| Predicting Weather-related Power Outages in Distribution GridImprovements in monitoring and data collection practices provide opportunities for more comprehensive modelling and managing grid operations. At the same time, advanced data analysis methods should be able to address service quality degradation due to outages, weather patterns and asset-related performance.In this paper, we apply Machine Learning and Computational Intelligence methods for the analysis of power distribution system data and constructing a system for predicting power outages. Weather and outage data are utilized by the proposed system for predicting purposes. We evaluate the prediction performance of different types of prediction models. We also propose and validate three different architectures of a system for predicting types of weather-related outages. We focus on outages caused by wind, snow and ice. An analysis of the prediction results is provided.T06-P02 University of Alberta | Publication | 2020-12-16 | |
| GridKG: Knowledge Graph Representation of Distribution Grid DataDistribution grid systems are complex networks containing multiple pieces of equipment. All of them interconnected, and all of them described a variety of pieces of information. A knowledge graph provides an interesting data format that allows us to represent information in a form of graphs, i.e., nodes and edges - relations between them. In this paper, we describe an application of a knowledge graph to represent information about a power grid. We show the main components of such a graph - called GridKG, a simple process of identifying electrical paths, and a few examples of grid analysis related to primary switches.T06-P02 University of Alberta | Publication | 2021-01-18 | |
| Construction and Application of Distribution Grid Knowledge GraphT06-P02 University of Alberta | Publication | 2017-01-01 | |
| Knowledge Graphs Representation of Power System and Data-Driven Analysis of OutagesPhD thesis focused on representing distribution power system as graph and using it to analyze outages and their effect on customers, also an extensive probabilistic-based analysis of weather outagesT06-P02 | Activity | 2017-01-01 | Marek Reformat, Yashar Kor |
| Knowledge Graph Representation of Power System and Data-Driven Analysis of OutagesT06-P02 | Publication | 2021-01-01 | Yashar Kor |
| Integrating Knowledge Graphs into Distribution Grid Decision Support SystemsT06-P02 University of Alberta | Publication | 2023-12-20 | |
| Human-centric Question Answering System over Multiple Different Knowledge GraphsT06-P02 | Publication | 2021-01-01 | Nhuan Duc To |
| A Support Vector Regression based Model Predictive Control for Volt-Var Optimization of Distribution SystemsT06-P02 | Publication | 2019-04-24 | Ebrahim Pourjafari, Marek Reformat |
| A Support Vector Regression based Model Predictive Control for Volt-Var OptimizationEbrahim Pourjafari, Marek Reformat, A Support Vector Regression based Model Predictive Control for Volt-Var Optimization, IEEE ACCESS Vol. 7, 11 July 2019, pages 93352 - 93363T06-P02 | Publication | 2019-07-01 | Ebrahim Pourjafari, Marek Reformat |
| Data-Centric Analysis & Artificial Intelligence Application In Weather Related Outage PredictionT06-P02 | Activity | 2019-11-21 | "François Blouin", "Saeed Nusri", Marek Reformat |
| Visiting studentT06-P02 | Activity | 2020-08-01 | Marek Reformat, "Niu Z." |
| Wind power forecasting using attention-based gated recurrent unit networkWind power forecasting (WPF) plays an increasingly essential role in power system operations. So far, most forecasting models have focused on a single-step-ahead WPF, and the obtained results are insufficient for planning and operations of the power system due to the intermittent and fluctuated nature of wind. At the same time, most of the current multi-step-ahead WPF models neglect the correlation between different forecasting tasks. In this paper, we propose a novel sequence-to-sequence model using the Attention-based Gated Recurrent Unit (AGRU) that improves accuracy of forecasting processes. It embeds the task of correlating different forecasting steps by hidden activations of GRU blocks. In addition, an attention mechanism is designed as a feature selection method to identify the most important input variables. To validate the effectiveness of the proposed AGRU model, three different case studies focused on forecasting accuracy, computational efficiency, and feature selection abilities are carried out. Their performances are compared with various wind power forecasting benchmarks.T06-P02 | Publication | 2020-04-01 | "Zhewen Niu", "Zeyuan Yu", "Wenhu Tang", "Qinghua Wu", Marek Reformat |
| Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial NetworkThe fourth industrial revolution - Industry 4.0 - puts emphasis on the application of intelligent technologies in the area of monitoring and identification of electrical equipment. High precision and non-contact qualities make the infrared thermography one of the most suitable technologies for intelligent inspection of high-voltage apparatus. Yet, due to imperfect data acquisition methods and difficulties in collecting data, electrical equipment images are limited in quantities and imbalanced in representing different types of devices. Additionally, it is not easy to extract representative features of infrared images due to their low-intensity contrast and uneven distribution. In this paper, a data-driven framework is proposed for the identification of electrical equipment based on infrared images. The main technique of this proposed system is a novel process of generating synthetic infrared images. For this purpose, an Edge-Oriented Generative Adversarial Network (EOGAN) is developed. The EOGAN is designed to create realistic infrared images that can be used as augmented data for developing data-driven identification methods. Extracted edge features of electrical equipment are utilized as prior information to guide the process of generating realistic infrared images. Finally, comparative experiments are carried out to show the effectiveness of the proposed EOGAN-based framework for equipment identification in the presence of limited and imbalanced image datasets.T06-P02 | Publication | 2020-07-24 | "Zhewen Niu", Marek Reformat, "Wenhu Tang", "Baining Zhao" |
| Software system for analysis of outages based on different weather conditions and geographical locations; graphical representation of probabilities of outages using heat maps T06-P02 | Publication | 2017-01-01 | Marek Reformat |
| Home Energy Management with V2X Capability using Reinforcement Learning. T06-P02 University of Alberta | Publication | 2023-06-05 | |
| Parametric and Nonparametric Modelling for Increasing Power System Reliability: A ReviewT06-P02 University of Alberta | Publication | 2017-01-01 | |
| RL-based Home Energy Management Agent for V2X ApplicationsT06-P02 University of Alberta | Publication | 2017-01-01 | |
| Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A ReviewT06-P02 University of Alberta | Publication | 2024-01-11 | |
