用次数: 4

A Framework for Cyber-Physical Model Creation and Evaluation 一种网络物理模型创建与评估框架

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065990

A. Sahu, Hao Huang, K. Davis, S. Zonouz

{"title":"A Framework for Cyber-Physical Model Creation and Evaluation","authors":"A. Sahu, Hao Huang, K. Davis, S. Zonouz","doi":"10.1109/ISAP48318.2019.9065990","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065990","url":null,"abstract":"In power systems, a cyber-physical model can play a significant role in contingency ranking to assist operators with preventive plans for cyber-related contingencies by identifying the most significant ones. Diverse cyber-physical models based on attack trees and graphs, fault trees, Markov state-space etc. have been proposed and are being developed by researches depending on specific objective. However, prior to the deployment of the models in real world, it is essential to evaluate the performance based on their computational bottlenecks, scalability and accuracy. This paper thus introduces a software-based model comparison framework that allows researchers to improve their models and also evaluate new models against existing ones. Additionaly, we present the algorithms of two cyber-physical modeling engines targeted for contingency and critical assets ranking; based on Attack Graph Analysis (AGA) and Markov Decision Process (MDP) and compare their performance. The models are evaluated for three different use cases: IEEE-24, CyPSA 8-substation, and IEEE-300 systems on cyber-physical model parameters such as MDP size, computation time of generation, number of attack paths, etc. This framework will not only allow us to design and validate models but also provide a platform to researches worldwide to test new models. Further an application is developed for visualization with one-line diagram and ranking of contingencies and critical assets.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Fast Generation Redispatch Techniques for Automated Remedial Action Schemes 自动补救行动方案的快速再调度技术

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065961

Hao Huang, M. Kazerooni, S. Hossain-McKenzie, Sriharsha Etigowni, S. Zonouz, K. Davis

引用次数: 7

Generating Feature Sets for Day-Ahead Load Demand Forecasting Using Deep Neural Network 基于深度神经网络的日前负荷需求预测特征集生成

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065979

Sonu Jha, Seetaram Maurya, N. Verma

{"title":"Generating Feature Sets for Day-Ahead Load Demand Forecasting Using Deep Neural Network","authors":"Sonu Jha, Seetaram Maurya, N. Verma","doi":"10.1109/ISAP48318.2019.9065979","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065979","url":null,"abstract":"The performance of load demand forecasting plays a vital role in economic operation and planning in the power sector. There are several methodologies in the literature for predicting load. However, there is still an essential need to develop more accurate load forecast method. The performance of these methods can be improved by using an effective machine learning methods by selecting informative feature sets. In this paper, at first, we choose the effective time lags based feature by using auto-correlation and cross-correlation. Then, more robust features have been extracted by using Principal Component Analysis (PCA) and Autoencoder (AE) based Deep Neural Network (DNN). Extracted features are provided as an input to the Artificial Neural Network (ANN) model. ANN with Levenberg-Marquardt (LM) training algorithm has been used for day-ahead load forecasting (DALF) using the extracted features. The proposed method is AE based DNN for features extraction followed by ANN with LM training algorithm. The proposed method has been compared with ANN and PCA-ANN. The performance evaluation for DALF has been analyzed on two different substations of New England Independent System Operator (NE-ISO) dataset. Each dataset is analyzed for two separate cases. The performance of the proposed approach is better than ANN and PCA-ANN method.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

EPSO enhanced by adaptive scaling and sub-swarms 采用自适应尺度和子群增强EPSO

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065982

Vladimiro Miranda, J. Vigo, L. Carvalho, C. Marcelino, E. Wanner

引用次数: 2

A Machine Learning Approach to the Identification of Voltage Control Area Using Synchrophasor Measurements 利用同步量测量识别电压控制区域的机器学习方法

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065931

Fazle Kibriya, D. Mahto, D. Mohanta

{"title":"A Machine Learning Approach to the Identification of Voltage Control Area Using Synchrophasor Measurements","authors":"Fazle Kibriya, D. Mahto, D. Mohanta","doi":"10.1109/ISAP48318.2019.9065931","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065931","url":null,"abstract":"Voltage instability has caused a great deal of concern in the existing power systems. Despite best efforts, it is still not uncommon a phenomenon and has caused some of the major blackouts and catastrophic failures in the recent past, resulting in huge social and economic losses. The advent of synchrophasor technology has made possible wide-area measurements in real-time, and has found huge applications in power systems. The identification of subregions in power systems that experience a unique voltage instability problem is one of the most important steps of voltage stability analysis. This paper presents a method to identify voltage control area (VCA), based on coherent groups of buses, using system states obtained from synchronized phasor measurements. The coherency is identified by applying hierarchical clustering, a machine learning technique. The coherent buses are identified by applying the machine learning technique on the angles obtained from bus voltage phasors. The results so obtained using the data from Phasor Measurement Units (PMUs) on 10-machine, 39-bus New England power system model are presented.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129166213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PMU Based Data Driven Approach For Online Dynamic Security Assessment in Power Systems 基于PMU的电力系统在线动态安全评估方法

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065968

Prajwal Kumar Jaiswal, Sayari Das, B. K. Panigrahi

{"title":"PMU Based Data Driven Approach For Online Dynamic Security Assessment in Power Systems","authors":"Prajwal Kumar Jaiswal, Sayari Das, B. K. Panigrahi","doi":"10.1109/ISAP48318.2019.9065968","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065968","url":null,"abstract":"This paper presents a methodology for utilizing Phasor Measurement units (PMUs) for procuring real time synchronized measurements for assessing the security of the power system dynamically. The concept of wide-area dynamic security assessment considers transient instability in the proposed methodology. Intelligent framework based approach for online dynamic security assessment has been suggested wherein the database consisting of critical features associated with the system is generated for a wide range of contingencies, which is utilized to build the data mining model. This data mining model along with the synchronized phasor measurements is expected to assist the system operator in assessing the security of the system pertaining to a particular contingency, thereby also creating possibility of incorporating control and preventive measures in order to avoid any unforeseen instability in the system. The proposed technique has been implemented on IEEE 39 bus system for accurately indicating the security of the system and is found to be quite robust in the case of noise in the measurement data obtained from the PMUs.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128781592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods 用聚类方法确定需求响应方案的最优数量和电价

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065957

C. Silva, P. Faria, Z. Vale

引用次数: 2

Design and Tuning of Multi-Band Based Power System Stabilizer and Implementation in HYPERSIM 基于多频段电力系统稳定器的设计与调谐与HYPERSIM实现

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065952

Ajit Kumar, Amine Bahjaoui, S. Musunuri, Biswajeet Rout

引用次数: 4

Fault Classification in Power Distribution Systems using PMU Data and Machine Learning 基于PMU数据和机器学习的配电系统故障分类

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) Pub Date : 2019-12-01 DOI: 10.1109/ISAP48318.2019.9065966

F. L. Grando, A. Lazzaretti, M. Moreto, H. S. Lopes

{"title":"Fault Classification in Power Distribution Systems using PMU Data and Machine Learning","authors":"F. L. Grando, A. Lazzaretti, M. Moreto, H. S. Lopes","doi":"10.1109/ISAP48318.2019.9065966","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065966","url":null,"abstract":"This work presents the analysis of machine learning methods for fault (short-circuit) classification in electrical distribution networks using data from PMUs (Phasor Measurement Units) installed along the network. The Alternative Transient Program was used to simulate 26,928 different instances distributed into 33 types of faults – single and multi-phase, including or not the ground and different wire breakages – and one normal condition of the system. The IEEE 123-bus distribution system was used as the test system. We compared five machine learning methods for classification: Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Decision Trees (DTs). The best result was achieved by the SVM with Gaussian kernel and ANN. The input data (feature extraction) was also varied, testing data from one or several PMUs, ABC sequence phasors and symmetrical sequence phasors. We obtained slightly better results for symmetrical components and multiple PMUs in the network. Finally, classes of the same short-circuit with different wire breakages were grouped, raising the overall classification accuracy, showing the feasibility of this approach for fault classification using PMU-data in a distribution network.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125534091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5