Stochastic Assessment of Harmonic Propagation and Amplification in Power Systems Under Uncertainty

Li, Zhaoyang; He, Zhengyou; Song, Yitong; Tang, Li; Wang, Yang

doi:10.1109/tpwrd.2020.3002890

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…In other words, high penetration of renewable generations, load level variations and some power system changes, such as variation in capacitor bank status, replacing overhead lines with underground cables, and electrical network configurations, bring about variation in harmonic propagation and amplification in electrical networks [27,28].…”

Section: Proposed Methodology For Monitoring Harmonic Resonance Condi...mentioning

confidence: 99%

Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty

Shakeri,

Koochi,

Esmaeili

2024

IET Generation Trans & Dist

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This paper proposes an optimization approach for allocating power quality monitors (PQMs) aiming to monitor all harmonic resonance conditions while taking power system uncertainties into account. The placement approach utilizes the frequency scan response for calculating impedances over a range of frequencies and consequently determining harmonic resonance conditions. Thereby, it is capable of building binary matrices, which include harmonic resonance conditions. Also, by utilizing the union operator at binary matrices, power system uncertainties such as photovoltaic generation and load level can be considered in the allocation method. The placement approach is expressed as a linear problem that determines the best locations of PQMs and their optimal number so that they monitor all harmonic resonance conditions. Besides, by considering the area of harmonic pollution of non‐linear loads in the proposed method, owners of electrical networks can find a solution with fewer PQMs to monitor harmonic resonance orders inside a particular area of the network. The performance of the presented approach is demonstrated using the 15‐node distribution network and a real electrical network, as well as a real large electrical network in Iran. Results show that the proposed method suggests fewer PQMs to monitor harmonic resonance conditions compared to previous methods.

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Section: Proposed Methodology For Monitoring Harmonic Resonance Condi...mentioning

confidence: 99%

Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty

Shakeri,

Koochi,

Esmaeili

2024

IET Generation Trans & Dist

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show abstract

“…As a reference, historical cases can help new maintenance personnel master the process of equipment operation and maintenance in the power system and improve their competency rapidly, which is vital for the effective diagnosis of the equipment later. According to the abovementioned analysis, combined with the data mining algorithm and bidirectional networks in the long- and short-terms, and random conditions, the research on the power system harmonic issue is completed to address the problems in the power system harmonic process effectively [ 5 , 6 ]. By constructing the power system equipment knowledge graph of the ontology model, power system equipment failure is identified by the BP neural network-based learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm

Yue

Hu²,

Zhang

2022

Computational Intelligence and Neuroscience

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At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, transmission, and utilization rate of electric energy, overheat electrical devices, generate vibration and interference, and then affect the aging and service life of the lines. In order to effectively reduce the harmonic problems caused by different levels of the power system, it is necessary to analyze the harmonic components. In this paper, the BP neural network learning algorithm is introduced into the harmonic problems of the power system. The mapping relationship between input and output signals is obtained by using the BP neural network algorithm, and the harmonic frequency, amplitude, and phase contained in the obtained data are analyzed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of existing defects, the problems are quickly located and the causes are analyzed. The practical results show that the BP neural network learning algorithm proposed in this paper has higher detection accuracy and analysis speed for the difficult problems in the power system.

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“…Electrified railway load has strong randomness and large variation, but it has certain rules according to the train schedule. Given this, some scholars adopt Monte Carlo [22], neural networks [23], and probabilistic harmonic methods [24] to describe and forecast harmonics.…”

Section: Introductionmentioning

confidence: 99%

Harmonic feature of 27.5 kV traction power system in a Chinese high‐speed railway

Fan

Chen

et al. 2022

IET Electrical Syst in Trans

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The key to controlling harmonics in high‐speed railways is to get information on its harmonic feature. In this study, a continuous 24 h harmonic observation is carried out in Ji'an West traction substation of a typical Chinese high‐speed railway. In both the 24 h operating period and the two train driving processes, the features of harmonics of 27.5 kV buses are studied under multiple operating modes, which finds that the voltage and the current harmonic content mainly consist of low‐order harmonics below the 11th order with high‐order harmonics that ranges from 13th to 31st and 89th to 99th order. The law that the harmonic distribution changes with the load current is derived. Calculation results show a power function relationship between total harmonic distortion and bus current. In regenerative braking modes, the harmonic content is higher than traction with less high‐order harmonics. Finally, the authors find an admittance matrix coupling between the 3rd to the 31st order harmonic voltage and current for simplified modelling of the harmonic transmission.

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Stochastic Assessment of Harmonic Propagation and Amplification in Power Systems Under Uncertainty

Cited by 12 publications

References 33 publications

Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty

Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm

Harmonic feature of 27.5 kV traction power system in a Chinese high‐speed railway

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