Multipoint harmonic responsibility division method

Abstract

The invention discloses a multipoint harmonic responsibility division method, which comprises the following steps of acquiring synchronous harmonic phasor measurement data of a concerned bus and each monitoring bus in a power system monitoring network, and establishing a multipoint harmonic responsibility division model according to the synchronous harmonic phasor measurement data of the concerned bus and each monitoring bus, and conducting harmonic wave responsibility division based on elastic network regression, reducing singularity of a measurement matrix by introducing a small amount of deviation, solving an ill-conditioned problem caused by multiple collinearity, considering influence of network normal degree fluctuation on harmonic wave measurement data, and estimating harmonic wave responsibility more accurately. The influence of different types of harmonic measurement scenes on the correlation degree of harmonic measurement data is considered, adaptability and robustness are achieved, and compared with traditional multipoint harmonic responsibility division, better accuracy and robustness are achieved under different harmonic responsibility division scenes.

Description

technical field

[0001] The invention belongs to the technical field of power systems, and in particular relates to a multi-point harmonic responsibility division method. Background technique

[0002] With the widespread use of distributed energy grid-connected and power electronic equipment, the harmonic pollution of the power system is becoming more and more serious. When a serious harmonic problem occurs in the system, the first thing is to locate the main harmonic source and quantify the responsibility. Generally, the traditional methods for analyzing harmonic responsibility can be divided into two categories, single-point method and multi-point method.

[0003] Traditional single-point methods include data correlation method, covariance method, and blind source separation methods. These methods use the normal fluctuations of harmonic current and voltage to estimate the harmonic impedance of the system side, and then determine the harmonic responsibilities of the system a...

Examples

Embodiment 1

[0081] Verified by IEEE30 node system, such as image 3 shown. In the study, buses 2, 5, 7, 8 and 21 are considered as non-linear loads, which are modeled with a current source model. The harmonic spectrum is shown in Table 1. This simulation case focuses on the analysis of the 5th harmonic problem. The harmonic current amplitude and phase of the harmonic source are adjusted according to the fundamental current vector.

[0082] Table 1

[0083]

[0084]

[0085] In the simulation case, the responsibility of the harmonic measurement current to buses 3, 7, and 21 is analyzed. In order to consider the fluctuation of the actual power system, it is assumed that the network load fluctuates randomly within the range of ±10% of the fundamental power. A total of 1000 simulated data were generated for each case, and a measurement error of ±0.5% was randomly added to each set of simulated data.

[0086] Since in the actual monitoring system, the load type contained in the moni...

) example 1

[0087] 1) Case 1: The monitoring buses are 2, 5, 7, 8, 15, 19 and 21, including all harmonic sources and some linear load buses of the system;

[0088] 2) Case 2: Harmonic sources 12 and 30 are added, and the monitoring bus is the same as Case 1;

) example 3

[0089] 3) Case 3: The monitored bus contains all the harmonic sources in Case 2, and harmonic sources 2 and 7 are significantly correlated.

[0090] In these simulation cases, the proposed method is compared with the multiple linear regression method, where the actual responsibility of each harmonic source is calculated using the actual harmonic impedance of the harmonic source relative to the bus C of interest.

[0091] 1) Case 1: The harmonic currents of all monitored buses and the harmonic voltages of bus C are used to estimate the harmonic liability. In this case there are no background harmonics, a preliminary analysis of the measured data is performed first:

[0092] a. Before adding the measurement error to the simulated data, X has 7 columns, but its rank is 5. That is, the measured harmonic currents are completely multicollinear. After adding the measurement error, X becomes full rank.

[0093] b. The Pearson correlation coefficient of the measured data is shown in...

technical field

[0001] The invention belongs to the technical field of power systems, and in particular relates to a multi-point harmonic responsibility division method. Background technique

[0002] With the widespread use of distributed energy grid-connected and power electronic equipment, the harmonic pollution of the power system is becoming more and more serious. When a serious harmonic problem occurs in the system, the first thing is to locate the main harmonic source and quantify the responsibility. Generally, the traditional methods for analyzing harmonic responsibility can be divided into two categories, single-point method and multi-point method.

[0003] Traditional single-point methods include data correlation method, covariance method, and blind source separation methods. These methods use the normal fluctuations of harmonic current and voltage to estimate the harmonic impedance of the system side, and then determine the harmonic responsibilities of the system a...

Examples

Embodiment 1

[0081] Verified by IEEE30 node system, such as image 3 shown. In the study, buses 2, 5, 7, 8 and 21 are considered as non-linear loads, which are modeled with a current source model. The harmonic spectrum is shown in Table 1. This simulation case focuses on the analysis of the 5th harmonic problem. The harmonic current amplitude and phase of the harmonic source are adjusted according to the fundamental current vector.

[0082] Table 1

[0083]

[0084]

[0085] In the simulation case, the responsibility of the harmonic measurement current to buses 3, 7, and 21 is analyzed. In order to consider the fluctuation of the actual power system, it is assumed that the network load fluctuates randomly within the range of ±10% of the fundamental power. A total of 1000 simulated data were generated for each case, and a measurement error of ±0.5% was randomly added to each set of simulated data.

[0086] Since in the actual monitoring system, the load type contained in the moni...

) example 1

[0087] 1) Case 1: The monitoring buses are 2, 5, 7, 8, 15, 19 and 21, including all harmonic sources and some linear load buses of the system;

[0088] 2) Case 2: Harmonic sources 12 and 30 are added, and the monitoring bus is the same as Case 1;

) example 3

[0089] 3) Case 3: The monitored bus contains all the harmonic sources in Case 2, and harmonic sources 2 and 7 are significantly correlated.

[0090] In these simulation cases, the proposed method is compared with the multiple linear regression method, where the actual responsibility of each harmonic source is calculated using the actual harmonic impedance of the harmonic source relative to the bus C of interest.

[0091] 1) Case 1: The harmonic currents of all monitored buses and the harmonic voltages of bus C are used to estimate the harmonic liability. In this case there are no background harmonics, a preliminary analysis of the measured data is performed first:

[0092] a. Before adding the measurement error to the simulated data, X has 7 columns, but its rank is 5. That is, the measured harmonic currents are completely multicollinear. After adding the measurement error, X becomes full rank.

[0093] b. The Pearson correlation coefficient of the measured data is shown in...

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