Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System

Abstract

In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon-the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms.

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

“…This typically means replacing the 'objective-driven' approach with a 'rule-driven' approach, which requires a good engineering effort in devising appropriate rules that help the system to perform as desired. See for example the hierarchical intelligent system in [26] used to classify abnormal behaviors in the power system possibly caused by abnormal EVs and other loads, or the index-based approach in [27] used to determine the charging priority of EVs based on surplus power. In [28] EVs are divided into responsive to the pricing signals, and unresponsive EVs that define their charging schedule regardless the cost: finally, most valley filling approaches involve a good deal of appropriately designed rules [29].…”

Impact of overhead line parameters on the short-term voltage stability and its mitigation devices

“…On the other hand, throughout the last few years, more and more converter-based renewable energy resources (such as wind and solar power) and HVDC links are being connected to the grid, giving rise to faster dynamics that, together with the high rate of motor loads (including air conditioning) in certain nodes, is raising the concern of scientific community [6][7][8][9]. This fact can be aggravated if in the future new massive renewable generation is located far from load centres and new overlay transmission networks or supergrids are built to distribute this energy throughout a continental area.…”

A comprehensive assessment to propose an improved line stability index

“…It should be noted that in deriving these indicators, active power ( P ) and reactive power ( Q ) are assumed to flow from the sending bus (SB) to the receiving bus (RB). Bus voltage stability indices: These indices are used to detect the distance between existing operating point and the maximum loading (ML) of load busses, which results in identification of weakest bus in the system. It is worth mentioning that the implementation of these indices is more complicated and they have more computational burden than line stability indices. Other indices: In addition to line and bus stability indices, there are some other methods like modal analysis, sensitivity analysis, artificial intelligence tools, on‐load tap‐changer (OLTC)–based methods, P‐Q and P‐V curve‐based methods, minimum singular value (MSV) of the power‐flow Jacobian, MSV of the reduced Jacobian, equivalent node voltage collapse index, and energy functions, which can be used to assess the stability of the whole power system. The main drawback of these indices is that they have high computational burden. Obviously, among above‐mentioned tools, the one that can provide more accurate assessment and has lower computational burden will be more effective for online applications.…”