Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

This paper presents an online event location estimating process in a wide-area power system using the phasor measurement unit (PMU). For real-world applications of PMU-based event location estimation (ELE), it is necessary to consider the insufficiency of PMU data caused by a lack of PMUs, communication failure, PMU malfunction, etc., because insufficient PMU data cannot guarantee event observability over the entire network. To ensure possible event locations in a real-world system, an estimation method is developed by combining offline zonal analysis and an online ELE algorithm. The K-mean clustering method is utilized for electrical-bus clustering, which enables us to recognize separate electrical zones (EZs). Then, we propose a two-stage ELE algorithm by estimating the event EZ and localizing the specified event section within the estimated EZ. Proposed ELE algorithm is applied to a real-world PMU data collected from southwestern part of the United States. Finally, successful results from a real-world PMU data application are presented at the end of this paper.

This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy.

Although the transient stability collapse rarely occurs in daily power system operations, it has a serious ripple effect on the system reliability. Thus, preventing and mitigating the ripple effects from an event or disturbance are important issues for stable operation. In this regard, phasor measurement unit (PMU) facilities enable the acquisition of synchrophasor information and fast transient stability assessments (TSA) before a stability collapse occurs. In addition, the application of a data-driven method to PMU data further enables transient stability prediction (TSP) stage from the near real-time event analysis. However, streaming environments require additional considerations, as always-on prediction must be conducted even before the event occurrences are confirmed, while recognizing events in real-time remains challenges. Consequently, this study proposes a framework that combines a parallel convolutional neural network and long short-term memory (LSTM) structures to automatically perform an event-based TSP. The proposed method uses single event signals to deliver TSP results as streaming data across all the times, eliminating the false alarm rate without requiring the event detection. Finally, the significance of the proposed detection-free TSP method is verified through case studies, addressing anticipated challenges in real-world applications.

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

This paper presents the phasor measurement unit (PMU)-based event localization technique. In order to achieve the event localization in real-time manner, proposed method is based on the measurement of the time required of the event propagation at each PMU. When an event propagates through the transmission line network, there exist a time delay to arrive each electrical bus in wide-area. As an initiated time of the event is unknown information in a practical power systems, time difference of arrival (TDoA) is used to estimate the event location instead of time of arrival (ToA). In this regard, the key-point of proposed localization utilizes the comparison of TDoA values which are composed of entire transmission lines to estimate the propagation path. As a result, the directions and time difference in shortest path are developed as features under the assumption of event location. Furthermore, historical event-based network training method is proposed to substitute the system state estimation and calculation of propagation speed. Finally, the comparisons between calculated and measured TDoA features could estimate the event bus, and real-world test shows successful estimation results.