Recovery Method for Missing Measurement Data of Power Systems Based on Long Short-Term Memory Networks

doi:10.12204/j.issn.1000-7229.2021.05.001

Abstract

Abstract:

As the scale of power systems continues to increase, measurement data of power system grows rapidly. However, the process of massive data collection, measurement, transmission, and storage in power systems may lose data, which seriously threatens the safety of the power grid. Aiming at the problem of missing data in power systems, this paper proposes a missing data recovery method based on long short-term memory (LSTM) networks. Firstly, since the LSTM network can extract the characteristics of the timing law of measurement data, a double-layer and full connection LSTM network architecture is presented, which uses known data to map missing data. Furthermore, in order to improve the recovery accuracy of missing data in different states, a state identification method based on random forest and a recovery strategy considering the location of the missing data are developed. Finally, case studies by using simulation data and measurement data verify the effectiveness and accuracy of the method proposed in this paper, and the proposed method can significantly improve the data quality of the power systems without system topology modeling.

Key words: power system, recovery of missing measurement data, long short-term memory network, random forest

CLC Number:

TM73

WANG Zixin, HU Junjie, LIU Baozhu. Recovery Method for Missing Measurement Data of Power Systems Based on Long Short-Term Memory Networks[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 1-8.

Figures/Tables 15

Fig.1 Schematic diagram of dynamic data

Fig.2 Unit structure of LSTM network

Fig.3 Double-layer LSTM network model

Fig.4 Data in steady state and dynamic state

Table 1 Recovery strategy for different locations

Fig.5 Process of missing data recovery

Fig.6 Topology of IEEE 39-node system

Fig.7 Simulation data in steady and dynamic state

Fig.8 State identification results of simulation data

Table 2 Effect of different missing positions on data recovery accuracy

Table 3 Effect of different missing ratios on data recovery accuracy

Fig.9 Recovery results of dynamic missing data

Fig.10 Field data of voltage amplitude of a bus

Table 4 Comparison of the identification results of different methods on the field data states

Fig.11 Recovery results of field data

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