Publications

2021

1. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints Shuai Fan, Guangyu He, Xinyang Zhou, and Mingjian* Cui IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 251–267, 2021. [Abs] [PDF]

   This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient outperforming existing algorithms.

2. Model-Free Emergency Frequency Control Based on Reinforcement Learning Chunyu Chen, Mingjian* Cui, Fangxing Li, Shengfei Yin, and Xinan Wang IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2336–2346, 2021. [Abs] [PDF]

   Unexpected large power surges will cause instantaneous grid shock, and thus emergency control plans must be implemented to prevent the system from collapsing. In this paper, by the aid of reinforcement learning (RL), novel model-free control (MFC)-based emergency control schemes are presented. Firstly, multi-Q-learning-based emergency plans are designed for limited emergency scenarios by using offline-training-online-approximation methods. To solve the more general multi-scenario emergency control problem, a deep deterministic policy gradient (DDPG) algorithm is adopted to learn near-optimal solutions. With the aid of deep Q network, DDPG-based strategies have better generalization abilities for unknown and untrained emergency scenarios, and thus are suitable for multi-scenario learning. Through simulations using benchmark systems, the proposed schemes are proven to achieve satisfactory performances.

3. Deeply Hidden Moving-Target-Defense for Cybersecure Unbalanced Distribution Systems Considering Voltage Stability Mingjian* Cui, and Jianhui Wang IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 251–267, 2021. [Abs] [PDF]

   A recent proactive defense mechanism, named as moving-target-defense (MTD), has been proposed as a prevailing topic that is capable of actively changing transmission line reactance to preclude cyberattacks. However, the MTD strategy has seldom been studied for the unbalanced AC distribution system in the existing literature. Towards the end, this paper proposes a deeply-hidden MTD (DH-MTD) to elaborately hide both the self and mutual reactance of each phase at the transmission line installed with D-FACTS devices. Both the branch and injection power phasor measurement functions are integrated into DH-MTD in the cyberattack scenario and under the normal operating condition, while the system voltage stability is ensured. The proposed DH-MTD model is solved using a nonlinear least square (NLS) method based on the trust-region algorithm due to the non-Gaussian noise assumption. Also, we cope with the MTD allocation (MTDA) problem using a data-driven normalized PDF peak residual (NPPR) index. The effectiveness of the proposed DH-MTD method is demonstrated in the unbalanced IEEE 123-bus distribution system against both branch and node cyberattacks.

2020

1. Load Altering Attack-Tolerant Defense Strategy for Load Frequency Control System Chunyu Chen, Mingjian* Cui, Xin Fang, Bixing Ren, and Yang Chen Applied Energy, vol. 280, pp. 116015, 2020. [Abs] [PDF]

   Cyber attacks become emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

2. Robustness and Adaptability Analysis for Equivalent Model of Doubly Fed Induction Generator Wind Farm Using Measured Data Jian Zhang, Mingjian* Cui, and Yigang He Applied Energy, vol. 261, pp. 114362, 2020. [Abs] [PDF]

   As many large wind farms connected to the power grid, it is necessary to develop a robust and adaptable dynamic equivalent model of the wind farm for system analysis and control. In this paper, the trajectory sensitivity of time-varying parameters of the equivalent model is analyzed. Then the non-time- varying parameters of the equivalent model are fixed as aggregated values, while the time-varying parameters are identified using the genetic learning particle swarm optimization based on phasor measurement unit data at the point of interconnection. The robustness and adaptability of the equivalent model under different scenarios are analyzed. The simulation results using the Western Electricity Coordinating Council benchmark test system show that the global searching capability to find the optimal point of the proposed method is higher than canonical particle swarm optimization and genetic algorithm by 2 orders. Further, the biggest mismatch between the identification results of the proposed method and the true values is within 10% for parameters with high sensitivity which is much better than previous work.

3. Flexible Machine Learning Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems Mingjian* Cui, Jianhui Wang, and Bo Chen IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1805–1808, 2020. [Abs] [PDF]

   This letter develops a flexible machine learning detection method for cyberattacks in distribution systems considering spatiotemporal patterns. Spatiotemporal patterns are recognized by the graph Laplacian based on system-wide measurements. A flexible Bayes classifier (BC) is used to train spatiotemporal patterns which could be violated when cyberattacks occur. Cyberattacks are detected by using flexible BCs online. The effectiveness of the developed method is demonstrated through standard IEEE 13- and 123-node test feeders.

4. Joint Probability Density Prediction for Multiperiod Thermal Ratings of Overhead Conductors Xu Jin, Mengxia Wang, Mingjian Cui, Hua Sun, and Ming Yang IEEE Transactions on Power Delivery, vol. , no. , pp. 1-1, 2020. [Abs] [PDF]

   For an overhead conductor, meteorological correlations exist among the meteorological elements that dominantly determine its thermal rating, and temporal correlations exist among the thermal ratings in sequential time periods. It is necessary to exploit these correlations to improve the performance of the probabilistic prediction of thermal ratings. To this end, a copula-based method of joint probability density prediction for multiperiod thermal ratings (JPDP-MPTR) is presented in this paper. In this method, the probability density functions (PDFs) of the thermal ratings for every 15 minutes over a 1-hour horizon are first predicted individually, considering the correlations among meteorological elements. Then, the joint probability density function (JPDF) of the multiperiod thermal ratings is further formulated based on copula theory. Finally, the probability distributions of the thermal ratings in the predicted time periods are estimated via joint sampling based on the JPDF. Numerical simulations based on actual meteorological data collected around an overhead conductor show that the proposed method can significantly improve prediction results through the integration of meteorological and temporal correlations into the probabilistic prediction of the thermal rating.

2019

1. Robust Time-Varying Synthesis Load Modeling in Distribution Networks Considering Voltage Disturbances Mingjian* Cui, Jianhui Wang, Yishen Wang, Ruisheng Diao, and Di Shi IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4438–4450, 2019. [Abs] [PDF]

   Uncertain power sources are increasingly integrated into distribution networks and causing more challenges for the traditional load modeling. A variety of distributed load components present dynamic characteristics with time-varying parameters. Toward this end, this paper proposes a robust time-varying parameter identification (TVPI) method for synthesis load modeling (SLM) in distribution networks, including time-varying ZIP, induction motor, and equivalent impedance models. The nonlinear optimization model is developed and solved by the nonlinear least square (NLS) to find the minimum error between estimated outputs and measurements. To cope with TVPI deteriorated by voltage disturbances, dynamic programming is first used to detect the disturbance. Then, a robust TVPI engine is designed to constrain the estimated time-varying parameters within a stable range. Furthermore, advanced tolerance thresholds are also required during iterations of NLS. Numerical simulations on the 9- and 129-bus distribution systems verify the effectiveness and robustness of the proposed TVPI method. Also, this method can be robust to the ambient noise of measurements.

2. Generalized Graph Laplacian Based Anomaly Detection for Spatiotemporal MicroPMU Data Mingjian* Cui, Jianhui Wang, Anthony R. Florita, and Yingchen Zhang IEEE Transactions on Power Systems, vol. 34, no. 5, pp. 3960–3963, 2019. [Abs] [PDF]

   This letter develops a novel anomaly detection method using the generalized graph Laplacian (GGL) matrix to visualize the spatiotemporal relationship of distribution-level phasor measurement unit (μPMU) data. The μPMU data in a specific time horizon is segregated into multiple segments. An optimization problem formulated as a Lagrangian function is utilized to estimate the GGL matrix. During the iterative process, an optimal update is constituted as a quadratic program (QP) problem. To perform the μPMU-based spatiotemporal analysis, normalized diagonal elements of GGL matrix are proposed as a quantitative metric. The effectiveness of the developed method is demonstrated through real-world μPMU measurements gathered from test feeders in Riverside, CA.

3. Fast Solving Method Based on Linearized Equations of Branch Power Flow for Coordinated Charging of EVs (EVCC) Jian Zhang, Mingjian* Cui, Bing Li, Hualiang Fang, and Yigang He IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4404–4418, 2019. [Abs] [PDF]

   Reported researches on smart charging methods have the disadvantages of low calculation efficiency or have not simultaneously taken the three-phase imbalance, voltage, and power flow constraints into account. It is an important topic to improve the computational speed to meet the online rolling optimization requirement for EVCC problems. In this paper, the branch power flow equations of balanced and unbalanced distribution system are derived. The linearization methods for the nonlinear terms of the branch power flow equations are proposed. Two stages linear programming (LP) is introduced for EVCC to minimize the total charging costs of the holders where three-phase imbalance, charging demand, voltage, and power flow constraints have been taken into account. Via ignoring the nonlinear terms of the branch power flow equations, the first stage LP is formulated to calculate the estimated branch power and node voltages as the initial points for linearizing the nonlinear terms of branch power flow equations. The second stage LP is formulated to calculate the optimal charging power using the linearized branch power flow equations. Four case studies show that the proposed method without the compromise of precision is significantly faster than state-of-the-art works with respect to the computational speed.

4. Deep Learning Based Time-Varying Parameter Identification for System-Wide Load Modeling Mingjian* Cui, Mahdi Khodayar, Chen Chen, Xinan Wang, Ying Zhang, and Mohammad E. Khodayar IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6102–6114, 2019. [Abs] [PDF]

   The integration of uncertain power resources is causing more challenges for traditional load modeling research. Parameter identification of load modeling is impacted by a variety of load components with time-varying characteristics. This paper develops a deep learning-based time-varying parameter identification model for composite load modeling (CLM) with ZIP load and induction motor. A multi-modal long short-term memory (M-LSTM) deep learning method is used to estimate all the time-varying parameters of CLM considering system-wide measurements. It contains a multi-modal structure that makes use of different modalities of the input data to accurately estimate time-varying load parameters. An LSTM network with a flexible number of temporal states is defined to capture powerful temporal patterns from the load parameters and measurements time series. The extracted features are further fed to a shared representation layer to capture the joint representation of input time series data. This temporal representation is used in a linear regression model to estimate time-varying load parameters at the current time. Numerical simulations on the 23- and 68-bus systems verify the effectiveness and robustness of the proposed M-LSTM method. Also, the optimal lag values of parameters and measurements as input variables are solved.

5. Machine Learning Based Anomaly Detection for Load Forecasting Under Cyberattacks Mingjian* Cui, Jianhui Wang, and Meng Yue IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5724–5734, 2019. [Abs] [PDF]

   Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. A widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.

6. A Novel Event Detection Method Using PMU Data With High Precision Mingjian* Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and Yingchen Zhang IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 454–466, 2019. [Abs] [PDF]

   To take full advantage of the considerably high reporting rate of phasor measurement units (PMU) data, this paper develops a novel PMU-based event detection methodology. Considering the huge amount of streaming PMU data, a data compression algorithm, swinging door trending (SDT), is first used to compress the PMU data and generate multiple compression intervals. Then dynamic programming is utilized to solve the optimization problem, which is recursively constituted by a score function. Based on predefined PMU event rules, dynamic programming merges adjacent compression intervals with the same slope direction. Finally, all the PMU event features are characterized. A conventional wavelet-based event detection method is compared with the developed dynamic programming based SDT (DPSDT) method. Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision. Also, the PMU event features, including the magnitude and duration of strokes, are characterized.

7. A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps Mingjian Cui, Venkat Krishnan, Bri-Mathias Hodge, and Jie Zhang IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3870-3882, 2019. [Abs]

   Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic WPR forecast (cp-WPRF) model based on copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model is adopted to characterize the WPR uncertainty and features. The Canonical maximum likelihood method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

8. A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting Mingjian Cui, Jie Zhang, Qin Wang, Venkat Krishnan, and Bri-Mathias Hodge IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1326–1338, 2019.
9. Energy-supported cascading failure model on interdependent networks considering control nodes MENG Tian, Zhengcheng Dong, Mingjian* Cui, Jianhui Wang, Xianpei Wang, and Le Zhao Physica A: Statistical Mechanics and its Applications, vol. 522, pp. 195–204, 2019.
10. Multilevel Programming-Based Coordinated Cyber Physical Attacks and Countermeasures in Smart Grid MENG Tian, Mingjian* Cui, Zhengcheng Dong, Xianpei Wang, Shengfei Yin, and Le Zhao IEEE Access, vol. 7, pp. 9836–9847, 2019.

2018

1. Estimating Ramping Requirements With Solar-Friendly Flexible Ramping Product in Multi-Timescale Power System Operations Mingjian Cui, and Jie Zhang Applied Energy, vol. 225, pp. 27–41, 2018.
2. Two Novel Load Balancing Platforms Using Common DC Buses Jian Zhang, Mingjian* Cui, Hualiang Fang, and Yigang He IEEE Transactions on Sustainable Energy, vol. 9, no. 3, pp. 1099–1107, 2018.
3. A Methodology for Quantifying Reliability Benefits From Improved Solar Power Forecasting in Multi-Timescale Power System Operations Mingjian Cui, Jie Zhang, Bri-Mathias Hodge, Siyuan Lu, and Hendrik F Hamann IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6897–6908, 2018.
4. Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model Mingjian Cui, Cong Feng, Zhenke Wang, and Jie Zhang IEEE Transactions on Sustainable Energy, vol. 9, no. 1, pp. 261–272, 2018.
5. Surrogate Model-Based Energy-Efficient Scheduling for LPWA-Based Environmental Monitoring Systems JIN Tong, Mingjian* Cui, MENG Tian, and Yigang He IEEE Access, vol. 6, pp. 59940–59948, 2018.
6. An Investigation of Coordinated Attack on Load Frequency Control Chunyu Chen, Mingjian Cui, Xinan Wang, Kaifeng Zhang, and Shengfei Yin IEEE Access, vol. 6, pp. 30414–30423, 2018.
7. Advanced Control of DFIG to Enhance the Transient Voltage Support Capability Yang-Wu Shen, Li-Qing Liang, Mingjian* Cui, Feifan Shen, Bin Zhang, and Ting CUI Journal of Energy Engineering, vol. 144, no. 2, pp. 04018009, 2018.
8. Unsupervised Clustering-Based Short-Term Solar Forecasting Cong Feng, Mingjian Cui, Bri-Mathias Hodge, Siyuan Lu, Hendrik Hamann, and Jie Zhang IEEE Transactions on Sustainable Energy, pp. 1–1, 2018.
9. Assessment of Dispatch Intervals in Power Systems with High Wind Penetration Liang Che, Xuan Liu, Xin Zhu, Mingjian Cui, and Zuyi Li IEEE Transactions on Sustainable Energy, pp. 1–1, 2018.

2017

1. Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations Mingjian Cui, Jie Zhang, Hongyu Wu, and Bri-Mathias Hodge IEEE Transactions on Sustainable Energy, vol. 8, no. 3, pp. 1064–1075, 2017.
2. Comprehensive Reactive Power Support of DFIG Adapted to Different Depth of Voltage Sags Yangwu Shen, Mingjian* Cui, Qin Wang, Feifan Shen, Bin Zhang, and Liqing Liang Energies, vol. 10, no. 6, pp. 808, 2017.
3. A Chance-Constrained Economic Dispatch Model in Wind-Thermal-Energy Storage System Yanzhe Hu, Yang Li, Mengjie Xu, Li Zhou, and Mingjian* Cui Energies, vol. 10, no. 3, pp. 326, 2017.
4. Characterizing and Analyzing Ramping Events in Wind Power, Solar Power, Load, and Netload Mingjian Cui, Jie Zhang, Cong Feng, Anthony R. Florita, Yuanzhang Sun, and Bri-Mathias Hodge Renewable Energy, vol. 111, pp. 227–244, 2017.

2016

1. Smart Charging of EVs in Residential Distribution Systems Using the Extended Iterative Method Jian Zhang, Mingjian* Cui, Hualiang Fang, and Yigang He Energies, vol. 9, no. 12, pp. 985, 2016.
2. An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events Mingjian Cui, Jie Zhang, Anthony R. Florita, Bri-Mathias Hodge, Deping Ke, and Yuanzhang Sun IEEE Transactions on Sustainable Energy, vol. 7, no. 1, pp. 150–162, 2016.

2015

1. Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method Mingjian Cui, Deping Ke, Yuanzhang Sun, Di Gan, Jie Zhang, and Bri-Mathias Hodge IEEE Transactions on Sustainable Energy, vol. 6, no. 2, pp. 422–433, 2015.
2. Statistical Scenarios Forecasting of Wind Power Ramp Events Using Modified Neural Networks Mingjian Cui, Deping Ke, Yuanzhang Sun, and Di Gan Journal of Modern Power System Clean Energy, vol. 3, no. 3, pp. 371–380, 2015.