Calculation of Line Loss in Transformer District Based on K-Means Clustering Algorithm and Improved MRVM

doi:10.11985/2021.01.009

Abstract

Abstract:

The calculation of distribution network line loss is an important technical measure for the management and analysis of distribution network line loss. In order to solve the problems of traditional distribution network theory, such as unable to realize automatically, heavy workload and inaccurate calculation results, a fast calculation method of line loss in low-voltage substation area based on K-Means clustering and Drosophila algorithm optimization multi classification correlation vector machine (MRVM) is proposed, and an algorithm model is built in Matlab. In order to solve the problem of dispersion of line loss, K-Means clustering algorithm is used to classify the samples. Then, the MRVM optimized by the Drosophila algorithm is trained by using the classified samples, and the mapping relationship between the line loss rate and the platform area parameters is obtained. Finally, 500 samples of a certain area in Sichuan Province are used to verify the results, and the results are compared with the traditional theoretical line loss calculation method. The results show that the proposed method has better accuracy. At the same time, it can improve the automation degree of the current theoretical calculation of line loss, reduce the calculation workload, and improve the operation efficiency of the distribution network of the grid company.

Key words: Distribution network, line loss, K-Means clustering algorithm, Drosophila algorithm, MRVM

CLC Number:

TM744

XIE Lin, LI Hongwei, YUAN Yue, ZHOU Hailin. Calculation of Line Loss in Transformer District Based on K-Means Clustering Algorithm and Improved MRVM[J]. Journal of Electrical Engineering, 2021, 16(1): 62-69.

Figures/Tables 15

References 20

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类别	初始聚类中心				P_E
类别	x₁(%)	x₂/m	x₃/m	x₄(%)	P_E
1	82.31	132	789	6.134	2.156
2	80.55	201	1 532	8.170	1.934
3	91.01	189	1 980	7.460	3.106
4	90.32	390	1 756	15.450	3.598
5	97.22	267	1 078	7.310	3.709
6	79.98	600	4 037	10.890	4.623

参数	训练目标误差(%)
参数	0.01	0.005	0.001	0.000 1
迭代次数	70	98	160	480
均方误差	1.50	0.67	0.17	0.09
E_C小于0.05%台数	0	7	9	39
E_C介于0.05%和0.1%之间台数	2	3	8	30
E_C介于0.1%和1%之间台数	38	40	141	317
E_C介于1%和5%台数	109	169	250	173
E_C介于5%和10%台数	108	119	80	19
E_C大于10%台数	343	262	112	22

样本号	线损特征指标				实测线损(%)	相对误差(%)
样本号	X₁(%)	X₂/m	X₃/m	X₄(%)	实测线损(%)	相对误差(%)
一类30号	98.1	246	490	11.33	11.26	25.7
四类5号	100	250	500	13.14	2.03	11.7
五类5号	85.2	200	470	9.1	15.03	21.4
五类15号	90.4	231	494	10.7	10.08	13.2
五类64号	101	236	500	12.1	7.09	17.6

参数	训练目标误差设为0.01
	第1类		第2类		第3类		第4类		第5类		第6类
	标准MRVM	改进MRVM	标准MRVM	改进MRVM	标准MRVM	改进MRVM	标准MRVM	改进MRVM	标准MRVM	改进MRVM	标准MRVM	改进MRVM
迭代次数	60 000	12	68 000	12	2 900	8	2 400	5	11 000	6	790	21
均方误差E_m(%)	41.3	2.1	28.4	4.1	33.8	2	24.4	3.2	16.7	2.4	31.1	1.8