Harmonic source identification and current separation in distribution systems

Zhao, Yong; Li, Jianhua; Dao-zhi, Xia

doi:10.1016/s0142-0615(03)00065-6

Cited by 23 publications

(6 citation statements)

References 13 publications

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“…According to (14)(15)(16)(17), the regression coefficient a = (a 0 , a 1 , a 2 , ..., a n ) T can be calculated by a = PQ (19) For the hth harmonic, the harmonic contribution of data segment l in the harmonic load i can be calculated using the BPLS method. Thereby, the total harmonic contribution at the concerned period HC pcc,i can be obtained according to (4)…”

Section: Harmonic Contribution Assessment Under the Fluctuation Of Thmentioning

confidence: 99%

“…Therefore, it is essential to precisely separate the harmonic contribution of various harmonic sources. In order to assess the harmonic contribution, the least squares (LS) methods [11,[15][16][17] are adopted, in which partial LS (PLS) are also employed to the variable dependence problem of multiple harmonic currents [17]. All of the above-mentioned achievements are based on the assumption that the utility harmonic data and harmonic impedance are constant.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive method for harmonic contribution assessment based on hierarchicalK‐means clustering and Bayesian partial least squares regression

Zang

et al. 2016

IET Generation, Transmission & Distribution

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Section: Harmonic Contribution Assessment Under the Fluctuation Of Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive method for harmonic contribution assessment based on hierarchicalK‐means clustering and Bayesian partial least squares regression

Zang

et al. 2016

IET Generation, Transmission & Distribution

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“…The address of the RTU matches with the address sent by the PC is then start sending the data. Here after selecting TR1 (1) Calculate Reactive Power (Q) using Eqn (2) Shift Current samples for 90 using SST as described in [6] End and reactive power are displayed along with the DC component of power. On choosing option 'Next' the first page as shown in fig.5 is opened again to choose other transformer.…”

Section: Display In the Rmumentioning

confidence: 99%

Remote monitoring for power quality analysis of distribution system

Saha¹,

Bera

Sarkar

2014

Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC)

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This paper depicting a technique through which power quality of a specific area is monitored using one centralized remote monitoring unit (RMU). Mainly the power quality of the output powers of the distribution transformers (DT) are to be monitored and analyzed in this proposed scheme. Each remote terminal unit (RTU) is connected with the DT which measures samples values of voltage and current signals. Then it estimates power quality in the form of harmonic components of active and reactive powers and sends these values to the RMU.In the central station a state-of-the-art graphical user interface (GUI) acquires these data and the quality of the power is displayed for monitoring purposes. A trend analysis can thus be performed to detect the areas injecting more harmonics into the system.

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“…Among state estimation methods, the least square estimation is a common method, which is used to find the harmonic injection at every bus with limited and redundant measurements. Accordingly, based on the injected harmonic energy at the buses, the suspected bus with a harmonic source is identified (Carta et al, 2019; D’Antona et al, 2009, 2011; Kumar et al, 2006; Ruíz Vallejo et al, 2007; Safargholi et al, 2018; Zhao et al, 2004). Dynamic harmonic state estimation has been proposed using Kalman filter approach in Beides and Heydt (1991).…”

Section: Introductionmentioning

confidence: 99%

An improved active power direction method for harmonic source identification

Joshi

Jain

2020

Transactions of the Institute of Measurement and Control

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Due to significant increment in harmonic polluting loads in the power system, there has been enhanced attention of the power professionals towards the estimation of harmonic signals and identification of their sources in the system. Harmonic source identification is an important step for proper accountability, monitoring, and mitigation of any harmonic pollution. The active power direction (APD) method is one of the conventional approaches for harmonic source detection in the distribution system. Although it is simple and easy to implement, serious concerns were raised on its validity, as the direction of active power is dependent on the phase angle. In this paper, APD is augmented with distorting and non-distorting power to improve its accuracy and reliability for harmonic source identification. The distorting and non-distorting portions of the loads are separated, and the distorting and non-distorting powers are calculated at each node. These calculated powers, in addition to the direction of the harmonic active power, are used to formulate the logic required for deciding the severity index at each node. The validity of the method has been tested on a single-phase network, an IEEE-5 bus system, and an IEEE-14 bus system. It has been observed that the proposed method provides good results than conventional APD with the same measurement requirement.

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Harmonic source identification and current separation in distribution systems

Cited by 23 publications

References 13 publications

Adaptive method for harmonic contribution assessment based on hierarchicalK‐means clustering and Bayesian partial least squares regression

Adaptive method for harmonic contribution assessment based on hierarchicalK‐means clustering and Bayesian partial least squares regression

Remote monitoring for power quality analysis of distribution system

An improved active power direction method for harmonic source identification

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