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.

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 a technique for the estimation of oscillation parameters via state-space modeling of synchrophasor data. Due to the spectral leakage of synchrophasors estimated by a Fourier transform-based algorithm, the oscillation parameters such as magnitude and frequency will be inherently distorted. Therefore, a state-space model of the instantaneous waveform signal under power system oscillation is derived as an exponentially damped sinusoidal (EDS) signal in order to account for the spectral leakage. The oscillation frequency and magnitude are estimated in real-time by using an unscented Kalman filter (UKF) based on the state-space model. The estimation accuracy performance is validated using the simulation data of sub-synchronous oscillation (SSO). In addition, the efficacy of the proposed method's performance is verified by the application of the proposed method to real-world oscillation events in a wind farm and comparison with the time-frequency analysis.

This paper presents a distributed synchrophasor data management (DSDM) technique for wide-area monitoring systems. The proposed technique focuses on managing voltage/current phasor data measured from a specific target bus by defining neighboring phasor measurement units (PMUs). We developed an algorithm for searching neighboring PMUs by interpreting the power network topology as a graph structure. By exploiting the data of neighboring PMUs, we constructed a multi-channel structure to recover low-quality entries by using the parallel implementation of long short-term memory networks. A successive output decision-making rule automatically determines the final output, considering the occurrence of defective input, data quality of the measured data, and operational conditions. The proposed DSDM can complement existing centralized management techniques by preserving information on local dynamics. In particular, because of the multi-channel structure, DSDM can provide robust responses to defective data in neighboring PMUs, such as an unintended change in a network topology. We verified the effectiveness of the DSDM with real-world synchrophasor data as well as simulated data from the IEEE 68-bus test system.

The smart grid was initially developed to facilitate communication between operators of the electric power system (such as power generation companies and transmission system operators) and consumers within the distribution network. To implement the smart grid paradigm, time-synchronized measurement devices were developed and introduced into the electric power system. Phasor measurement units (PMUs) and waveform measurement units (WMUs) were created for wide-area transmission networks (at the high-voltage layer), while micro-PMUs were introduced for real-time state estimation in distribution networks (at the low-voltage layer). These time-synchronized measurement devices allow power system operators to monitor the operational status of power generation, transmission, and distribution infrastructure in real time. In particular, data-driven applications utilizing the measurement data can intelligentize and advance the monitoring, operation, and control of the smart grid. The capabilities of digitized high-resolution measurement and time-synchronization are the key factors that enable these contributions to the smart grid. This paper provides an introduction to time-synchronized measurement devices, outlines their specific capabilities, and explores the data-driven applications that can be implemented for advanced smart grid monitoring systems.