电力系统态势感知：CNN-LSTM驱动的预测模型提升

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本文主要探讨了在电力系统中利用深度学习技术——特别是卷积神经网络（CNN）和长期短期记忆（LSTM）递归神经网络——来提升态势感知（SA）的预测能力。态势感知在电力系统的稳定性和安全性方面扮演着关键角色，尤其是在可再生能源集成后，系统面临着复杂多变的不确定性。以往的研究主要集中在SA的感知和理解阶段，而对预测这一高级且具有挑战性的环节鲜有涉及。文章创新性地提出了一种基于CNN-LSTM网络的SA模型，该模型结合了CNN的局部特征提取能力和LSTM的序列建模优势。CNN在处理空间特征时表现出色，能从相量测量单元（PMU）数据中捕捉到时间序列中的模式，而LSTM则能够捕获数据中的长期依赖关系，从而实现对时空数据的有效挖掘。这种模型设计包括两个核心功能分支：一是应急定位器，用于精确定位系统故障的发生位置；二是稳定性预测器，旨在预判电力系统的未来稳定性状态。作者强调，即使在数据充分性较低的情况下，该CNN-LSTM模型也能展现出卓越的性能，如高精度的故障定位和稳定的稳定性预测。这表明，深度学习方法能够有效地应对电力系统动态环境下的复杂问题，提升态势感知的预测水平。研究成果发表在《工业信息化交易》（IEEE Transactions on Industrial Informatics）上，第17卷第10期，2021年10月，具有重要的理论和实际应用价值。总结来说，本文的主要贡献在于将机器学习，尤其是深度学习技术与电力系统态势感知相结合，提出了一个创新的、在时空数据上高效协作的CNN-LSTM模型，对于电力系统的安全监控和决策支持具有重要意义。

WANG et al.: TOWARD THE PREDICTION LEVEL OF SITUATION AWARENESS 6953

Fig. 1. Flowchart of the proposed SA model and its application.

the pixel-correlated arrays. The pooling layers (usually max

pooling) then merge those similar features in each feature

map [22]. RNN is another skillful model when processing

sequential data such as text and speech. RNN has a specialized

recurrent structure which can maintain memory over every time

step of the input sequence [23]. A variant of RNN, which is

known as LSTM network, has ﬁxed the gradient vanishing or

exploding problems by a meticulously designed cell structure.

As a result, LSTM network has proved to be more powerful in

harnessing long-term memory [24].

The excellent performance of deep learning has facilitated a

number of data mining studies based on the wide-area PMU data.

Some of them utilized the spatial connectivity of the data [15],

[18], while others concentrated on the temporal correlation [14],

[16], [17]. However, few studies have made simultaneous min-

ing on both the spatial and temporal features. This will limit

the data mining ability of AI when dealing with the transient

stability prediction problems in power systems because power

system transient stability is a system-level issue which requires

exploration on the spatial connectivity, and at the same time

also a dynamic process which requires analysis on the temporal

correlation.

III. M

ETHODOLOGY

A. Framework Overview

The framework and workﬂow of the proposed SA model is

outlined in Fig. 1. First, all the measurement data from PMUs

are obtained and preprocessed to uncover the spatio-temporal

patterns inside the data. As the main body of the workﬂow,

the CNN-LSTM model is aggregated by two deep learning

modules—the CNN module and the LSTM module. The spatial

features within the data are extracted and learned by the CNN

module, while the temporal features are handled by the LSTM

module. The aggregation of these two modules is realized by a

time-distributed operation.

The CNN-LSTM model is designed to concurrently accom-

plish two tasks, i.e., contingency location and stability predic-

tion. By feeding a very short period of postfault data, the model

can not only locate the exact fault l ocation at present, but also

predict the stability status of the system in the future. In this

way, all three levels of SA (i.e., perception, comprehension, and

prediction) can be achieved. As for the ﬁeld application, the

proposed SA model can alert the system operator in a prompt

and accurate manner, in order to implement very fast postfault

actions (PFA) subsequently.

B. Data Preprocessing

1) Matrix Mapping: As the ﬁrst component of the CNN-

LSTM model, the CNN module requires the shape of input data

to be 2-D array-like. Therefore, it is necessary to map the 1-D

measurement data into the expected 2-D form. Some pioneer

studies have presented feasible solutions on the PMU data

preparation for the CNN model. For example, Gupta et al. [16]

proposed a novel operation to map the 1-D PMU data into a 2-D

heatmap representation, to facilitate the instability prediction

function of the CNN model. Inspired by [16], this article creates

an improved mapping operation named matrix mapping, to map

the 1-D measurement data (i.e., 1-D vector which consists of

all PMU measurements) into a 2-D measurement matrix. The

matrix mapping is operated as

X =[x

i,j

]

m×m



i,j

= 0 i = j

i,j

= a

− a

i = j

(1)

where X is the m × m measurement matrix, x

i,j

denotes every

entry of X for i = 1, 2,...,m, and j = 1, 2,...,m, m is the

total number of measurements. Besides, a

and a

are the

respective ith and jth measurement from the original vector

,...,a

The matrix mapping uses a simple but effective way to obtain

the spatial relationships of every PMU measurement. These

spatial relationships are stored pairwise as every entry of the 2-D

measurement matrix. By representing the spatial r elationships,

the 2-D measurement matrix is ﬁlled with implicit spatial fea-

tures which are well prepared to be captured by the subsequent

CNN module. In addition, since the difference between two mea-

surements tends to be small, such matrix mapping is conducive

to form a uniform scale of the 2-D measurement matrix, which

adapts better to the shared weights property of CNN.

2) Label Encoding: Most of the deep learning models (in-

cluding the proposed CNN-LSTM model) require all input

variables and output variables to be numerical. However, the

label data are in fact categorical rather than numerical which

cannot be directly utilized as the output of the CNN-LSTM

model. Therefore, it is necessary to encode the label data into a

numerical form to fulﬁll the output requirements. To be speciﬁc

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