Study on Harmonic Impedance Estimation and Harmonic Contribution Evaluation Index

Liu, Ziteng; Xu, Yan; Jiang, Haiwei; Tao, Shun

doi:10.1109/access.2020.2982950

Cited by 18 publications

(7 citation statements)

References 22 publications

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“…A harmonic impedance estimating technique is proposed in [88] based on the similarity measure algorithm and ordering points to identify the clustering structure (OPTICS). This method considers the background harmonic voltage fluctuation and utility impedance change.…”

Section: B Artificial Intelligence-based Harmonic Estimation Techniquesmentioning

confidence: 99%

A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning

et al. 2023

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Modern industrial and commercial devices that are fed by power electronics circuits and behave non-linearly tend to produce power quality issues in power systems including harmonics and interharmonics, swell, flicker, spikes, notches, and transient instabilities. Among them, harmonic emission is the most significant challenge to be overcome by the distribution networks. Unwanted current, overheating motors and transformers, equipment failure, and circuit breaker misoperation are some of the harmonic consequences. While it is important to employ the best methods to mitigate or suppress the harmonic distortions in power systems, it is even more essential to estimate these harmonics at the outset by developing smart, efficient, and accurate techniques. Due to their capability for learning, predicting, and identifying, researchers have turned to Artificial Intelligence technologies for harmonic estimation in distribution networks. Although the power system parameters (impedance/admittance model) and many harmonic monitors are prerequisites for traditional harmonic estimation methods, by utilizing Artificial Intelligence, these requirements are minimized. In this paper, a comprehensive review of traditional and modern (smart) harmonic estimation techniques are discussed.

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Section: B Artificial Intelligence-based Harmonic Estimation Techniquesmentioning

confidence: 99%

A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning

et al. 2023

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“…Existing methods for harmonic responsibility division primarily rely on harmonic voltage projection, which is calculating the percentage of projection caused by individual harmonic sources at the Point of Common Coupling (PCC) [ 14 , 15 , 16 ]. However, these methods have two limitations.…”

Section: Problem Description and Model Settingmentioning

confidence: 99%

Causal Responsibility Division of Chronological Continuous Treatment Based on Change-Point Detection

Liu

et al. 2023

Entropy

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This paper introduces a novel approach, called causal relation quantification, based on change-point detection to address the issue of harmonic responsibility division in power systems. The proposed method focuses on determining the causal effect of chronological continuous treatment, enabling the identification of crucial treatment intervals. Within each interval, three propensity-score-based algorithms are executed to assess their respective causal effects. By integrating the results from each interval, the overall causal effect of a chronological continuous treatment variable can be calculated. This calculated overall causal effect represents the causal responsibility of each harmonic customer. The effectiveness of the proposed method is evaluated through a simulation study and demonstrated in an empirical harmonic application. The results of the simulation study indicate that our method provides accurate and robust estimates, while the calculated results in the harmonic application align closely with the real-world scenario as verified by on-site investigations.

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“…The characteristic impedance of each network bus can be calculated for different frequency values by active and passive methods [24]. Non-invasive methods are used to harmonic impedance calculation using the harmonic voltage and current data measured at a PCC without harmful impact on power system [25][26][27]. Aiming at this goal, the equivalent impedance is calculated from the point of view of each target bus using the method presented in [11] and converting Thévenin equivalent to the Norton and finally using Equation (1).…”

Section: Calculation Of Norton's Equivalent Circuit From the Perspect...mentioning

confidence: 99%

Imposing fair penalty to the harmonic sources based on the measurement data

Tadayon

Hooshmand

Kiyoumarsi

et al. 2021

IET Generation Trans & Dist

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Improving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14-bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs.

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Study on Harmonic Impedance Estimation and Harmonic Contribution Evaluation Index

Cited by 18 publications

References 22 publications

A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning

A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning

Causal Responsibility Division of Chronological Continuous Treatment Based on Change-Point Detection

Imposing fair penalty to the harmonic sources based on the measurement data

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