An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation

Abstract

The ever-increasing penetration of centralized and distributed renewable energy, power electronics-based transmission equipment and loads, advanced protection and control systems, storage devices and new power market rules all contribute to the growing dynamics and stochastic behaviors being observed in today's grid operation. Understanding operational risks and providing prompt control actions are of great importance to ensure secure and economic operation of a bulk power system. In this paper, a novel integrated online dynamic security assessment system (DSAS) is developed that intakes real-time EMS snapshots combining both bus/branch and node/breaker network models, performs dynamic contingency analysis under various conditions, calculates real-time transfer limits, and provides online control suggestions to mitigate operational risks. Several unique and innovative features are developed to address practical challenges, including: (1) real-time stitching of power flow information from both node/breaker and bus/branch models covering different geographical regions of interest; (2) online corrections and enhancements to power flow and dynamic models; (3) equipment-name-based modelling approaches for complex contingencies and control systems; and (5) distributed computing capabilities for significant computational speed enhancement. The developed DSAS has been deployed in the control center of State Grid Jiangsu Electric Power Company with hundreds of HVAC and 8 (U)HVDC transmission lines, which has been running reliably since Sept. 2018, achieving satisfactory performance in improving situational awareness and economic operation of the power grid. INDEX TERMS Co-simulation, distributed computing, dynamic security assessment, EMS, node/breaker model, transient stability, user defined model.

A new method for operating point free power system transient stability situational awareness based on single machine equivalent approach

“…Security and more specifically transient rotor angle stability assessment is an essential element in situational awareness and decision making process of a system operator for maintaining the secure operation. In this connection, continuous efforts have been made for providing reliable situational awareness to a system operator by assessing the transient stability 2,3 . Transient instability occurs when the system is subjected to large disturbances.…”

“…In this connection, continuous efforts have been made for providing reliable situational awareness to a system operator by assessing the transient stability. 2,3 Transient instability occurs when the system is subjected to large disturbances. In response to these severe disturbances generator rotor angles are subjected to large excursions and these excursions are influenced by nonlinear power angle relationship.…”

Security Assessment and Coordinated Emergency Control Strategy for Power Systems with Multi-Infeed HVDCs

“…Up to now, many security assessment methods have been proposed for AC/DC systems. The time-domain simulation method is widely used for its good model extensibility and can be classified into two categories: One is based on mature transient stability (TS) simulators with the built-in models and solvers, like Power System Simulator/Engineering (PSS/E) [12], Bonneville Power Administration (BPA) [13], and Transient Security Assessment Tool (TSAT) [14], and the other is based on customized models and solving algorithms, like the voltage source equivalent-based method [15], multi-decomposition method [16], and optimal subinterval selection method [17]. However, in these methods, HVDC converters are expressed by steady-state models, and the fault propagation phenomena between AC and DC subsystems, such as commutation failures and blocking events caused by AC system faults, may not be reflected accurately.…”

Incremental broad learning for real‐time updating of data‐driven power system dynamic security assessment models

“…Based on phasor measurement unit measurements, data‐driven approaches have been developed for DSA based on some machine learning algorithms. Some examples are decision trees [8–11], support vector machine [12], artificial neural networks [13], extreme learning machines (ELM) [5], random vector functional link neural network (RVFL) [14, 15], generative adversarial network [16], Lyapunov exponent [17], shapelet classification [18], imbalanced learning technique [19], probabilistic reliability method [20], integrated online learning method [21], random forest [22], semi‐supervised learning techniques [23], transfer learning algorithm [24], deep spatial‐temporal data‐driven approach [25], and online sequential learning method [26], which can carry out DSA control actions on time. As an emerging learning technology, ensemble learning also shows higher DSA performance [6].…”