6 research outputs found
Data-Driven Event Identification Using Deep Graph Neural Network and PMU Data
Phasor measurement units (PMUs) are being widely installed on power
transmission systems, which provides a unique opportunity to enhance wide-area
situational awareness. One essential application is to utilize PMU data for
real-time event identification. However, taking full advantage of all PMU data
in event identification is still an open problem. Hence, we propose a novel
event identification method using multiple PMU measurements and deep graph
neural network techniques. Unlike the previous models that rely on data from
single PMU and ignore the interactive relationships between different PMUs or
use multiple PMUs but determine the functional connectivity manually, our
method performs interactive relationship inference in a data-driven manner. To
ensure the optimality of the interactive inference procedure, the proposed
method learns the interactive graph jointly with the event identification
model. Moreover, instead of generating a single statistical graph to represent
pair-wise relationships among PMUs during different events, our approach
produces different event identification-specific graphs for different power
system events, which handles the uncertainty of event location. To test the
proposed data-driven approach, a large real-world dataset from tens of PMU
sources and the corresponding event logs have been utilized in this work. The
numerical results validate that our method has higher identification accuracy
compared to the existing methods
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
A large-scale deployment of phasor measurement units (PMUs) that reveal the
inherent physical laws of power systems from a data perspective enables an
enhanced awareness of power system operation. However, the high-granularity and
non-stationary nature of PMU time series and imperfect data quality could bring
great technical challenges to real-time system event identification. To address
these issues, this paper proposes a two-stage learning-based framework. At the
first stage, a Markov transition field (MTF) algorithm is exploited to extract
the latent data features by encoding temporal dependency and transition
statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided
convolutional neural network (CNN) is established to efficiently and accurately
identify operation events. The proposed method fully builds on and is also
tested on a large real dataset from several tens of PMU sources (and the
corresponding event logs), located across the U.S., with a time span of two
consecutive years. The numerical results validate that our method has high
identification accuracy while showing good robustness against poor data
quality
Improved SVD-based data compression method for synchronous phasor measurement in distribution networks
The integration of phasor measurement units (PMUs) greatly improves the operation monitoring level of distribution networks. However, high sampling rates in PMUs generate huge volumes of measurement data, which creates heavy transmission and storage burdens in information and communication systems. In this paper, an improved singular value decomposition (SVD)-based data compression method for PMU measurements in distribution networks is proposed. First, a lossless phase angle conversion method is proposed, which converts the discontinuous phase angle data of PMU into continuous data sequence to enhance the compression performance. Then, a PMU data compression method is proposed based on SVD, and the compression capability is further enhanced by a lossless compression that utilizes the orthogonal property of the two sub-matrices generated by SVD. Moreover, an error control strategy is designed to dynamically optimizes the scale of transmitted data according to the accuracy requirement of different applications in distribution networks. Finally, case studies are performed using real PMU measurement data from a pilot project in China to validate the compression performance and advantages of the proposed method
Deep Learning in Demand Side Management: A Comprehensive Framework for Smart Homes
The advent of deep learning has elevated machine intelligence to an unprecedented high level. Fundamental concepts, algorithms, and implementations of differentiable programming, including gradient-based measures such as gradient descent and backpropagation, have powered many deep learning algorithms to accomplish millions of tasks in computer vision, signal processing, natural language comprehension, and recommender systems. Demand-side management (DSM) serves as a crucial tactic on the customer side of meters which regulates electricity consumption without hampering the occupant comfort of homeowners. As more residents participate in the energy management program, DSM will further contribute to grid stability protection, economical operation, and carbon emission reduction. However, DSM cannot be implemented effectively without the penetration of smart home technologies that integrate intelligent algorithms into hardware. Resident behaviors being analyzed and comprehended by deep learning algorithms based on sensor-collected human activities data is one typical example of such technology integration. This thesis applies deep learning to DSM and provides a comprehensive framework for smart home management. Firstly, a detailed literature review is conducted on DSM, smart homes, and deep learning. Secondly, the four papers published during the candidate’s Ph.D. career are utilized in lieu of thesis chapters: “A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation,” “A Novel High-Performance Deep Learning Framework for Load Recognition: Deep-Shallow Model Based on Fast Backpropagation,” “An Object Surveillance Algorithm Based on Batch-Normalized CNN and Data Augmentation in Smart Home,” “Integrated optimization algorithm: A metaheuristic approach for complicated optimization.” Thirdly, a discussion section is offered to synthesize ideas and key results of the four papers published. Conclusion and directions for future research are provided in the final section of this thesis