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155 results on '"Cui, Mingjian"'

2. Research on Optimization Strategy of Heterogeneous Loads Participating in Demand Side Response

Author

Tu, Ge, Wang, Yunpeng, Chen, Chunyu, Cui, Mingjian, Wang, Ying, Zhang, Kaifeng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yang, Qingxin, editor, Li, Jian, editor, Xie, Kaigui, editor, and Hu, Jianlin, editor

Published
2023
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3. Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder

Author

Lin, You, Wang, Jianhui, and Cui, Mingjian

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper presents a new solution for reconstructing missing data in power system measurements. An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the neighbor values correlation and Long Short-Term Memory (LSTM) networks. The proposed LSTM-EDAE is able to remove the noise, extract principle features of the dataset, and reconstruct the missing information for new inputs. The paper shows that the utilization of neighbor correlation can perform better in missing data reconstruction. Trained with LSTM networks, the EDAE is more effective in coping with big data in power systems and obtains a better performance than the neural network in conventional Denoising Autoencoder. A random data sequence and the simulated Phasor Measurement Unit (PMU) data of power system are utilized to verify the effectiveness of the proposed LSTM-EDAE., Comment: This paper is accepted by the conference 2019 IEEE PES General Meeting

Published
2019

5. An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending

Author

Feng, Cong, Cui, Mingjian, Hodge, Bri-Mathias, Lu, Siyuan, Hamann, Hendrik F., and Zhang, Jie

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting. This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition (SVM-PR) is adopted to recognize the category of a certain day using the first few hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer Machine learning based Multi-Model (M3) forecasting framework. The developed UC-based methodology is validated by using 1-year of data with six solar features. Numerical results show that (i) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; (ii) M3-based models also outperform the single-algorithm machine learning (SAML) models by approximately 20%.

Published
2018

16. Dual Timescales Voltages Regulation in Distribution Systems Using Data-Driven and Physics-Based Optimization

Author

Zhang, Jian, Cui, Mingjian, and He, Yigang

Abstract

A large number of electric vehicles, distributed solar, and/or wind turbine generators connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators and static VAR compensators is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process is developed, in which the charge/discharge power of energy storage systems, statuses/ratios of switchable capacitors reactors, and voltage regulators are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient deep reinforcement learning algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

Published
2024
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17. Temperature-Dependent Resistance Constrained PV Accommodation Capacity Improvement Based on Multi-Objective DRL

Author

Yin, Ziyang, Wang, Shouxiang, Zhao, Qianyu, and Cui, Mingjian

Abstract

Against the backdrop of the low-carbon energy transition, distribution system operators face the urgent challenge of balancing the contradictory demands of high photovoltaic (PV) accommodation capacity and low operation cost. Meanwhile, most iteration-based PV accommodation capacity improvement methods are limited by imprecise line resistance and the conflicting relationship between decision efficiency and modeling accuracy. To this end, a two-timescale distribution network dispatching approach based on muti-objective DRL is proposed. This approach is an online decision-making method based on real-time data and robust to temperature-dependent resistance via constructing a two-stage decision-making model based on multi-objective Markov decision process considering the weather factors. Also, the proposed model has a vectorized reward function to assess the trade-off between the economy and accommodation capacity for better operation. A novel multi-objective DRL (MODRL) algorithm based on the tchebycheff norm is proposed, which decomposes the proposed decision-making model into multiple sub-models for learning Pareto optimal policies. Comparative tests on the IEEE 33-bus system validate that the proposed method effectively acquires optimization strategies under varying user preferences to improve economic and PV accommodation capacity. The proposed algorithm obtains more diverse Pareto fronts and high-quality solutions than other state-of-the-art MODRLs.

Published
2024
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