Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle

Zhou, Fulin; Liu, Feifan; Yang, Ruixuan; Liu, Huanrui

doi:10.3390/en13040879

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…The decomposition of apparent power under harmonic condition under IEEE Std.1459-2010 is shown in Figure 4. Based on establishment of index set of harmonic economic loss on PCC, economic loss on PCC corresponding to each index can be obtained according to Equations (8) - (13). Therefore, according to Equation (14), total harmonic economic loss on PCC can be determined.…”

Section: A Evaluation Of Total Economic Loss Of Harmonic On Pccmentioning

confidence: 99%

“…Subsequently, this method has been widely used. Based on it, the literature [10][11][12][13] carry out indepth research on calculation of harmonic impedance of utility side, but it in fact uses traditional method based on superposition projection principle to calculate harmonic responsibility quantitatively, which does not effectively innovate quantitative calculation method of harmonic responsibility. The literature [14] carries out research on quantitative calculation method of harmonic responsibility based on the principle of superimposed projection, which realizes harmonic source can be divided into two kinds, one kind is augmentation harmonic source, the other kind is subtraction harmonic source.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Determination of Harmonic Responsibility Based on Quality Engineering Theory

Fang

Liao

et al. 2020

IEEE Access

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At present, the essence of traditional methods for determination of harmonic responsibility among different parts in power system is mainly based on the contribution of relevant physical quantity on the point of common coupling (PCC) of both utility side and customer side. However, theoretical basis of various methods is of much difference, leading to a large difference in the final calculation result of harmonic responsibility. Therefore, there is no reliable approach applied in actual cases. This project aims to fill the current gap in literature, proposing a new method to determine harmonic responsibility from the perspective of economic loss. Firstly, according to power theory of IEEE Std.1459-2010 and quality engineering theory, the index set of harmonic pollution evaluation on PCC is established, and the expressions of calculation of economic loss are obtained. Secondly, collecting relevant data on PCC, the parameters of Norton equivalent models of utility side and customer side are obtained by the reference impedance method, then contributions of corresponding physical quantity on PCC can be analyzed. Based on relevant expressions of economic loss, calculation results of economic loss are all obtained. At the same time, values of harmonic responsibility of utility side and customer side are calculated. Finally, new adjustment scheme of rewarding-penalizing electricity charge (RPEC) is discussed and compared with other traditional schemes. After comparison, Results of comparative analysis show that the new adjustment scheme can not only increase the penalty for harmonics but also reflect commodity characteristic of electricity energy. In this manner, calculation result of electricity charge is more reasonable. Hence, electricity environment on PCC can be improved.

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Section: A Evaluation Of Total Economic Loss Of Harmonic On Pccmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Method for Determination of Harmonic Responsibility Based on Quality Engineering Theory

Fang

Liao

et al. 2020

IEEE Access

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“…The work by F.L. Zhou et al [13], "Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle", proposed a method for estimating harmonic parameters in the case of monitoring data without phase, based on the partial least square regression method. The proposed method utilized the amplitude information of the harmonic voltage and current of the point of common coupling to estimate the harmonic parameters and the harmonic responsibility of each harmonic source.…”

Section: Brief Overview Of the Contributionsmentioning

confidence: 99%

Machine Learning for Energy Systems

2020

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

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“…Then, using large amounts of measured harmonic data, they calculate the harmonic impedance and harmonic emission level by regression estimation. Moreover, [20] proposed a method to estimate harmonic parameters and harmonic responsibility with the harmonic amplitude and phase difference of harmonic voltage and current. These methods have already had quite mature applications in the UPG, so in these research works it is possible to use the linear regression method in RESs.…”

Section: Introductionmentioning

confidence: 99%

A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism

2020

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In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method.

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Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle

Cited by 8 publications

References 11 publications

A New Method for Determination of Harmonic Responsibility Based on Quality Engineering Theory

A New Method for Determination of Harmonic Responsibility Based on Quality Engineering Theory

Machine Learning for Energy Systems

A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism

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