Research

16 pages, 1830 KiB

Open AccessArticle

Investigation and Control of Damping in VSC–MTDC System with DC Circuit Breakers

by Wenzhong Ma, Jianyi Gao, Guanyu Zhou, Yan Zhang, Kuitong Zhang, Jia Han and Lei Dong

Symmetry 2021, 13(11), 2037; https://doi.org/10.3390/sym13112037 - 28 Oct 2021

Viewed by 1365

DC circuit breaker (DCCB) systems with a DC reactor in series are normally equipped in the voltage-sourced-converter-based multi-terminal DC (VSC–MTDC) systems for DC fault clearance. However, it is revealed that the use of DC reactors could undermine the system damping and deteriorate the [...] Read more.

DC circuit breaker (DCCB) systems with a DC reactor in series are normally equipped in the voltage-sourced-converter-based multi-terminal DC (VSC–MTDC) systems for DC fault clearance. However, it is revealed that the use of DC reactors could undermine the system damping and deteriorate the system stability. In this paper, a controller based on hybrid sensitivity is proposed to improve the stability of power system and realize the power symmetry of multi-terminal systems. Firstly, based on a generalized MTDC small-signal model, an eigenvalue analysis is performed to provide deep insight into the stability issue imposed by DC reactors. Furthermore, a local controller based on hybrid sensitivity was proposed, and on this basis, a global controller was designed to solve asymmetrical power flow. Finally, a four-terminal VSC–MTDC model was built in Simulink to evaluate the performance of DC-PSS. Simulation results verify the effectiveness of the proposed controller in stabilizing MTDC systems and symmetrizing of power flow. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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14 pages, 3336 KiB

Open AccessArticle

Research on the New Topology and Coordinated Control Strategy of Renewable Power Generation Connected MMC-Based DC Power Grid Integration System

by Shanshan Wang, Shanmeng Qin, Panbo Yang, Yuanyuan Sun, Bing Zhao, Rui Yin, Shengya Sun, Chunyi Tian and Yuetong Zhao

Symmetry 2021, 13(10), 1965; https://doi.org/10.3390/sym13101965 - 18 Oct 2021

Cited by 5 | Viewed by 1808

Abstract

The modular multilevel converter (MMC) station connected to the islanded renewable energy generation system needs to adopt the voltage frequency (VF) control to provide AC voltage. The single-pole converter fault will unbalance the input and output power of the DC power grid, which [...] Read more.

The modular multilevel converter (MMC) station connected to the islanded renewable energy generation system needs to adopt the voltage frequency (VF) control to provide AC voltage. The single-pole converter fault will unbalance the input and output power of the DC power grid, which causes the DC voltage or the bridge arm current of the non-fault pole to exceed the protection value in the time scale of tens to hundreds of milliseconds, leading to cascading failures. To realize the fault ride-through (FRT) of single-pole converter fault, this paper analyzes the electrical characteristic of the system. Based on the analysis, the existing topology is optimized and the reasonable operation reserved margin is designed. Furthermore, the corresponding control strategy is proposed, which can not only ensure the single-pole converter block fault ride-through but can also realize economic, stable, and resilient power supply and address asymmetrical problems. Finally, the simulation model is built in PSCAD/EMTDC and the simulation results validate the effectiveness of the proposed control strategy. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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27 pages, 2175 KiB

Open AccessArticle

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

by Xinheng Wang, Xiaojin Gao, Zuoxun Wang, Chunrui Ma and Zengxu Song

Symmetry 2021, 13(9), 1579; https://doi.org/10.3390/sym13091579 - 27 Aug 2021

Cited by 10 | Viewed by 1714

Abstract

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, [...] Read more.

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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19 pages, 2730 KiB

Open AccessArticle

Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

by Xingquan Ji, Xuan Zhang, Yumin Zhang, Ziyang Yin, Ming Yang and Xueshan Han

Symmetry 2021, 13(8), 1479; https://doi.org/10.3390/sym13081479 - 12 Aug 2021

Cited by 4 | Viewed by 1674

Abstract

This paper develops a novel dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints, which can divide the time period according to the time-varying symmetric load demand and symmetric distributed generations (DGs) output condition for a given [...] Read more.

This paper develops a novel dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints, which can divide the time period according to the time-varying symmetric load demand and symmetric distributed generations (DGs) output condition for a given time interval. The significance of the proposed technique is that by approximating the cluster center as the load status and DGs output status of the corresponding period, in this way, the intractable dynamic reconfiguration problem can be recast as multiple single-stage static three-phase symmetric DNR problems, which can effectively reduce the complexity of the three-phase symmetric dynamic reconfiguration. Furthermore, an improved fireworks algorithm considering heuristic rules (H-IFWA) is proposed and investigated to efficiently manage each single-stage static three-phase symmetric DNR problem. In order to avoid trapping into a local optimum or to facilitate the computational performance, the power moment method and the coding method based on heuristic rules are employed to reduce the solution space. The effectiveness of the proposed H-IFWA is validated on the IEEE 33, 119-bus system and a practical-scale Taiwan power company (TPC) 84-bus test system with DGs. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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17 pages, 3443 KiB

Open AccessArticle

A Method for Responsibility Division of Multi-Harmonic Sources Based on Canonical Correlation Analysis

by Yi Zhang, Youran Wang, Junyu Guo and Zhenguo Shao

Symmetry 2021, 13(8), 1451; https://doi.org/10.3390/sym13081451 - 09 Aug 2021

Cited by 3 | Viewed by 1542

Abstract

The variable background harmonic data and incomplete phasor information make multi-harmonic source responsibility division in three-phase symmetrical power system a significant challenge. In this paper, a background harmonic data selection method based on canonical correlation analysis is proposed to deal with multi-harmonic source [...] Read more.

The variable background harmonic data and incomplete phasor information make multi-harmonic source responsibility division in three-phase symmetrical power system a significant challenge. In this paper, a background harmonic data selection method based on canonical correlation analysis is proposed to deal with multi-harmonic source responsibility division without phasor information. Firstly, the canonical correlation coefficient between harmonic voltage and harmonic current is used to characterize the fluctuations of background harmonic voltage. Then, the sliding window method is adopted to select the harmonic voltage and harmonic current with small fluctuations. Next, the canonical correlation results for selected data are used to calculate the harmonic responsibility index via the linear regression method. The harmonic responsibility index in the form of percentage represents the harmonic responsibility division. Finally, several experimental results demonstrate that the proposed method has a high accuracy in calculating the harmonic responsibility division, particularly when the user side contains fluctuations of unknown harmonic sources. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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19 pages, 30882 KiB

Open AccessArticle

Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

by Yuanyuan Sun, Gongde Xu, Na Li, Kejun Li, Yongliang Liang, Hui Zhong, Lina Zhang and Ping Liu

Symmetry 2021, 13(8), 1320; https://doi.org/10.3390/sym13081320 - 22 Jul 2021

Cited by 8 | Viewed by 3169

Abstract

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of [...] Read more.

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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16 pages, 5236 KiB

Open AccessArticle

Fault Calculation Method of Distribution Network Based on Deep Learning

by Cong Zhang, Ke Peng, Huan Li, Bingyin Xu and Yu Chen

Symmetry 2021, 13(6), 1086; https://doi.org/10.3390/sym13061086 - 18 Jun 2021

Cited by 3 | Viewed by 1547

Abstract

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network [...] Read more.

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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14 pages, 5184 KiB

Open AccessArticle

Correlation between Dielectric Loss and Partial Discharge of Oil-Pressboard Insulation

by Yan Li, Min Li and Jun Xie

Symmetry 2021, 13(5), 864; https://doi.org/10.3390/sym13050864 - 12 May 2021

Cited by 2 | Viewed by 1519

Abstract

Dielectric loss tanδ and partial discharge (PD) are important indicators for status assessment of oil-pressboard insulation. The correlation characteristics between these two parameters has significance for understanding the material’s degradation and helps to eliminate the information asymmetry for diagnostics. In this paper, the [...] Read more.

Dielectric loss tanδ and partial discharge (PD) are important indicators for status assessment of oil-pressboard insulation. The correlation characteristics between these two parameters has significance for understanding the material’s degradation and helps to eliminate the information asymmetry for diagnostics. In this paper, the symmetric experimental platform is set up to measure the dielectric loss tanδ and PD for oil-pressboard insulation following the designed testing procedure consisted of raised and rested voltages. Three sets of samples with different water content were tested. The variation mechanism of tanδ with voltage is explained by proposed equivalent circuit, which introduced an asymmetric component representing defect part. PDs are found to be symmetric in the sinusoidal voltage cycles and their statistical parameters are calculated. Besides, the correlation between dielectric loss difference from raised voltage to rested voltage and PD is researched. Strong correlation is observed between dielectric loss and PD, which offers degradation insight for oil-pressboard insulation and helps to eliminate information asymmetry for material status diagnostics. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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10 pages, 1655 KiB

Open AccessArticle

A Topology Analysis-Based MMC-HVDC Grid Transmission Capacity Calculation Method

by Xiao Yu, Bing Zhao, Shanshan Wang, Tiezhu Wang and Lu Zhang

Symmetry 2021, 13(5), 822; https://doi.org/10.3390/sym13050822 - 08 May 2021

Cited by 2 | Viewed by 1394

Abstract

Modular multilevel converter-based high voltage direct current (MMC-HVDC) has a broad application prospect in renewable energy transmission. With the development of converter capacity, the transmission capacity calculation for MMC-HVDC grids becomes important for power dispatching. The existing method depends on manual work and [...] Read more.

Modular multilevel converter-based high voltage direct current (MMC-HVDC) has a broad application prospect in renewable energy transmission. With the development of converter capacity, the transmission capacity calculation for MMC-HVDC grids becomes important for power dispatching. The existing method depends on manual work and is suitable for a simple grid. However, as the grid structure and operation modes become more and more complex, it becomes difficult to calculate the transmission capacity of different operation modes for an MMC-HVDC grid. This paper analyzed and simplified affecting factors and basic topologies. On this basis, a topology-based MMC-HVDC grid transmission capacity calculation method is proposed. First, an MMC-HVDC grid is divided into sending end grid, transmission section lines, and receiving end grid. Then the power limits of these three parts are calculated. Finally, the transmission capacity is determined by analyzing the connection mode of these three parts. This method can be adapted to any kind of MMC-HVDC grid and can be easily programmed. Full article

(This article belongs to the Special Issue Advanced Technologies in Electrical and Electronic Engineering)

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