This page has only limited features, please log in for full access.

Unclaimed
Shengyuan Liu
Electrical Engineering and Computer Science, University of Tennessee Knoxville, 4292 Knoxville, Tennessee, United States

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 24 June 2021 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. The proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

ACS Style

Shengyuan Liu; Shutang You; Z.Z. Lin; Chujie Zeng; Hongyu Li; Weikang Wang; Xuetao Hu; Yilu Liu. Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Shengyuan Liu, Shutang You, Z.Z. Lin, Chujie Zeng, Hongyu Li, Weikang Wang, Xuetao Hu, Yilu Liu. Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Shengyuan Liu; Shutang You; Z.Z. Lin; Chujie Zeng; Hongyu Li; Weikang Wang; Xuetao Hu; Yilu Liu. 2021. "Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Special issue paper
Published: 27 May 2021 in International Transactions on Electrical Energy Systems
Reads 0
Downloads 0

With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies. To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to demonstrate the influence of various factors on electrical demand. The proposed algorithm's effectiveness for LF and error formulations shows that optimal-BRA-enabled LF presents better accuracy than state-of-the-art approaches. Thus, the proposed electric demand prediction strategy could help the system operator know DR potential at different times better, leading to optimal system resources dispatching through DR actions.

ACS Style

Muhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Zhang Jinai; Mian Rizwan; Intisar Ali Sajjad. Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors. International Transactions on Electrical Energy Systems 2021, e12967 .

AMA Style

Muhammad Waseem, Zhenzhi Lin, Shengyuan Liu, Zhang Jinai, Mian Rizwan, Intisar Ali Sajjad. Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors. International Transactions on Electrical Energy Systems. 2021; ():e12967.

Chicago/Turabian Style

Muhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Zhang Jinai; Mian Rizwan; Intisar Ali Sajjad. 2021. "Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors." International Transactions on Electrical Energy Systems , no. : e12967.

Short communication
Published: 06 April 2021 in Electric Power Systems Research
Reads 0
Downloads 0

Synchrophasor measurement devices (SMDs) have been widely deployed to support real-time monitoring and control of power systems. In the meantime, data spoofing has emerged in recent years. Therefore, it is of great importance to study data authentication algorithms for detecting and defending the data spoofing effectively. In this work, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract temporal signatures hidden in frequency, voltage angle and amplitude data; then the gated recurrent unit (GRU) employs these temporal signatures for data source authentication. In case studies, the performances of different algorithms are tested in large-scale power systems with numerous SMDs for the first time, and comparisons among different algorithms show that the proposed algorithm can achieve a higher accuracy of data source authentication with a shorter time window.

ACS Style

Shengyuan Liu; Shutang You; Chujie Zeng; He Yin; Zhenzhi Lin; Yuqing Dong; Wei Qiu; Wenxuan Yao; Yilu Liu. Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU. Electric Power Systems Research 2021, 196, 107207 .

AMA Style

Shengyuan Liu, Shutang You, Chujie Zeng, He Yin, Zhenzhi Lin, Yuqing Dong, Wei Qiu, Wenxuan Yao, Yilu Liu. Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU. Electric Power Systems Research. 2021; 196 ():107207.

Chicago/Turabian Style

Shengyuan Liu; Shutang You; Chujie Zeng; He Yin; Zhenzhi Lin; Yuqing Dong; Wei Qiu; Wenxuan Yao; Yilu Liu. 2021. "Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU." Electric Power Systems Research 196, no. : 107207.

Original research paper
Published: 21 February 2021 in IET Generation, Transmission & Distribution
Reads 0
Downloads 0

As the physical carrier of the Energy Internet, integrated energy system (IES) is a future development trend in the energy field, and the optimal scheduling of IES for improving energy utilisation efficiency has become a hot topic. An optimal day‐ahead scheduling model of multiple IESs considering integrated demand response (IDR), cooperative game and virtual energy storage (VES) is proposed innovatively in this study to maximise the overall benefits of the cooperative alliance. IDR and VES are considered together for the first time to optimise the internal scheduling of each IES, where IDR can enhance the response potential on the demand side and VES can improve the scheduling flexibility of IES. Cooperative game theory is utilised to process the energy trading mechanism among multiple IESs and the Nash bargaining method is utilised to solve the cooperative game problem and obtain a fair and Pareto‐optimal energy trading strategy. The case study shows that the proposed model effectively improves the operating benefits and the renewable energy penetration levels of each IES.

ACS Style

Changming Chen; Xin Deng; Zhi Zhang; Shengyuan Liu; Muhammad Waseem; Yangqing Dan; Zhou Lan; Zhenzhi Lin; Li Yang; Yi Ding. Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage. IET Generation, Transmission & Distribution 2021, 1 .

AMA Style

Changming Chen, Xin Deng, Zhi Zhang, Shengyuan Liu, Muhammad Waseem, Yangqing Dan, Zhou Lan, Zhenzhi Lin, Li Yang, Yi Ding. Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage. IET Generation, Transmission & Distribution. 2021; ():1.

Chicago/Turabian Style

Changming Chen; Xin Deng; Zhi Zhang; Shengyuan Liu; Muhammad Waseem; Yangqing Dan; Zhou Lan; Zhenzhi Lin; Li Yang; Yi Ding. 2021. "Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage." IET Generation, Transmission & Distribution , no. : 1.

Review
Published: 23 November 2020 in International Transactions on Electrical Energy Systems
Reads 0
Downloads 0

Background When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations.

ACS Style

Tarique Aziz; Zhenzhi Lin; Muhammad Waseem; Shengyuan Liu. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems 2020, 31, 1 .

AMA Style

Tarique Aziz, Zhenzhi Lin, Muhammad Waseem, Shengyuan Liu. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems. 2020; 31 (3):1.

Chicago/Turabian Style

Tarique Aziz; Zhenzhi Lin; Muhammad Waseem; Shengyuan Liu. 2020. "Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience." International Transactions on Electrical Energy Systems 31, no. 3: 1.

Review
Published: 25 September 2020 in Sustainability
Reads 0
Downloads 0

Due to the heavy stress on environmental deterioration and the excessive consumption of fossil resources, the transition of global energy from fossil fuel energy to clean energy has significantly accelerated in recent years. The power industry and policymakers in almost all countries are focusing on clean energy development. Thanks to progressive clean energy policies, significant progress in clean energy integration and greenhouse gas reduction has been achieved around the world. However, due to the differences in economic structures, clean energy distributions, and development models, clean energy policy scope, focus, and coverage vary between different countries, states, and utilities. This paper aims at providing a policy review for readers to easily obtain clean energy policy information on various clean energies in the U.S. and some other countries. Firstly, this paper reviews and compares some countries’ clean energy policies on electricity. Then, taking the U.S. as an example, this paper introduces the clean energy policies of some representative states and utilities in the U.S in perspectives of renewable energies, electric vehicles, and energy storage.

ACS Style

Kaiqi Sun; HuangQing Xiao; Shengyuan Liu; Shutang You; Fan Yang; Yuqing Dong; Weikang Wang; Yilu Liu. A Review of Clean Electricity Policies—From Countries to Utilities. Sustainability 2020, 12, 7946 .

AMA Style

Kaiqi Sun, HuangQing Xiao, Shengyuan Liu, Shutang You, Fan Yang, Yuqing Dong, Weikang Wang, Yilu Liu. A Review of Clean Electricity Policies—From Countries to Utilities. Sustainability. 2020; 12 (19):7946.

Chicago/Turabian Style

Kaiqi Sun; HuangQing Xiao; Shengyuan Liu; Shutang You; Fan Yang; Yuqing Dong; Weikang Wang; Yilu Liu. 2020. "A Review of Clean Electricity Policies—From Countries to Utilities." Sustainability 12, no. 19: 7946.

Research article
Published: 03 September 2020 in IET Generation, Transmission & Distribution
Reads 0
Downloads 0

Natural disasters, such as typhoons and earthquakes, pose great challenges to power system resilience and power supply reliability. It is widely acknowledged that critical transmission lines play a significant role in enhancing the resilience and reliability of power systems under severe natural disasters. In order to improve the disaster resilience of power systems with renewables, a critical line identification approach is proposed in this work based on the complex network theory. Firstly, the concept of core skeleton network (CSN) of power systems with renewables is introduced and a two-step strategy is presented to optimise the CSNs of the concerned power system considering the variable outputs of renewables. Then, a statistical salience method incorporating edge salience and the state-of-the-art null model in complex network theory is proposed to identify critical lines of each CSN and the concerned power system with renewables. Finally, the effectiveness of the proposed critical lines identification approach is verified by simulations on the modified IEEE 118-bus system with renewables, Guangdong Provincial Power System (GPPS) and Zhejiang Provincial Power System (ZPPS) in China. Simulation results show that by using the proposed approach, the critical transmission lines can be effectively identified, not requiring a predefined number of critical lines.

ACS Style

Yuxuan Zhao; Shengyuan Liu; Zhenzhi Lin; Li Yang; Qiang Gao; Yiwei Chen. Identification of critical lines for enhancing disaster resilience of power systems with renewables based on complex network theory. IET Generation, Transmission & Distribution 2020, 14, 4459 -4467.

AMA Style

Yuxuan Zhao, Shengyuan Liu, Zhenzhi Lin, Li Yang, Qiang Gao, Yiwei Chen. Identification of critical lines for enhancing disaster resilience of power systems with renewables based on complex network theory. IET Generation, Transmission & Distribution. 2020; 14 (20):4459-4467.

Chicago/Turabian Style

Yuxuan Zhao; Shengyuan Liu; Zhenzhi Lin; Li Yang; Qiang Gao; Yiwei Chen. 2020. "Identification of critical lines for enhancing disaster resilience of power systems with renewables based on complex network theory." IET Generation, Transmission & Distribution 14, no. 20: 4459-4467.

Research article
Published: 21 May 2020 in IET Generation, Transmission & Distribution
Reads 0
Downloads 0

With the development of renewable energy market, Renewable Portfolio Standard (RPS) has become the targeted renewable energy incentive mechanism in China and it is under a government's long-term plan that RPS overtakes feed-in tariff gradually in recent years. Given this background, the decision-making model of electricity retailers to accomplish the obligation under RPS is constructed as a bi-layer portfolio selection model. In the inner-layer, both capital allocation of the retailers to purchase various kinds of renewable energy capacity as well as RECs are optimised. In the outer-layer, the capital investment portfolio of each risky and risk-free strategy is determined based on the Behavioural Portfolio Theory, with different mental accounts (MAs) with different aspiration points and threshold levels built to describe the investor philosophy of retailers during the decision-making process. The simulation results of a provincial electricity market in China demonstrate that the presented model can effectively assist electricity retailers in making their capital investment strategies under RPS with relevant market factors. In addition, the proposed model provides insights for policy makers to set key parameters involving the design of RPS by analysing behaviours of retailers, including required quota ratio and value of fines.

ACS Style

Yicheng Jiang; Shengyuan Liu; Li Yang; Zhenzhi Lin; Yi Ding; Chuan He; Jing Li; Kai Wang. Bi‐layer portfolio selection model for electricity retailers based on behavioural portfolio theory under quota obligation of RPS. IET Generation, Transmission & Distribution 2020, 14, 2857 -2868.

AMA Style

Yicheng Jiang, Shengyuan Liu, Li Yang, Zhenzhi Lin, Yi Ding, Chuan He, Jing Li, Kai Wang. Bi‐layer portfolio selection model for electricity retailers based on behavioural portfolio theory under quota obligation of RPS. IET Generation, Transmission & Distribution. 2020; 14 (14):2857-2868.

Chicago/Turabian Style

Yicheng Jiang; Shengyuan Liu; Li Yang; Zhenzhi Lin; Yi Ding; Chuan He; Jing Li; Kai Wang. 2020. "Bi‐layer portfolio selection model for electricity retailers based on behavioural portfolio theory under quota obligation of RPS." IET Generation, Transmission & Distribution 14, no. 14: 2857-2868.

Journal article
Published: 04 May 2020 in IEEE Access
Reads 0
Downloads 0

The development of the concentrating solar power (CSP) plant as a new dispatchable resource that can participate in the electricity markets as an independent power producer and coordinate intermittent renewables has attracted much attention recently. In this work, optimal offering strategies of a price-taker CSP plant in the day-ahead (DA) and real-time (RT) electricity markets are addressed considering non-stochastic uncertainties (NSUs) from the thermal production of the CSP plant and stochastic uncertainties (SUs) from the market prices as well as the risk attitude of the CSP plant concerned. A hybrid stochastic information gap approach (SIGA) integrating the well-established information gap decision theory with the mixed conditional value at risk (CVaR) is proposed to hedge the revenue risk against NSUs and SUs in the offering problem based on the risk preference of the decision maker. A two-stage architecture is utilized for framing the DA and RT offering problems, where the first-stage co-optimizes offering strategies in the DA and RT markets, while the second-stage determines the actual RT hourly offering strategy in a rolling horizon manner. Case studies show that the SIGA can make optimal offering strategies against the non-stochastic thermal production and stochastic market prices given the risk attitude of the CSP plant. Comparisons also demonstrate that the SIGA could be an effective tool to manage coexistent NSUs and SUs.

ACS Style

Yuxuan Zhao; Shengyuan Liu; Zhenzhi Lin; Fushuan Wen; Li Yang; Qin Wang. A Mixed CVaR-Based Stochastic Information Gap Approach for Building Optimal Offering Strategies of a CSP Plant in Electricity Markets. IEEE Access 2020, 8, 85772 -85783.

AMA Style

Yuxuan Zhao, Shengyuan Liu, Zhenzhi Lin, Fushuan Wen, Li Yang, Qin Wang. A Mixed CVaR-Based Stochastic Information Gap Approach for Building Optimal Offering Strategies of a CSP Plant in Electricity Markets. IEEE Access. 2020; 8 (99):85772-85783.

Chicago/Turabian Style

Yuxuan Zhao; Shengyuan Liu; Zhenzhi Lin; Fushuan Wen; Li Yang; Qin Wang. 2020. "A Mixed CVaR-Based Stochastic Information Gap Approach for Building Optimal Offering Strategies of a CSP Plant in Electricity Markets." IEEE Access 8, no. 99: 85772-85783.

Journal article
Published: 08 April 2020 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

With the development and wide deployment of measurement equipment, data can be automatically measured and visualized for situation awareness in power systems. However, the cyber security of power systems is also threated by data spoofing attacks. This letter proposed a measurement data source authentication (MDSA) algorithm based on feature extraction techniques including ensemble empirical mode decomposition (EEMD) and fast Fourier transform (FFT), and machine learning for real-time measurement data classification. Compared with previous work, the proposed algorithm can achieve higher accuracy of MDSA using a shorter window of data from closely located synchrophasor measurement sensors.

ACS Style

Shengyuan Liu; Shutang You; He Yin; Zhenzhi Lin; Yilu Liu; Wenxuan Yao; Lakshmi Sundaresh. Model-Free Data Authentication for Cyber Security in Power Systems. IEEE Transactions on Smart Grid 2020, 11, 4565 -4568.

AMA Style

Shengyuan Liu, Shutang You, He Yin, Zhenzhi Lin, Yilu Liu, Wenxuan Yao, Lakshmi Sundaresh. Model-Free Data Authentication for Cyber Security in Power Systems. IEEE Transactions on Smart Grid. 2020; 11 (5):4565-4568.

Chicago/Turabian Style

Shengyuan Liu; Shutang You; He Yin; Zhenzhi Lin; Yilu Liu; Wenxuan Yao; Lakshmi Sundaresh. 2020. "Model-Free Data Authentication for Cyber Security in Power Systems." IEEE Transactions on Smart Grid 11, no. 5: 4565-4568.

Article
Published: 11 February 2020 in IET Smart Grid
Reads 0
Downloads 0

With increased penetration of wind power units and plug-in electric vehicles (PEVs), their control flexibility and quick response potentially provide an alternative way to fulfil the need for rapid restoration. A two-stage restoration strategy optimisation approach is presented with the coordination of PEVs and wind power units considered. The optimisation model is to maximise the restored generation capability and minimise the fluctuation of cranking power. In the first stage, the aim is to provide reliable cranking power remotely for black start generators through coordinated dispatch of PEVs and wind power units and quadratic programming (QP) models for dispatching electric vehicle aggregators (EVAs) subject to wind power fluctuations, and for dispatching numerous PEVs within each EVA are developed. To ensure close coordination between these two dispatching procedures, bi-level programming-based hierarchical decomposition approach is used to solve the QP models in an iterative way. In the second stage, an integer linear programming model is proposed to optimise the restoration schedules through an effective transformation of the original non-linear formulation, so as to reduce the computing time and effort significantly. Finally, a case study is presented to demonstrate the effectiveness and essential features of the developed models and methods.

ACS Style

Can Zhang; Huayi Zhang; Shengyuan Liu; Zhenzhi Lin; Fushuan Wen. Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units. IET Smart Grid 2020, 3, 123 -132.

AMA Style

Can Zhang, Huayi Zhang, Shengyuan Liu, Zhenzhi Lin, Fushuan Wen. Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units. IET Smart Grid. 2020; 3 (2):123-132.

Chicago/Turabian Style

Can Zhang; Huayi Zhang; Shengyuan Liu; Zhenzhi Lin; Fushuan Wen. 2020. "Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units." IET Smart Grid 3, no. 2: 123-132.

Journal article
Published: 06 February 2020 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F-statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

ACS Style

Shengyuan Liu; Zhenzhi Lin; Yuxuan Zhao; Yilu Liu; Yi Ding; Bo Zhang; Li Yang; Qin Wang; Samantha Emma White. Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources. IEEE Transactions on Power Systems 2020, 35, 3574 -3587.

AMA Style

Shengyuan Liu, Zhenzhi Lin, Yuxuan Zhao, Yilu Liu, Yi Ding, Bo Zhang, Li Yang, Qin Wang, Samantha Emma White. Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources. IEEE Transactions on Power Systems. 2020; 35 (5):3574-3587.

Chicago/Turabian Style

Shengyuan Liu; Zhenzhi Lin; Yuxuan Zhao; Yilu Liu; Yi Ding; Bo Zhang; Li Yang; Qin Wang; Samantha Emma White. 2020. "Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources." IEEE Transactions on Power Systems 35, no. 5: 3574-3587.

Journal article
Published: 28 January 2020 in IEEE Access
Reads 0
Downloads 0

With the wide deployment of advancing metering infrastructure (AMI) in power distribution systems, the quantity of power consumers’ electricity data is increasing rapidly and the data also become more and more accurate. To make full use of these power consumers’ AMI data, a data-driven abnormity assessment algorithm for low-voltage power consumers is proposed based on the CRITIC (CRiteria Importance Though Intercrieria Correlation) method and the improved radar chart method. First, the indexes that characterize the consumer's abnormal features of power consumption and supplies are extracted from the original AMI data. Then, the abnormity assessment algorithm is used to determine power consumers’ abnormal features of power consumption and supplies by using the extracted indexes, in which the weights of indexes are determined by the CRITIC method and the assessment value of abnormal features is determined by the improved radar chart method. Next, the abnormity assessment algorithm is used again to assess power consumers’ power consumption and supplies abnormities. Finally, the effectiveness of proposed algorithm is demonstrated in case studies by employing AMI data collected from power utilities in Zhejiang Province, China, and the results show that the algorithm can be used in actual applications.

ACS Style

Bo Zhang; Shengyuan Liu; Hanyu Dong; Songsong Zheng; Ling Zhao; Ruiqian Zhu; Limei Zhao; Zhenzhi Lin; Li Yang; Qin Wang. Data-Driven Abnormity Assessment for Low-Voltage Power Consumption and Supplies Based on CRITIC and Improved Radar Chart Algorithms. IEEE Access 2020, 8, 27139 -27151.

AMA Style

Bo Zhang, Shengyuan Liu, Hanyu Dong, Songsong Zheng, Ling Zhao, Ruiqian Zhu, Limei Zhao, Zhenzhi Lin, Li Yang, Qin Wang. Data-Driven Abnormity Assessment for Low-Voltage Power Consumption and Supplies Based on CRITIC and Improved Radar Chart Algorithms. IEEE Access. 2020; 8 (99):27139-27151.

Chicago/Turabian Style

Bo Zhang; Shengyuan Liu; Hanyu Dong; Songsong Zheng; Ling Zhao; Ruiqian Zhu; Limei Zhao; Zhenzhi Lin; Li Yang; Qin Wang. 2020. "Data-Driven Abnormity Assessment for Low-Voltage Power Consumption and Supplies Based on CRITIC and Improved Radar Chart Algorithms." IEEE Access 8, no. 99: 27139-27151.

Journal article
Published: 10 January 2020 in IEEE Transactions on Power Systems
Reads 0
Downloads 0

Unbalanced operation of a three-phase distribution system could incur more power losses, compared with the balanced operation. So far, the phase information of users in low voltage areas is not recorded by power utilities and the three-phase unbalance degree is very significant in some low voltage distribution systems. Given this background, this letter presents an integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation, and a Mixed Integer Linear Programming (MILP) model is then formulated. Some actual cases in Zhejiang province, China, are utilized to verify the effectiveness of the presented phase identification algorithm, and simulation results show that the three-phase unbalance can be significantly mitigated.

ACS Style

Shengyuan Liu; Renyun Jin; Haifeng Qiu; Xueyuan Cui; Zhenzhi Lin; Zikuan Lian; Zhian Lin; Fushuan Wen; Yi Ding; Qin Wang; Li Yang. Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification. IEEE Transactions on Power Systems 2020, 35, 1653 -1656.

AMA Style

Shengyuan Liu, Renyun Jin, Haifeng Qiu, Xueyuan Cui, Zhenzhi Lin, Zikuan Lian, Zhian Lin, Fushuan Wen, Yi Ding, Qin Wang, Li Yang. Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification. IEEE Transactions on Power Systems. 2020; 35 (2):1653-1656.

Chicago/Turabian Style

Shengyuan Liu; Renyun Jin; Haifeng Qiu; Xueyuan Cui; Zhenzhi Lin; Zikuan Lian; Zhian Lin; Fushuan Wen; Yi Ding; Qin Wang; Li Yang. 2020. "Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification." IEEE Transactions on Power Systems 35, no. 2: 1653-1656.

Journal article
Published: 26 September 2019 in Energies
Reads 0
Downloads 0

With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits of power utilities. First, seven indexes for assessing operation quality of smart meters are defined based on the metering data and the Gaussian mixture model (GMM) clustering algorithm is applied to extract the typical index data from the massive data of smart meters. Then, the combination optimization model of index’s weight is presented with the subject experience of experts and object difference of data considered; and the comprehensive assessment algorithm based on the revised technique for order preference by similarity to an ideal solution (TOPSIS) is proposed to evaluate the operation quality of smart meters. Finally, the proposed data-driven assessment algorithm is illustrated by the actual metering data from Zhejiang Ningbo power supply company of China and practical application is briefly introduced. The results show that the proposed algorithm is effective for assessing the operation quality of smart meters and could be helpful for energy measurement and asset management.

ACS Style

Liu; Fangbin Ye; Lin; Jia Yang; Haiwei Xie; Ye; Yang; Xie; Shengyuan Liu; Zhenzhi Lin; Haigang Liu; Yinghe Lin. Comprehensive Quality Assessment Algorithm for Smart Meters. Energies 2019, 12, 3690 .

AMA Style

Liu, Fangbin Ye, Lin, Jia Yang, Haiwei Xie, Ye, Yang, Xie, Shengyuan Liu, Zhenzhi Lin, Haigang Liu, Yinghe Lin. Comprehensive Quality Assessment Algorithm for Smart Meters. Energies. 2019; 12 (19):3690.

Chicago/Turabian Style

Liu; Fangbin Ye; Lin; Jia Yang; Haiwei Xie; Ye; Yang; Xie; Shengyuan Liu; Zhenzhi Lin; Haigang Liu; Yinghe Lin. 2019. "Comprehensive Quality Assessment Algorithm for Smart Meters." Energies 12, no. 19: 3690.

Journal article
Published: 16 September 2019 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. The results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

ACS Style

Shengyuan Liu; Yuxuan Zhao; Zhenzhi Lin; Yilu Liu; Yi Ding; Li Yang; Shimin Yi. Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor. IEEE Transactions on Smart Grid 2019, 11, 1630 -1643.

AMA Style

Shengyuan Liu, Yuxuan Zhao, Zhenzhi Lin, Yilu Liu, Yi Ding, Li Yang, Shimin Yi. Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor. IEEE Transactions on Smart Grid. 2019; 11 (2):1630-1643.

Chicago/Turabian Style

Shengyuan Liu; Yuxuan Zhao; Zhenzhi Lin; Yilu Liu; Yi Ding; Li Yang; Shimin Yi. 2019. "Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor." IEEE Transactions on Smart Grid 11, no. 2: 1630-1643.

Journal article
Published: 19 April 2019 in IEEE Transactions on Power Delivery
Reads 0
Downloads 0

Consumers' transformers play an important role in power systems, and they are essential for operation reliability and commercial benefits. In the past, maintenance personnel had to spend plenty of time on examining consumers' transformers one by one. Nowadays, with the wide deployment of power user electric energy data acquire system (PUEEDAS), informative metering data are becoming available, which can be utilized for further condition monitoring. Thus, this paper proposes a data-driven abnormal condition monitoring algorithm of data acquisition for consumers' transformers, which could send abnormal condition alerts to operators and maintenance personnel timely. In the proposed algorithm, Spearman's rank correlation coefficient is utilized to show the degree of correlation among phase currents, and its t-statistics is used to determine whether abnormal condition of data acquisition exists based on the hypothesis testing. Finally, actual acquisition data from Zhejiang power system in China are employed to validate the effectiveness of the proposed algorithm, and to analyze the characteristics of normal and abnormal conditions respectively. Sensitive analyses on different significant levels and sampling rates are performed for considering its impact on monitoring results; and the application in real power systems is also given to demonstrate the practicality of the proposed algorithm.

ACS Style

Shengyuan Liu; Yuxuan Zhao; Zhenzhi Lin; Yi Ding; Yong Yan; Li Yang; Qin Wang; Hao Zhou; Hongwei Wu. Data-Driven Condition Monitoring of Data Acquisition for Consumers’ Transformers in Actual Distribution Systems Using t-Statistics. IEEE Transactions on Power Delivery 2019, 34, 1578 -1587.

AMA Style

Shengyuan Liu, Yuxuan Zhao, Zhenzhi Lin, Yi Ding, Yong Yan, Li Yang, Qin Wang, Hao Zhou, Hongwei Wu. Data-Driven Condition Monitoring of Data Acquisition for Consumers’ Transformers in Actual Distribution Systems Using t-Statistics. IEEE Transactions on Power Delivery. 2019; 34 (4):1578-1587.

Chicago/Turabian Style

Shengyuan Liu; Yuxuan Zhao; Zhenzhi Lin; Yi Ding; Yong Yan; Li Yang; Qin Wang; Hao Zhou; Hongwei Wu. 2019. "Data-Driven Condition Monitoring of Data Acquisition for Consumers’ Transformers in Actual Distribution Systems Using t-Statistics." IEEE Transactions on Power Delivery 34, no. 4: 1578-1587.

Journal article
Published: 01 February 2019 in Journal of Energy Engineering
Reads 0
Downloads 0

Coherency identification among generators plays an important role in controlled islanding and wide-area monitoring of a power system with renewable energy sources. So far, wide-area measurement systems (WAMS) have been deployed widely in power plants and key substations, so the trajectories measured by phasor measurement units (PMUs) and WAMS could be utilized to identify coherent generators (CGs). In this study, an algorithm for coherency identification among generators based on the fuzzy equivalence relation (FER) clustering method and synthesized weight is proposed. First, 10 indices for measuring the similarity of trajectories are presented. Then, the technique for order preference by similarity to ideal solution (TOPSIS) and a method based on entropy and multicorrelation coefficients is presented to determine the weight of each index for decision making of trajectory similarities. Next, the FER clustering method is proposed to cluster the trajectories and the F-statistics, which could measure the rationality of clustering results, is presented to determine the optimal cluster number of coherent groups among generators. Finally, the actual Guangdong power system in China, a revised 16-unit 68-bus power system, and the simplified China South Power Grid (CSPG) are used to demonstrate the effectiveness of the proposed methodology. Comparisons with some existing methods are performed and the impacts of integrated renewable energy generation sources on power system oscillations are also investigated.

ACS Style

Shengyuan Liu; Zhenzhi Lin; Fushuan Wen; Li Yang; Yi Ding; Yusheng Xue; Shimin Yi; Yong Yan. Fuzzy Equivalence Relation Clustering-Based Algorithm for Coherency Identification among Generators. Journal of Energy Engineering 2019, 145, 04018070 .

AMA Style

Shengyuan Liu, Zhenzhi Lin, Fushuan Wen, Li Yang, Yi Ding, Yusheng Xue, Shimin Yi, Yong Yan. Fuzzy Equivalence Relation Clustering-Based Algorithm for Coherency Identification among Generators. Journal of Energy Engineering. 2019; 145 (1):04018070.

Chicago/Turabian Style

Shengyuan Liu; Zhenzhi Lin; Fushuan Wen; Li Yang; Yi Ding; Yusheng Xue; Shimin Yi; Yong Yan. 2019. "Fuzzy Equivalence Relation Clustering-Based Algorithm for Coherency Identification among Generators." Journal of Energy Engineering 145, no. 1: 04018070.

Conference paper
Published: 05 November 2018 in IOP Conference Series: Earth and Environmental Science
Reads 0
Downloads 0

Substation siting and sizing planning is one of the important contents of distribution network planning, which directly affects the results of subsequent distribution network planning, and affects the quality of power supply and the economy of power grid operation. Given this background, a deep learning algorithm for preliminary siting of substations in distribution network planning is proposed in this work. Features related to the principle of siting of substations are extracted and multichannel data characterization are utilized. Then, the features are integrated into a convolutional neural network (CNN), which is one of deep learning algorithms, based on actual geographical relationships. Next, the preliminary siting of substations for the subsequent planning process is completed. Finally, the validity of the proposed algorithm considering different input features is demonstrated on a distribution network of one certain province in China by case studies and comparisons. The simulation results show that the proposed deep learning algorithm for preliminary siting of substations is more accurate with more input features, and is better than shallow learning algorithms, thus can be employed to preliminary siting of substations in distribution network planning.

ACS Style

Liang Feng; Can Cui; Runze Ma; Jian Wu; Yang Yang; Xiaolei Zhang; Shengyuan Liu. Deep Learning Algorithm for Preliminary Siting of Substations Considering Various Features in Distribution Network Planning. IOP Conference Series: Earth and Environmental Science 2018, 192, 012032 .

AMA Style

Liang Feng, Can Cui, Runze Ma, Jian Wu, Yang Yang, Xiaolei Zhang, Shengyuan Liu. Deep Learning Algorithm for Preliminary Siting of Substations Considering Various Features in Distribution Network Planning. IOP Conference Series: Earth and Environmental Science. 2018; 192 (1):012032.

Chicago/Turabian Style

Liang Feng; Can Cui; Runze Ma; Jian Wu; Yang Yang; Xiaolei Zhang; Shengyuan Liu. 2018. "Deep Learning Algorithm for Preliminary Siting of Substations Considering Various Features in Distribution Network Planning." IOP Conference Series: Earth and Environmental Science 192, no. 1: 012032.

Journal article
Published: 01 November 2018 in Energies
Reads 0
Downloads 0

Transient stability after islanding is of crucial importance because a controlled islanding strategy is not feasible if transient stability cannot be maintained in the islands created. A new indicator of transient stability for controlled islanding strategies, defined as the critical islanding time (CIT), is presented for slow coherency-based controlled islanding strategies to determine whether all the islands created are transiently stable. Then, the stable islanding interval (SII) is also defined to determine the appropriate time frame for stable islanding. Simulations were conducted on the New England test system–New York interconnected system to demonstrate the characteristics of the critical islanding time and stable islanding interval. Simulation results showed that the answer for when to island could be easily reflected by the proposed CIT and SII indicators. These two indicators are beneficial to power dispatchers to keep the power systems transiently stable and prevent widespread blackouts.

ACS Style

Zhenzhi Lin; Yuxuan Zhao; Shengyuan Liu; Fushuan Wen; Yi Ding; Li Yang; Chang Han; Hao Zhou; Hongwei Wu. A New Indicator of Transient Stability for Controlled Islanding of Power Systems: Critical Islanding Time. Energies 2018, 11, 2975 .

AMA Style

Zhenzhi Lin, Yuxuan Zhao, Shengyuan Liu, Fushuan Wen, Yi Ding, Li Yang, Chang Han, Hao Zhou, Hongwei Wu. A New Indicator of Transient Stability for Controlled Islanding of Power Systems: Critical Islanding Time. Energies. 2018; 11 (11):2975.

Chicago/Turabian Style

Zhenzhi Lin; Yuxuan Zhao; Shengyuan Liu; Fushuan Wen; Yi Ding; Li Yang; Chang Han; Hao Zhou; Hongwei Wu. 2018. "A New Indicator of Transient Stability for Controlled Islanding of Power Systems: Critical Islanding Time." Energies 11, no. 11: 2975.