Error Status Evaluation Method for Protection Measurement Circuit Based on Improved ED and CM-BPNN Algorithms

doi:10.11930/j.issn.1004-9649.202308052

Abstract

Abstract:

The accurate evaluation of the error status of the protection measurement circuit can help to understand the operation status of the protection devices, and to timely and effectively find the hidden dangers of the protection devices. This paper proposes an error status evaluation method for substation protection measurement circuit based on improved ED and CM-BPNN algorithms. Firstly, the normalization principle of different index data is formulated for the measurement data. Secondly, the improved Euclidean distance is introduced as the starting criterion for error evaluation. According to the degree of influence on the performance of the protection action, the error status is graded, and the cloud model is introduced to calculate the error status membership degree of the secondary index. Finally, the positioning mapping results corresponding to each error status are determined, and the BP neural network model is constructed, which is then used to evaluate and locate the unknown error status of the current/voltage of the protection devices. Based on PSCAD software, a 220kV substation model was constructed to verify the effectiveness of the proposed method.

Key words: protection measurement circuit, improved Euclidean distance, cloud model, neural network

Yawen YI, Jingpu ZHAO, Can LIU, Xinxin JIANG, Fangjiu LI, Zhenxing LI. Error Status Evaluation Method for Protection Measurement Circuit Based on Improved ED and CM-BPNN Algorithms[J]. Electric Power, 2023, 56(11): 143-150.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202308052

https://www.electricpower.com.cn/EN/Y2023/V56/I11/143

Figures/Tables 11

Table 1 The scores corresponding to status

评估状态	误差值区间	归一化数值
正常（U₁）	[0, 5%]	[0.8, 1.0]
异常（U₂）	(5%, 15%]	[0.4, 0.8)
严重异常（U₃）	(15%, 25%]	[0.0, 0.4)

Table 2 Determination of the eigenvalues of the cloud model

状态等级区间	特征值
状态等级区间	$ E_{\rm{z}} $	$ E_{\rm{n}} $	$ H_{\rm{e}} $
U₁[0, 0.4]	c	$ \dfrac{{E_{\rm z2} - E_{\rm z1}}}{3} $	0.01
U₂(0.4, 0.8]	(a+b)/2	$ \dfrac{{E_{\rm z2} - E_{\rm z1}}}{3} $	0.01
U₃(0.8, 1.0]	a/2	$ \dfrac{{E_{\rm z3} - E_{\rm z2}}}{3} $	0.01

Fig.1 Cloud models in three states

Table 3 The relationship between error status and positioning value

误差状态		定位数值Y_k
$ {l_{k3}} {\text{＞}} {l_{k2}} \; \&\; {l_{k3}} {\text{＞}} {l_{k1}} $		3
$ {l_{k2}} {\text{＞}} {l_{k3}} \; \&\; {l_{k2}} {\text{＞}} {l_{k1}} $		2
$ {l_{k1}} {\text{＞}} {l_{k3}} \; \& \; {l_{k1}} {\text{＞}} {l_{k2}} $		1

Fig.2 Error status assessment process of protection measurement circuit

Fig.3 Three-phase voltage amplitude waveform of secondary index data

Fig.4 Comparison results of voltage positive sequence component weight values

Fig.5 Sequence component improved Euclidean distance initiation criterion

Fig.6 Mean squared error for each round of the BP neural network model

Fig.7 The regression coefficients for BP neural network models

Fig.8 Comparison of the actual status of 30 sets of A-phase voltage data and the model evaluation status results

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