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Abstract

The output of renewable energy is strongly uncertain and random, and the distribution of voltage and reactive power in regional power grids is changed with the access to large-scale renewable energy. In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a novel combinational evaluation method of voltage and reactive power in regional power grids containing renewable energy is proposed. Firstly, the actual operation data of renewable energy and load demand are clustered based on the K-means algorithm, and several typical scenarios are divided. Then, the entropy weight method (EWM) and the analytic hierarchy process (AHP) are combined to evaluate the voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve in typical scenarios. Besides, the evaluation results are used as the training samples for back-propagation (BP) neural networks. The proposed combinational evaluation method can calculate the weight coefficient of the indexes adaptively with the change of samples, which simplifies the calculation process of the indexes’ weight. At last, the case simulation of an actual regional power grid is provided, and the historical data of one year is taken as the sample for training, evaluating and analyzing. And finally, the effectiveness of the proposed method is verified based on the comparison with the existing method. The evaluated results could provide reference and guidance to the operation analysis and planning of renewable energy.
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Authors and Affiliations

Yuqi Ji
1
ORCID: ORCID
Xuehan Chen
1
Han Xiao
2
Shaoyu Shi
2
Jing Kang
2
Jialin Wang
2
Shaofeng Zhang
2

  1. Zhengzhou University of Light Industry College of Electrical and Information Engineering, China
  2. Sanmenxia Power Supply Company of State Grid Henan Electric Power Company, China
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Abstract

The neutral point clamped (NPC) three-level grid-tied converter is the key equipment connecting renewable energy and power grids. The current sensor fault caused by harsh environment may lead to the split of renewable energy. The existing sensor fault-tolerant methods will reduce the modulation ratio index of the converter system. To ensure continuous operation of the converter system and improve the modulation index, a model predictive control method based on reconstructed current is proposed in this paper. According to the relationship between fault phase current and a voltage vector, the original voltage vector is combined and classified. To maintain the stable operation of the converter and improve the utilization rate of DC voltage, two kinds of fault phase current are reconstructed with DC current, normal phase current and predicted current, respectively. Based on reconstructed three-phase current, a current predictive control model is designed, and a model predictive control method is proposed. The proposed method selects the optimal voltage vector with the cost function and reduces time delay with the current reconstruction sector. The simulation and experimental results showthat the proposed strategy can keep the NPC converter running stably with one AC sensor, and the modulation index is increased from 57.7% to 100%.
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Authors and Affiliations

Yanyan Li
1
ORCID: ORCID
Han Xiao
1
Nan Jin
1
ORCID: ORCID
Guanglu Yang
1 2

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, China
  2. Nanyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., China

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