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Huaiguang Jiang
National Renewable Energy Laboratory, Golden, CO 80401, USA

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Journal article
Published: 27 January 2020 in IEEE Access
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Global horizontal irradiance (GHI) is a critical index to indicate the output power of the photovaltaic (PV). In traditional approaches, the local GHI can be measured with very expensive instruments, and the large-area GHI collection depends on complex satellite-based models, solargis algorithms, and the high-performance computers (HPC). In this paper, a novel approach is proposed to capture the GHI conveniently and accurately. Considering the nonstationary property of the GHI on cloudy days, the GHI capturing is cast as an image regression problem. In traditional approaches, the image regression problem is treated as two parts, feature extraction (for the images) and regression model (for the regression targets), which are optimized separately and blocked the interconnections. Considering the nonlinear regression capability, a convolutional neural network (CNN) based image regression approach is proposed to provide an End-to-End solution for the cloudy day GHI capturing problem in this paper. The multilayer CNN is based on the AlexNet and VGG. The L2 (least square errors) with regularization is used as the loss function in the regression layer. For data cleaning, the Gaussian mixture model with Bayesian inference is employed to detect and eliminate the anomaly data in a nonparametric manner. The purified data are used as input data for the proposed image regression approach. In the experiments, three-month sky images and GHI data (with 1-min resolution) are provided by the National Renewable Energy Laboratory (NREL) with the HPC system. The numerical results demonstrate the feasibility and effectiveness of the proposed approach.

ACS Style

Huaiguang Jiang; Yi Gu; Yu Xie; Rui Yang; Yingchen Zhang. Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach. IEEE Access 2020, 8, 22235 -22248.

AMA Style

Huaiguang Jiang, Yi Gu, Yu Xie, Rui Yang, Yingchen Zhang. Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach. IEEE Access. 2020; 8 (99):22235-22248.

Chicago/Turabian Style

Huaiguang Jiang; Yi Gu; Yu Xie; Rui Yang; Yingchen Zhang. 2020. "Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach." IEEE Access 8, no. 99: 22235-22248.

Journal article
Published: 26 January 2018 in Sustainability
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With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); soft open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.

ACS Style

Zhenshan Zhu; Dichen Liu; Qingfen Liao; Fei Tang; Jun Jason Zhang; Huaiguang Jiang. Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network. Sustainability 2018, 10, 318 .

AMA Style

Zhenshan Zhu, Dichen Liu, Qingfen Liao, Fei Tang, Jun Jason Zhang, Huaiguang Jiang. Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network. Sustainability. 2018; 10 (2):318.

Chicago/Turabian Style

Zhenshan Zhu; Dichen Liu; Qingfen Liao; Fei Tang; Jun Jason Zhang; Huaiguang Jiang. 2018. "Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network." Sustainability 10, no. 2: 318.

Journal article
Published: 01 October 2017 in Journal of Energy Engineering
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In this paper, a big data-based approach is proposed for the security improvement of an unplanned microgrid islanding (UMI). The proposed approach contains two major steps: the first step is big data analysis of wide-area monitoring to detect a UMI and locate it; the second step is particle swarm optimization (PSO)-based stability enhancement for the UMI. First, an optimal synchrophasor measurement device selection (OSMDS) and matching pursuit decomposition (MPD)-based spatial-temporal analysis approach is proposed to significantly reduce the volume of data while keeping appropriate information from the synchrophasor measurements. Second, a random forest-based ensemble learning approach is trained to detect the UMI. When combined with grid topology, the UMI can be located. Then the stability problem of the UMI is formulated as an optimization problem and the PSO is used to find the optimal operational parameters of the UMI. An eigenvalue-based multiobjective function is proposed, which aims to improve the damping and dynamic characteristics of the UMI. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed approach.

ACS Style

Huaiguang Jiang; Yan Li; Yingchen Zhang; Jun Jason Zhang; David Wenzhong Gao; Eduard Muljadi; Yi Gu. Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding. Journal of Energy Engineering 2017, 143, 04017045 .

AMA Style

Huaiguang Jiang, Yan Li, Yingchen Zhang, Jun Jason Zhang, David Wenzhong Gao, Eduard Muljadi, Yi Gu. Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding. Journal of Energy Engineering. 2017; 143 (5):04017045.

Chicago/Turabian Style

Huaiguang Jiang; Yan Li; Yingchen Zhang; Jun Jason Zhang; David Wenzhong Gao; Eduard Muljadi; Yi Gu. 2017. "Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding." Journal of Energy Engineering 143, no. 5: 04017045.

Journal article
Published: 17 August 2017 in IEEE Intelligent Systems
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For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

ACS Style

Yingchen Zhang; Rui Yang; Kaiqing Zhang; Huaiguang Jiang; Jun Jason Zhang. Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch. IEEE Intelligent Systems 2017, 32, 59 -63.

AMA Style

Yingchen Zhang, Rui Yang, Kaiqing Zhang, Huaiguang Jiang, Jun Jason Zhang. Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch. IEEE Intelligent Systems. 2017; 32 (4):59-63.

Chicago/Turabian Style

Yingchen Zhang; Rui Yang; Kaiqing Zhang; Huaiguang Jiang; Jun Jason Zhang. 2017. "Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch." IEEE Intelligent Systems 32, no. 4: 59-63.

Journal article
Published: 18 November 2016 in IEEE Transactions on Smart Grid
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This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.

ACS Style

Huaiguang Jiang; Yingchen Zhang; Eduard Muljadi; Jun Jason Zhang; David Wenzhong Gao. A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization. IEEE Transactions on Smart Grid 2016, 9, 3341 -3350.

AMA Style

Huaiguang Jiang, Yingchen Zhang, Eduard Muljadi, Jun Jason Zhang, David Wenzhong Gao. A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization. IEEE Transactions on Smart Grid. 2016; 9 (4):3341-3350.

Chicago/Turabian Style

Huaiguang Jiang; Yingchen Zhang; Eduard Muljadi; Jun Jason Zhang; David Wenzhong Gao. 2016. "A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization." IEEE Transactions on Smart Grid 9, no. 4: 3341-3350.

Conference paper
Published: 14 November 2016 in 2016 IEEE Power and Energy Society General Meeting (PESGM)
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This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster.

ACS Style

Huaiguang Jiang; Yingchen Zhang. Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine. 2016 IEEE Power and Energy Society General Meeting (PESGM) 2016, 1 -5.

AMA Style

Huaiguang Jiang, Yingchen Zhang. Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine. 2016 IEEE Power and Energy Society General Meeting (PESGM). 2016; ():1-5.

Chicago/Turabian Style

Huaiguang Jiang; Yingchen Zhang. 2016. "Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine." 2016 IEEE Power and Energy Society General Meeting (PESGM) , no. : 1-5.

Conference paper
Published: 01 July 2016 in 2016 IEEE Power and Energy Society General Meeting (PESGM)
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This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

ACS Style

Fei Ding; Huaiguang Jiang; Jin Tan. Automatic distribution network reconfiguration: An event-driven approach. 2016 IEEE Power and Energy Society General Meeting (PESGM) 2016, 1 -5.

AMA Style

Fei Ding, Huaiguang Jiang, Jin Tan. Automatic distribution network reconfiguration: An event-driven approach. 2016 IEEE Power and Energy Society General Meeting (PESGM). 2016; ():1-5.

Chicago/Turabian Style

Fei Ding; Huaiguang Jiang; Jin Tan. 2016. "Automatic distribution network reconfiguration: An event-driven approach." 2016 IEEE Power and Energy Society General Meeting (PESGM) , no. : 1-5.

Journal article
Published: 14 April 2016 in IEEE Transactions on Smart Grid
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An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

ACS Style

Huaiguang Jiang; Xiaoxiao Dai; David Wenzhong Gao; Jun Jason Zhang; Yingchen Zhang; Eduard Muljadi. Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis. IEEE Transactions on Smart Grid 2016, 7, 2525 -2536.

AMA Style

Huaiguang Jiang, Xiaoxiao Dai, David Wenzhong Gao, Jun Jason Zhang, Yingchen Zhang, Eduard Muljadi. Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis. IEEE Transactions on Smart Grid. 2016; 7 (5):2525-2536.

Chicago/Turabian Style

Huaiguang Jiang; Xiaoxiao Dai; David Wenzhong Gao; Jun Jason Zhang; Yingchen Zhang; Eduard Muljadi. 2016. "Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis." IEEE Transactions on Smart Grid 7, no. 5: 2525-2536.

Conference paper
Published: 01 July 2015 in 2015 IEEE Power & Energy Society General Meeting
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Because wind power penetration levels in electric power systems are continuously increasing, voltage stability is a critical issue for maintaining power system security and operation. The traditional methods to analyze voltage stability can be classified into two categories: dynamic and steady-state. Dynamic analysis relies on time-domain simulations of faults at different locations; however, this method needs to exhaust faults at all locations to find the security region for voltage at a single bus. With the widely located phasor measurement units (PMUs), the Thevenin equivalent matrix can be calculated by the voltage and current information collected by the PMUs. This paper proposes a method based on a Thevenin equivalent matrix to identify system locations that will have the greatest impact on the voltage at the wind power plant's point of interconnection. The number of dynamic voltage stability analysis runs is greatly reduced by using the proposed method. The numerical results demonstrate the feasibility, effectiveness, and robustness of the proposed approach for voltage security assessment for a wind power plant.

ACS Style

Huaiguang Jiang; Yingchen Zhang; Jun Jason Zhang; Eduard Muljadi. PMU-aided voltage security assessment for a wind power plant. 2015 IEEE Power & Energy Society General Meeting 2015, 1 -5.

AMA Style

Huaiguang Jiang, Yingchen Zhang, Jun Jason Zhang, Eduard Muljadi. PMU-aided voltage security assessment for a wind power plant. 2015 IEEE Power & Energy Society General Meeting. 2015; ():1-5.

Chicago/Turabian Style

Huaiguang Jiang; Yingchen Zhang; Jun Jason Zhang; Eduard Muljadi. 2015. "PMU-aided voltage security assessment for a wind power plant." 2015 IEEE Power & Energy Society General Meeting , no. : 1-5.

Journal article
Published: 01 April 2015 in Digital Signal Processing
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ACS Style

Mahesh K. Banavar; Jun J. Zhang; Bhavana Chakraborty; Homin Kwon; Ying Li; Huaiguang Jiang; Andreas Spanias; Cihan Tepedelenlioğlu; Chaitali Chakrabarti; Antonia Papandreou-Suppappola. An overview of recent advances on distributed and agile sensing algorithms and implementation. Digital Signal Processing 2015, 39, 1 -14.

AMA Style

Mahesh K. Banavar, Jun J. Zhang, Bhavana Chakraborty, Homin Kwon, Ying Li, Huaiguang Jiang, Andreas Spanias, Cihan Tepedelenlioğlu, Chaitali Chakrabarti, Antonia Papandreou-Suppappola. An overview of recent advances on distributed and agile sensing algorithms and implementation. Digital Signal Processing. 2015; 39 ():1-14.

Chicago/Turabian Style

Mahesh K. Banavar; Jun J. Zhang; Bhavana Chakraborty; Homin Kwon; Ying Li; Huaiguang Jiang; Andreas Spanias; Cihan Tepedelenlioğlu; Chaitali Chakrabarti; Antonia Papandreou-Suppappola. 2015. "An overview of recent advances on distributed and agile sensing algorithms and implementation." Digital Signal Processing 39, no. : 1-14.

Journal article
Published: 20 January 2015 in IEEE Transactions on Smart Grid
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Wind energy is highly location-dependent. Many desirable wind resources in North America are located in rural areas without direct access to the transmission grid. By connecting megawatt-scale wind turbines to the distribution system, the cost of building transmission facilities can be avoided and wind power supplied to consumers can be greatly increased; however, integrating megawatt-scale wind turbines on distribution feeders will impact the distribution feeder stability, especially voltage stability. Distributed wind turbine generators (WTGs) have the capability to aid in grid stability if equipped with appropriate controllers, but few investigations are focusing on this. This paper investigates the potential of using real-time measurements from distribution phasor measurement units for a new WTG control algorithm to stabilize the voltage deviation of a distribution feeder. This paper proposes a novel auxiliary coordinated-control approach based on a support vector machine (SVM) predictor and a multiple-input and multiple-output model predictive control on linear time-invariant and linear time-variant systems. The voltage condition of the distribution system is predicted by the SVM classifier using synchrophasor measurement data. The controllers equipped on WTGs are triggered by the prediction results. The IEEE 13-bus distribution system with WTGs is used to validate and evaluate the proposed auxiliary control approach.

ACS Style

Huaiguang Jiang; Yingchen Zhang; Jun Jason Zhang; David Wenzhong Gao; Eduard Muljadi. Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration. IEEE Transactions on Smart Grid 2015, 6, 2107 -2115.

AMA Style

Huaiguang Jiang, Yingchen Zhang, Jun Jason Zhang, David Wenzhong Gao, Eduard Muljadi. Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration. IEEE Transactions on Smart Grid. 2015; 6 (4):2107-2115.

Chicago/Turabian Style

Huaiguang Jiang; Yingchen Zhang; Jun Jason Zhang; David Wenzhong Gao; Eduard Muljadi. 2015. "Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration." IEEE Transactions on Smart Grid 6, no. 4: 2107-2115.

Conference paper
Published: 01 November 2014 in 2014 48th Asilomar Conference on Signals, Systems and Computers
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An approach for fully characterizing a synchrophasor measurement system is proposed in this paper, which aims to provide substantial data volume reduction while keep comprehensive information from synchrophasor measurements in time and spatial domains. Specifically, the optimal synchrophasor sensor placement (OSSP) problem with the effect of zero-injection buses (ZIB) is modeled and solved to ensure the minimum number of installed sensors and also the full observability of the power system. After the sensors are optimally placed, the matching pursuit decomposition algorithm is used to extract the time-frequency features for full description of the time-domain synchrophasor measurements. To demonstrate the effectiveness of the proposed characterization approach, power system situational awareness is investigated on Hidden Markov Model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the proposed approach. Several IEEE standard systems such as the IEEE 14 bus system, IEEE 30 bus system and IEEE 39 bus system are employed to validate and evaluate the proposed approach.

ACS Style

Huaiguang Jiang; Lei Huang; Jun Jason Zhang; Yingchen Zhang; David Wenzhong Gao. Spatial-temporal characterization of synchrophasor measurement systems A big data approach for smart grid system situational awareness. 2014 48th Asilomar Conference on Signals, Systems and Computers 2014, 750 -754.

AMA Style

Huaiguang Jiang, Lei Huang, Jun Jason Zhang, Yingchen Zhang, David Wenzhong Gao. Spatial-temporal characterization of synchrophasor measurement systems A big data approach for smart grid system situational awareness. 2014 48th Asilomar Conference on Signals, Systems and Computers. 2014; ():750-754.

Chicago/Turabian Style

Huaiguang Jiang; Lei Huang; Jun Jason Zhang; Yingchen Zhang; David Wenzhong Gao. 2014. "Spatial-temporal characterization of synchrophasor measurement systems A big data approach for smart grid system situational awareness." 2014 48th Asilomar Conference on Signals, Systems and Computers , no. : 750-754.

Conference paper
Published: 01 November 2014 in 2014 48th Asilomar Conference on Signals, Systems and Computers
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A statistical scheduling approach to economic dispatch and energy reserves is proposed in this paper. The proposed approach focuses on minimizing the overall power operating cost with considerations of renewable energy uncertainty and power system security. In such a system, it is challenging and yet an open question on the scheduling of economic dispatch together with energy reserves, due to renewable energy generation uncertainty, and spatially wide distribution of energy resources. The hybrid power system scheduling is formulated as a convex programming problem to minimize power operating cost, taking considerations of renewable energy generation, power generation-consumption balance and power system security. A genetic algorithm based approach is used for solving the minimization of the power operating cost. The IEEE 24-bus reliability test system (IEEE-RTS), which is commonly used for evaluating the price stability of power system and reliability, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach.

ACS Style

Yi Gu; Huaiguang Jiang; Yingchen Zhang; David Wenzhong Gao. Statistical scheduling of economic dispatch and energy reserves of hybrid power systems with high renewable energy penetration. 2014 48th Asilomar Conference on Signals, Systems and Computers 2014, 530 -534.

AMA Style

Yi Gu, Huaiguang Jiang, Yingchen Zhang, David Wenzhong Gao. Statistical scheduling of economic dispatch and energy reserves of hybrid power systems with high renewable energy penetration. 2014 48th Asilomar Conference on Signals, Systems and Computers. 2014; ():530-534.

Chicago/Turabian Style

Yi Gu; Huaiguang Jiang; Yingchen Zhang; David Wenzhong Gao. 2014. "Statistical scheduling of economic dispatch and energy reserves of hybrid power systems with high renewable energy penetration." 2014 48th Asilomar Conference on Signals, Systems and Computers , no. : 530-534.

Journal article
Published: 08 July 2014 in IEEE Transactions on Smart Grid
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A fault detection, identification, and location approach is proposed and studied in this paper. This approach is based on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features, and fault contour maps generated by machine learning algorithms in smart grid (SG) systems. Specifically, the time-frequency features are extracted by MPD from the frequency and voltage signals, which are sampled by the frequency disturbance recorders in SG. A hybrid clustering algorithm is then developed and used to cluster the frequency and voltage signal features into various symbols. Using the symbols, two detection HMMs are trained for fault detection to distinguish between normal and abnormal SG operation conditions. Also, several identification HMMs are trained under different system fault scenarios, and if a fault occurs, the trained identification HMMs are used to identify different fault types. In the meantime, if the fault is detected by the detection HMMs, a fault contour map will be generated using the feature extracted by the MPD from the voltage signals and topology information of SG. The numerical results demonstrate the feasibility, effectiveness, and accuracy of the proposed approach for the diagnosis of various types of faults with different measurement signal-to-noise ratios in SG systems.

ACS Style

Huaiguang Jiang; Jun J. Zhang; Wenzhong Gao; Ziping Wu. Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods. IEEE Transactions on Smart Grid 2014, 5, 2947 -2956.

AMA Style

Huaiguang Jiang, Jun J. Zhang, Wenzhong Gao, Ziping Wu. Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods. IEEE Transactions on Smart Grid. 2014; 5 (6):2947-2956.

Chicago/Turabian Style

Huaiguang Jiang; Jun J. Zhang; Wenzhong Gao; Ziping Wu. 2014. "Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods." IEEE Transactions on Smart Grid 5, no. 6: 2947-2956.

Conference paper
Published: 01 July 2014 in 2014 IEEE Symposium on Power Electronics and Machines for Wind and Water Applications
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An auxiliary coordinated control approach focusing on transient voltage stability is proposed in this paper. The concept is based on support vector machine (SVM) classifier and multiple-input and multiple-output (MIMO) model predictive control (MPC) on the high penetration renewable power system. To achieve the objective, the voltage stability condition of the power system is predicted by the SVM classifier first, using measured synchrophasor data in the power system. Next, the control strategy is triggered by the prediction results. The designed auxiliary MPC strategy will augment the existing control variables aiming to keep transient voltage stability. To validate the proposed approach, the Kundur two-area power system with a wind plant is built and the numerical results demonstrate the feasibility, effectiveness and accuracy of the proposed method.

ACS Style

Huaiguang Jiang; Jun Jason Zhang; David Wenzhong Gao; Yingchen Zhang; Eduard Muljadi; Zhang J.J.; Muljadi E.; Gao D.W.. Synchrophasor based auxiliary controller to enhance power system transient voltage stability in a high penetration renewable energy scenario. 2014 IEEE Symposium on Power Electronics and Machines for Wind and Water Applications 2014, 1 -7.

AMA Style

Huaiguang Jiang, Jun Jason Zhang, David Wenzhong Gao, Yingchen Zhang, Eduard Muljadi, Zhang J.J., Muljadi E., Gao D.W.. Synchrophasor based auxiliary controller to enhance power system transient voltage stability in a high penetration renewable energy scenario. 2014 IEEE Symposium on Power Electronics and Machines for Wind and Water Applications. 2014; ():1-7.

Chicago/Turabian Style

Huaiguang Jiang; Jun Jason Zhang; David Wenzhong Gao; Yingchen Zhang; Eduard Muljadi; Zhang J.J.; Muljadi E.; Gao D.W.. 2014. "Synchrophasor based auxiliary controller to enhance power system transient voltage stability in a high penetration renewable energy scenario." 2014 IEEE Symposium on Power Electronics and Machines for Wind and Water Applications , no. : 1-7.

Conference paper
Published: 01 November 2012 in 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
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A wavelet based fault localization method in Smart Grid (SG) systems is proposed in this paper. In SG systems, voltage, current, frequency and phase measurements can be collected in real-time using phasor measurement units (PMUs). Based on the wavelet analysis of these measurements, the signal features can be extracted by computing the maximum wavelet transform coefficients (WTCs) and further processing them with a new hybrid clustering algorithm. The clustered signal features then form a fault contour map which can be used to locate faults in the SG system accurately. Both long-term and short-term faults of transmission line, transformer, generator, and load, which are major components of SG systems, are simulated in PSCAD and PowerWorld using the IEEE New England 39-bus system to verify the proposed method. The numerical results demonstrate the feasibility and effectiveness of our proposed method for accurate fault localization in SG systems.

ACS Style

Huaiguang Jiang; Jun Jason Zhang; David W. Gao. Fault localization in Smart Grid using wavelet analysis and unsupervised learning. 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR) 2012, 386 -390.

AMA Style

Huaiguang Jiang, Jun Jason Zhang, David W. Gao. Fault localization in Smart Grid using wavelet analysis and unsupervised learning. 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR). 2012; ():386-390.

Chicago/Turabian Style

Huaiguang Jiang; Jun Jason Zhang; David W. Gao. 2012. "Fault localization in Smart Grid using wavelet analysis and unsupervised learning." 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR) , no. : 386-390.