Data-Driven Cascading Failure Prediction Method for High-Proportion Renewable Energy Systems Based on Multi-scale Topological Features

doi:10.12204/j.issn.1000-7229.2023.06.010

Abstract

Abstract:

With the continuous advancement in the renewable power system construction process, the uncertainty of system operating conditions has significantly increased, resulting in a lower antidisturbance ability, higher cascading failure occurrence rate, and shorter evolution time. The traditional power-flow calculation method relies on the enumeration of scenarios to consider random factors, making it difficult to cope with the complex operating conditions experienced with a high percentage of new energy grids in terms of timeliness and accuracy. Therefore, a multi-scale feature-set-based data-driven method for identifying cascading faults under conditions of having a high percentage of new energy grids is proposed by mining historical data patterns and fully considering the stochastic information contained in historical operation data. Indexes that can describe the topological characteristics of a complex network and the operation state of the system are extracted from the macro-, meso-, and micro-levels to form the index set. Based on a bidirectional long-term and short-term memory neural network, the mapping relationship between the index set and the system operation state is studied using the historical cascading failure dataset. This enables a cascading fault prediction model of a high-proportion renewable power system to be constructed. The effectiveness of the proposed model is verified using the topology of the Xinjiang power-grid system.

Key words: cascading failure, data-driven, topological features, high-proportion renewable energy, complex network

CLC Number:

TM732

LI Guoqing, ZHANG Bin, XIAO Guilian, LIU Dagui, FAN Huijing, ZHEN Zhao, REN Hui. Data-Driven Cascading Failure Prediction Method for High-Proportion Renewable Energy Systems Based on Multi-scale Topological Features[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 91-100.

Figures/Tables 12

Fig.1 Overall framework of the proposed method

Fig.2 Structure of LSTM unit

Fig.3 Structure of Bi-LSTM

Fig.4 Node topology of Xinjiang power grid

Table 1 Relationship between cities and nodes in Xinjiang power grid simulation model

Table 2 Forecasting results accuracy of n=2 and n=3 cascading failure

Fig.5 Forecasting results of n=2 and n=3 cascading failure

Table 3 Typical branches failure numbers and forecasting accuracy

Fig.6 Statistics for stability of 100 forecasts

Table 4 Propose model single branch training and forecasting time

Fig.7 Different artificial intelligence model forecasting results

Fig.8 Different indicators input model forecasting results

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