This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions.

This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.

Focusing on fully learning intrinsic spatial and temporal dependencies from smart grids’ complicated transients in a computationally efficient way, this article develops an intelligent machine learning approach for online short-term voltage stability (SVS) assessment. Based on static network information and dynamic system responses, spatial correlations are first comprehensively described from both model-based and data-based viewpoints. Such correlations are further formulated as spatial attention factors, which are leveraged to carefully rectify multiple transient trajectories. Taking the rectified trajectories as inputs, the long short-term memory based deep recurrent neural network (RNN) algorithm is employed to learn sequential SVS features. In this way, the RNN learning procedure is comprehensively guided by both spatial and temporal information, thereby deriving a highly reliable and robust classification model for online SVS assessment. Extensive numerical tests on the Nordic test system and the realistic Guangdong Power Grid in South China illustrate the superior reliability, scalability, and applicability of the proposed approach over existing methods.

Battery Energy Storage System (BESS) can be utilized to shave the peak load in power systems and thus defer the need to upgrade the power grid. Based on a rolling load forecasting method, along with the peak load reduction requirements in reality, at the planning level, we propose a BESS capacity planning model for peak and load shaving problem. At the operational level, we consider the optimal control policy towards charging and discharging power with two different optimization objectives: one is to diminish the difference between the peak load and the valley load, the other is to minimize the daily load variance. Particularly, the constraint of charging and discharging cycles, which is an important issue in practice, is taken into consideration. Finally, based on real load data, we provide simulation results that validate the proposed optimization models and control strategies.

A large high-voltage direct current (HVDC) reactor is required in modular multilevel converter (MMC) projects to realize DC fault current limiting (FCL). This condition may result in adverse effects on MMC control dynamics and oscillation and instability of HVDC systems. In this study, a virtual reactor (VR) control strategy is proposed to reduce a fixed large HVDC reactor. The VR characteristic of MMCs is revealed and analyzed as a controlled reactor, and the inductance of this reactor can be directly controlled by the discharge degree of submodule capacitors. A VRFCL control strategy in which VR can effectively limit DC fault current and substitute the fixed HVDC reactor is proposed accordingly. Simulation and experimental results verify the proposed control strategy. The adoption of the proposed strategy can eliminate the undesirable effect caused by an HVDC reactor, and the SM capacitor voltage can be reserved well. In addition, fault clearance time and fault energy dissipation can be reduced. The proposed VRFCL control is beneficial for MMCs to safely ride through DC fault in a continuous operation manner.

The advancing machine learning techniques have been widely applied to data-driven dynamic stability assessment (DSA) in modern smart grids. However, how to extract critical spatial-temporal features from wide-area system stability dynamics still remains an open issue. Emphasizing on short-term voltage stability (SVS) assessment, this paper develops a novel sequential feature learning approach to address this problem in two steps. First, based on visualized voltage contours, it tactfully constructs a comprehensive spatial-temporal sequence model to dynamically characterize multiplex spatial-temporal SVS evolution trends. Second, the time series shapelet classification method is leveraged to subtly extract critical consecutive SVS features in sequential forms, i.e., the multidimensional shapelets (discriminative sub-shapes). Test results on the real-world Hong Kong Power Grid demonstrate the efficacy, adaptability and scalability of the proposed approach for SVS assessment. In addition to the outstanding performances on online DSA, with its favorable interpretability, it is capable of providing intuitive insights into regional SVS patterns from spatial-temporal perspectives.