Big Data In Power Systems Research Articles

Preface

It is a pleasure to welcome you to the 2024 International Symposium on New Energy Technologies and Power Systems (NETPS 2024). At this dynamic and innovative time, we were honored to have invite academic elites from all over the world to discuss the latest advances and cutting-edge research in new energy technologies and power systems. NETPS 2024 was held in Nanjing, China from March 22nd to 24th 2024 (virtual event). It focused on cutting-edge issues in new energy technologies and power systems, and provided a platform for academics to share knowledge, exchange views and promote collaboration. To make the program more informative and colorful, the Conference invited renowned scholars and experts from around the world, who brought us a series of fascinating thematic presentations. These reports covered multiple aspects of new energy technologies and power systems, including but not limited to hot topics such as New Energy Power Generation, Power System Communication and Control, Intelligent Energy Information Support Technology, Computational Intelligence in Electrical Engineering, Smart Grid, Big Data in Power Systems, etc. Experts and scholars including Prof. Ting Yang (Tianjin University, China), Prof. Pierluigi Siano (University of Salerno, Italy), Prof. Mohan Lal Kolhe, University of Agder (Norway), Norway, etc. performed dramatic presentations on their own researches. We believe that these reports have provided participants with both insight and inspiration, and promoted research and development in related fields. In addition, it featured oral presentations and poster presentations, providing an opportunity for graduate students and young researchers to present their research findings. It was an important platform for participants to exchange and interact with each other, helping to share experiences, promote cooperation and build new partnerships. We believe that through the interaction and cooperation in this Conference, we have deepened our understanding of relevant fields, promoted interdisciplinary integration, and provided new solutions to the complex problems facing the world today. Finally, we believe that the Proceedings of this Conference will be of great significance to the academic community. This Proceedings features outstanding papers from NETPS 2024, which represents the latest research results and innovative ideas in the fields. By publishing and sharing these papers, we are able to further expand the impact of our research, promote communication and collaboration among the academic community, and provide valuable references for future research. We would like to acknowledge all those who supported and participated in NETPS 2024. We look forward to have your active participation in the future series conference of NETPS! The Committee of NETPS 2024 List of Committee Member is available in this Pdf.

A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.

A data-driven method to detect and localize the single-phase grounding fault in distribution network based on synchronized phasor measurement

The single-phase grounding fault of power systems is influenced by a variety of factors, resulting from the developing sizes and increasing complexity of power systems. In order to take advantage of big data in power systems, we propose a revised method with the use of synchronized phasor measurement. The data-driven method is designed to detect and localize the single-phase grounding fault, which reveals the correlation between eigenvalues and status of power systems. First, it calculates the contribution of the fault to each node; it then combines with a split window to monitor power systems in real time and to detect fault more efficiently. Based on the correlation between the elements of the matrix, it is robust against bad data and highly sensitive to weak signals. In general, the proposed method is applicable to various faults and well-functioning with real-time analysis. We test the proposed method with case studies from a distribution network with 80 nodes.