Prediction of Steady-State Indices of Voltage Quality Based on Correlation Analysis and Long Short-Term Memory Network

doi:10.12204/j.issn.1000-7229.2021.04.002

Abstract

Abstract:

With the increasing scale of power network, many steady-state power quality (PQ) problems are caused by multiple loads connected to the distribution network. The prediction of voltage quality according to the monitoring data in distribution stations is helpful to grasp the change trend of power quality level, which is of great significance for early warning and governance of power quality. In order to effectively analyze the change law of steady-state data and improve the level of power quality, a new method for voltage quality steady-state indices prediction is proposed. The method is based on long short-term memory (LSTM) network. It mines and uses the correlation of different time series data and optimizes the prediction effect of steady-state indices. Firstly, the correlation between active power and voltage quality steady-state indices is analyzed, and then their time series characteristics are matched by temporal correlation. Secondly, LSTM network is used to model correlation relationship between selected active power and actual steady-state monitoring data for voltage quality. Finally, the forecasting model is used to predict the voltage quality steady-state data of an area of Fujian power grid. According to experimental results, the DTW-LSTM model constructed by combining with the specific user's electricity consumption behavior can improve the short-term prediction accuracy of voltage quality steady-state indices under both relatively constant and changing daily electricity consumption behavior of users. Moreover, the long-term predictions are more effective in the second case.

Key words: voltage quality, steady-state indices, correlation analysis, time series, long short-term memory network

CLC Number:

TM73

YANG Chaoyun, XIA Shengfeng, JIANG Nan, HUANG Yibiao, LI Ke, ZHANG Yi. Prediction of Steady-State Indices of Voltage Quality Based on Correlation Analysis and Long Short-Term Memory Network[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 9-16.

Figures/Tables 11

Fig.1 Curves of active power of different users on the same day

Fig.2 Curves of voltage quality steady-state indices

Fig.3 Schematic of LSTM cell structure

Fig.4 Time series of total harmonic distortion of the voltage

Fig.5 Time-series of active power of the users

Table 1 Correlation measure between THDu and active power

Table 2 Forecast error statistics of THDu in the first case

Table 3 Forecast error statistics of voltage deviation in the first case

Fig.6 Time series of active power of a power user

Table 4 Forecast error statistics of THDu in the second case

Table 5 Forecast error statistics of voltage deviation in the second case

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