Cascading Outage Analyses by Integrating Distribution Factor Method With AC Power Flow

Yun Liu,Yusheng Xue

doi:10.1109/access.2019.2958960

Yun Liu, Yusheng Xue

Open Access

https://doi.org/10.1109/access.2019.2958960

Copy DOI

Abstract

Cascading failure may incur catastrophic consequences. The high order and non-simultaneousness properties are the leading causes for the massive power flow (PF) calculations of the online static security assessment (SSA). Current PF models fail to satisfy the requirements for accuracy, speed, and robustness. This paper proposes a novel method where line outage distribution factor (LODF) model and AC model are coordinated by a binary classifier. The classifier determines the feasibility of LODF model in a specific case by evaluating its potential error. Cases are preferred to be analyzed by LODF model, while the ones with large potential errors will be analyzed by AC model. Case studies of three IEEE systems and the Texas 2000-bus system are given to verify the effectiveness, flexibility, and robustness of the proposed method.

Highlights

Large-scale blackouts have happened worldwide during the past decades, resulting in economic loss, social inconvenience, and cyber physical-attacks [1]–[5]
Addressed to the empirical designation of power flow (PF) models and the neglect of high order or non-simultaneousness property of cascading outages, this paper proposes a novel hybrid method where line outage distribution factor (LODF) model and AC model are adaptively selected by a binary classifier
This paper proposes the classifierbased method coordinating LODF model and AC model

Summary

INTRODUCTION

Large-scale blackouts have happened worldwide during the past decades, resulting in economic loss, social inconvenience, and cyber physical-attacks [1]–[5]. The hot-start DC model and the ACLODF-based model are two typical approaches, where an approximate model is used for the base case or the first round of cascading outages, whereas AC model for the rest [20], [21] This method reaches notable acceleration in PF calculations, yet with poor accuracy when high order contingencies are involved. Addressed to the empirical designation of PF models and the neglect of high order or non-simultaneousness property of cascading outages, this paper proposes a novel hybrid method where LODF model and AC model are adaptively selected by a binary classifier. Features of the classifier are extracted upon the analytical error of LODF model, considering both high order and non-simultaneousness properties of cascading outages. It consists of feature extraction (inputs), classification learning (input-output relations), and pattern generation (outputs)

INTEGRATION OF CAUSAL INFERENCE AND STATISTICAL PARADIGM

FEATURE EXTRACTION

CASE STUDIES

Findings

CONCLUSION AND FUTURE WORK