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Yonghui Sun
Hohai University, Hohai University College of Energy and Electrical Engineering, 529404 Nanjing, Jiangsu, China

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Journal article
Published: 14 June 2021 in IEEE Transactions on Fuzzy Systems
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This paper focuses on the finite-time guaranteed cost (FTGC) control problem for interval type-2 (IT2) fuzzy semi-Markov switching systems (S-MSSs) with additive disturbances. Based on the IT2 fuzzy S-MSSs and the state feedback control method, a novel IT2 mode-dependent fuzzy GC controller is developed under the mismatched membership functions. Then, some new sufficient conditions are given to ensure that IT2 fuzzy S-MSSs are FT stochastically stable based on the designed IT2 mode-dependent fuzzy GC controller. Furthermore, the transition rates boundaries condition is relaxed, and a new incomplete transition rates boundaries model is constructed to reflect the transition rates information. With a view to both the incomplete transition rates boundaries and the actuator fault phenomenon, the improved stability criteria are provided to stabilize the IT2 fuzzy S-MSSs within a FT interval and determine the gains of the IT2 mode-dependent fuzzy GC controller, resulting in a much greater design flexibility. Distinct from the existing results, the presented FTGC control scheme is more suitable for many practical systems, because the IT2 fuzzy S-MSSs model which is more general compared with the traditional fuzzy system and S-MSSs is considered. Finally, three illustrative examples are offered to explain the effectiveness and feasibility of proposed FTGC control approach.

ACS Style

Linchuang Zhang; Yonghui Sun; Hak-Keung Lam; Hongyi Li; Jianxi Wang; Dongchen Hou. Guaranteed Cost Control for Interval Type-2 Fuzzy Semi-Markov Switching Systems within A Finite-Time Interval. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Linchuang Zhang, Yonghui Sun, Hak-Keung Lam, Hongyi Li, Jianxi Wang, Dongchen Hou. Guaranteed Cost Control for Interval Type-2 Fuzzy Semi-Markov Switching Systems within A Finite-Time Interval. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Linchuang Zhang; Yonghui Sun; Hak-Keung Lam; Hongyi Li; Jianxi Wang; Dongchen Hou. 2021. "Guaranteed Cost Control for Interval Type-2 Fuzzy Semi-Markov Switching Systems within A Finite-Time Interval." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Original research paper
Published: 21 January 2021 in IET Renewable Power Generation
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Owing to random load changes and transmission time delays in interconnected power systems with renewable energy, the load frequency control scheme has become one of the main methods to keep stability and security of power systems. To relieve communication burden and increase network utilisation, an adaptive event‐triggered scheme is explored. Then, a new fractional‐order global sliding mode control scheme comprising the fractional‐order term in the sliding surface is adopted to improve robustness of load frequency control. The fractional‐order term generates a new degree of freedom and more adjustable parameters to improve control performance. Furthermore, the Markov theory is applied in the modelling process to better describe the uncertainty of parameters and external disturbances. The stability and stabilisation criteria for multi‐area power systems load frequency control are put forward by employing the improved Lyapunov function and integral inequalities with auxiliary functions. Finally, two simulation examples containing a two‐area power system and modified IEEE 39‐bus New England test power system with three wind farms are presented to investigate the effectiveness of the proposed method.

ACS Style

Xinxin Lv; Yonghui Sun; Wenqiang Hu; Venkata Dinavahi. Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control. IET Renewable Power Generation 2021, 15, 1046 -1057.

AMA Style

Xinxin Lv, Yonghui Sun, Wenqiang Hu, Venkata Dinavahi. Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control. IET Renewable Power Generation. 2021; 15 (5):1046-1057.

Chicago/Turabian Style

Xinxin Lv; Yonghui Sun; Wenqiang Hu; Venkata Dinavahi. 2021. "Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control." IET Renewable Power Generation 15, no. 5: 1046-1057.

Research article
Published: 15 October 2020 in International Journal of Robust and Nonlinear Control
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This article investigates the event‐triggered (ET) states feedback robust control problem for a class of continuous‐time networked semi‐Markov jump systems (S‐MJSs). An ET scheme, which depends on semi‐Markov process, is presented to design a suitable controller and save communication resources. To cope with the network transmission delay phenomenon, a time‐delay S‐MJSs model under the ET scheme is introduced to describe this phenomenon. Then, it is assumed that the communication links between event detector and zero‐order holder are imperfect, where the signal quantization and the actuator fault occur simultaneously. The sufficient conditions are derived by means of linear matrix inequalities approach, which guarantees the stochastic stability of the constructed time‐delay S‐MJSs in an optimized performance level. Based on these criteria, the parameters of controller under the ET scheme are readily calculated. Some simulation results with respect to F‐404 aircraft engine system for two kinds of ET parameters are given to validate the proposed method.

ACS Style

Linchuang Zhang; Yonghui Sun; Yingnan Pan; Dongchen Hou; Sen Wang. Network‐based robust event‐triggered control for continuous‐time uncertain semi‐Markov jump systems. International Journal of Robust and Nonlinear Control 2020, 31, 306 -323.

AMA Style

Linchuang Zhang, Yonghui Sun, Yingnan Pan, Dongchen Hou, Sen Wang. Network‐based robust event‐triggered control for continuous‐time uncertain semi‐Markov jump systems. International Journal of Robust and Nonlinear Control. 2020; 31 (1):306-323.

Chicago/Turabian Style

Linchuang Zhang; Yonghui Sun; Yingnan Pan; Dongchen Hou; Sen Wang. 2020. "Network‐based robust event‐triggered control for continuous‐time uncertain semi‐Markov jump systems." International Journal of Robust and Nonlinear Control 31, no. 1: 306-323.

Research article
Published: 18 September 2020 in IET Generation, Transmission & Distribution
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In this study, a new method is put forward for the stability and stabilisation analysis of the event-triggered load frequency control (LFC) with interval time-varying delays, considering the global sliding mode controller. To lighten the network bandwidth and save more limited networked resources, the event-triggered scheme is optimised through quantum genetic algorithm, according to different circumstances. Additionally, global sliding mode control (GSMC) scheme is proposed to provide stronger robustness performance, which against the frequency deviation caused by power unbalance or transmission time delays better. Based on the proposed schemes, multi-area LFC for the power system model is formulated as a Markov jump linear system model, considering transmission time delays and external disturbances. By applying improved Lyapunov stability theory, criteria about the stability and stabilisation conditions for multi-area power system can be deduced in terms of linear matrix inequality. Finally, to validate a more realistic LFC application, the proposed event-triggered GSMC is also deployed on Kundur's two-area test system. Simulation studies are carried out to illustrate the effectiveness and superiority of the developed schemes.

ACS Style

Xinxin Lv; Yonghui Sun; Shiqi Cao; Venkata Dinavahi. Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach. IET Generation, Transmission & Distribution 2020, 14, 4878 -4887.

AMA Style

Xinxin Lv, Yonghui Sun, Shiqi Cao, Venkata Dinavahi. Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach. IET Generation, Transmission & Distribution. 2020; 14 (21):4878-4887.

Chicago/Turabian Style

Xinxin Lv; Yonghui Sun; Shiqi Cao; Venkata Dinavahi. 2020. "Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach." IET Generation, Transmission & Distribution 14, no. 21: 4878-4887.

Editorial
Published: 10 September 2020 in Energies
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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

ACS Style

Denis Sidorov; Fang Liu; Yonghui Sun. Machine Learning for Energy Systems. Energies 2020, 13, 4708 .

AMA Style

Denis Sidorov, Fang Liu, Yonghui Sun. Machine Learning for Energy Systems. Energies. 2020; 13 (18):4708.

Chicago/Turabian Style

Denis Sidorov; Fang Liu; Yonghui Sun. 2020. "Machine Learning for Energy Systems." Energies 13, no. 18: 4708.

Journal article
Published: 01 September 2020 in Energies
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In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

ACS Style

Kai Chen; Rabea Jamil Mahfoud; Yonghui Sun; Dongliang Nan; Kaike Wang; Hassan Haes Alhelou; Pierluigi Siano. Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM. Energies 2020, 13, 4522 .

AMA Style

Kai Chen, Rabea Jamil Mahfoud, Yonghui Sun, Dongliang Nan, Kaike Wang, Hassan Haes Alhelou, Pierluigi Siano. Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM. Energies. 2020; 13 (17):4522.

Chicago/Turabian Style

Kai Chen; Rabea Jamil Mahfoud; Yonghui Sun; Dongliang Nan; Kaike Wang; Hassan Haes Alhelou; Pierluigi Siano. 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM." Energies 13, no. 17: 4522.

Journal article
Published: 06 May 2020 in IEEE Access
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The robust stability and stabilization of adaptive event-triggered load frequency control (LFC) with sliding mode control (SMC) for multi-area power systems under a networked environment are investigated in this paper. The adaptive event-triggered scheme is proposed to maximize network bandwidth utilization, and it can be adaptively modified according to circumstances. Furthermore, to provide stronger robustness performance which is against the frequency deviation induced by power unbalance or transmission time delays, the SMC is developed. Then, the LFC scheme for multi-area power systems under the networked environment is modeled as a Markov jump linear system model, to describe the uncertainty parameters and external disturbances better in this system. Additionally, by employing Wirtinger-based inequality and Lyapunov theory, the robust stability and stabilization criteria with less conservatism are derived. Finally, simulations are performed to demonstrate the efficacy and superiority of the developed approach.

ACS Style

Xinxin Lv; Yonghui Sun; Yi Wang; Venkata Dinavahi. Adaptive Event-Triggered Load Frequency Control of Multi-Area Power Systems Under Networked Environment via Sliding Mode Control. IEEE Access 2020, 8, 86585 -86594.

AMA Style

Xinxin Lv, Yonghui Sun, Yi Wang, Venkata Dinavahi. Adaptive Event-Triggered Load Frequency Control of Multi-Area Power Systems Under Networked Environment via Sliding Mode Control. IEEE Access. 2020; 8 (99):86585-86594.

Chicago/Turabian Style

Xinxin Lv; Yonghui Sun; Yi Wang; Venkata Dinavahi. 2020. "Adaptive Event-Triggered Load Frequency Control of Multi-Area Power Systems Under Networked Environment via Sliding Mode Control." IEEE Access 8, no. 99: 86585-86594.

Journal article
Published: 19 February 2020 in Applied Sciences
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In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

ACS Style

Rabea Jamil Mahfoud; Nizar Faisal Alkayem; Yonghui Sun; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente. Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems. Applied Sciences 2020, 10, 1384 .

AMA Style

Rabea Jamil Mahfoud, Nizar Faisal Alkayem, Yonghui Sun, Hassan Haes Alhelou, Pierluigi Siano, Mimmo Parente. Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems. Applied Sciences. 2020; 10 (4):1384.

Chicago/Turabian Style

Rabea Jamil Mahfoud; Nizar Faisal Alkayem; Yonghui Sun; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente. 2020. "Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems." Applied Sciences 10, no. 4: 1384.

Journal article
Published: 06 February 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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In this article, by using distributed consensus algorithm, the power compensation problem of network losses in a microgrid is considered with battery energy storage systems (BESSs). At first, the battery storage unit (BSU) is assembled at each node to maintain the power supply demand balance of the microgrid, then the power constraint is considered, which demands each BSU must run between its upper and lower bounds, otherwise, the upper or lower bounds will be used to replace the real power of the BSU. In order to keep the power balance of microgrid when network losses exist, a novel distributed consensus algorithm is proposed to compensate the missing power through the discharging process of BSUs, and the state of charge (SOC) of BESS is also considered. Besides, the case of battery faults is also considered, where the minimum layout point constraint is presented to ensure the completely compensation of the whole transmission losses. Finally, the test results of IEEE 14-bus and IEEE 57-bus microgrid systems are provided to verify the effectiveness and validity of the proposed method, respectively.

ACS Style

Yonghui Sun; Xiaopeng Wu; Jianxi Wang; Dongchen Hou; Sen Wang. Power Compensation of Network Losses in a Microgrid With BESS by Distributed Consensus Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, 51, 2091 -2100.

AMA Style

Yonghui Sun, Xiaopeng Wu, Jianxi Wang, Dongchen Hou, Sen Wang. Power Compensation of Network Losses in a Microgrid With BESS by Distributed Consensus Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; 51 (4):2091-2100.

Chicago/Turabian Style

Yonghui Sun; Xiaopeng Wu; Jianxi Wang; Dongchen Hou; Sen Wang. 2020. "Power Compensation of Network Losses in a Microgrid With BESS by Distributed Consensus Algorithm." IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 4: 2091-2100.

Journal article
Published: 23 December 2019 in Energies
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The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

ACS Style

Sen Wang; Yonghui Sun; Yan Zhou; Rabea Jamil Mahfoud; Dongchen Hou. A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. Energies 2019, 13, 87 .

AMA Style

Sen Wang, Yonghui Sun, Yan Zhou, Rabea Jamil Mahfoud, Dongchen Hou. A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. Energies. 2019; 13 (1):87.

Chicago/Turabian Style

Sen Wang; Yonghui Sun; Yan Zhou; Rabea Jamil Mahfoud; Dongchen Hou. 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM." Energies 13, no. 1: 87.

Research article
Published: 13 November 2019 in IET Generation, Transmission & Distribution
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In this study, a distributed hierarchical consensus algorithm is proposed to solve the economic dispatch problem in smart grid. In the proposed hierarchical control strategy, the upper level is the leader level, which contains the feedback elements of power difference mismatch and plays a dominant role in the whole algorithm. The lower level is the follower level, which receives information from the leader level and realises self-adjustment based on the received information. Besides, the constraint conditions of generator nodes are considered to ensure that all of the generator nodes could operate within their limitations. With the increase of the number of leader nodes, the high convergence rate could be illustrated by the comparison between single-leader node and multi-leader nodes. Finally, simulation results based on 5-node network topology and IEEE-57 bus systems are presented to verify the validity of the proposed algorithm, respectively.

ACS Style

Xiaopeng Wu; Yonghui Sun; Zhinong Wei; Guoqiang Sun. Distributed hierarchical consensus algorithm for economic dispatch in smart grid. IET Generation, Transmission & Distribution 2019, 13, 5541 -5549.

AMA Style

Xiaopeng Wu, Yonghui Sun, Zhinong Wei, Guoqiang Sun. Distributed hierarchical consensus algorithm for economic dispatch in smart grid. IET Generation, Transmission & Distribution. 2019; 13 (24):5541-5549.

Chicago/Turabian Style

Xiaopeng Wu; Yonghui Sun; Zhinong Wei; Guoqiang Sun. 2019. "Distributed hierarchical consensus algorithm for economic dispatch in smart grid." IET Generation, Transmission & Distribution 13, no. 24: 5541-5549.

Journal article
Published: 28 October 2019 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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ACS Style

Hongjing Liang; Linchuang Zhang; Yonghui Sun; Tingwen Huang. Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2019, 51, 3889 -3899.

AMA Style

Hongjing Liang, Linchuang Zhang, Yonghui Sun, Tingwen Huang. Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019; 51 (6):3889-3899.

Chicago/Turabian Style

Hongjing Liang; Linchuang Zhang; Yonghui Sun; Tingwen Huang. 2019. "Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies." IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 6: 3889-3899.

Research article
Published: 09 September 2019 in Wind Energy
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Considering the inevitable prediction errors in the traditional point predictions of wind power, in this paper, a new ultra short‐term probability prediction method for wind power is proposed, in which the long short‐term memory (LSTM) network, wavelet decomposition (WT), and principal component analysis (PCA) are combined together for ultra short‐term probability prediction of wind power, a conditional normal distribution model that is developed to describe the uncertainty of prediction errors. First, WT and PCA are jointly used to smooth the original time series, then the point prediction model for subsequence data based on LSTM network is proposed. It is worth pointing out that the input matrix of the model includes many features, such as wind power and wind speed, which will be helpful for improving prediction performance. After optimizing the index of the ultra short‐term probability prediction interval (PI) of wind power by particle swarm optimization (PSO), the conditional normal distribution model of prediction errors is developed. Thus, the ultra short‐term PIs for wind power are obtained. Finally, based on the data of two wind farms in China, simulation results are provided to illustrate the usefulness of the proposed prediction model. It follows from those results that the proposed method can improve the accuracy of prediction, and the reliability of probability prediction for wind power is also improved.

ACS Style

Yonghui Sun; Peng Wang; Suwei Zhai; Dongchen Hou; Sen Wang; Yan Zhou. Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution. Wind Energy 2019, 23, 63 -76.

AMA Style

Yonghui Sun, Peng Wang, Suwei Zhai, Dongchen Hou, Sen Wang, Yan Zhou. Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution. Wind Energy. 2019; 23 (1):63-76.

Chicago/Turabian Style

Yonghui Sun; Peng Wang; Suwei Zhai; Dongchen Hou; Sen Wang; Yan Zhou. 2019. "Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution." Wind Energy 23, no. 1: 63-76.

Journal article
Published: 20 August 2019 in IEEE Transactions on Fuzzy Systems
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This paper studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. Firstly, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Secondly, the IT2 fuzzy semi-Markov modedependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with H1 performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system which is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.

ACS Style

Linchuang Zhang; Hak-Keung Lam; Yonghui Sun; Hongjing Liang. Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach. IEEE Transactions on Fuzzy Systems 2019, 28, 2375 -2388.

AMA Style

Linchuang Zhang, Hak-Keung Lam, Yonghui Sun, Hongjing Liang. Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach. IEEE Transactions on Fuzzy Systems. 2019; 28 (10):2375-2388.

Chicago/Turabian Style

Linchuang Zhang; Hak-Keung Lam; Yonghui Sun; Hongjing Liang. 2019. "Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach." IEEE Transactions on Fuzzy Systems 28, no. 10: 2375-2388.

Journal article
Published: 19 August 2019 in IEEE Transactions on Power Systems
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Accurate forecasting-aided state estimation plays a vital role in reliable and secure operation of power systems. However, most of existing methods are unable to deal with the uncertainties that might be caused by uncertain model parameters or uncertain noise statistics. Therefore, the performance of these methods may be inevitably degraded significantly. To address these issues, based on the robust control theory, in this paper, by incorporating the modified innovation based Sage-Husa estimator of noise statistics and the proposed estimation error covariance matrix adaptive technique, a novel adaptive $H_{\infty}$ extended Kalman filter(AHEKF) is developed to realize robust forecasting-aided state estimation for power system with model uncertainties. Extensive simulations carried out on several different test systems demonstrate the efficiency and robustness of the proposed method.

ACS Style

Yi Wang; Yonghui Sun; Venkata Dinavahi. Robust Forecasting-Aided State Estimation for Power System Against Uncertainties. IEEE Transactions on Power Systems 2019, 35, 691 -702.

AMA Style

Yi Wang, Yonghui Sun, Venkata Dinavahi. Robust Forecasting-Aided State Estimation for Power System Against Uncertainties. IEEE Transactions on Power Systems. 2019; 35 (1):691-702.

Chicago/Turabian Style

Yi Wang; Yonghui Sun; Venkata Dinavahi. 2019. "Robust Forecasting-Aided State Estimation for Power System Against Uncertainties." IEEE Transactions on Power Systems 35, no. 1: 691-702.

Journal article
Published: 17 August 2019 in Applied Sciences
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In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

ACS Style

Rabea Jamil Mahfoud; Yonghui Sun; Nizar Faisal Alkayem; Hassan Haes Alhelou; Pierluigi Siano; Miadreza Shafie-Khah. A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems. Applied Sciences 2019, 9, 3394 .

AMA Style

Rabea Jamil Mahfoud, Yonghui Sun, Nizar Faisal Alkayem, Hassan Haes Alhelou, Pierluigi Siano, Miadreza Shafie-Khah. A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems. Applied Sciences. 2019; 9 (16):3394.

Chicago/Turabian Style

Rabea Jamil Mahfoud; Yonghui Sun; Nizar Faisal Alkayem; Hassan Haes Alhelou; Pierluigi Siano; Miadreza Shafie-Khah. 2019. "A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems." Applied Sciences 9, no. 16: 3394.

Conference paper
Published: 08 August 2019 in International Conference on Communication, Computing and Electronics Systems
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A comparative study on the problem of optimal capacitor allocation and sizing in unbalanced radial distribution systems (URDSs) is presented in this paper. The comparison is performed between three different sensitivity-based indices, which are utilized for selecting the optimal locations of the three-phase capacitors. Those indices are: index vector (IV), combined loss sensitivity factor (CLSF) and voltage stability index (VSI). For the comparison purpose, the capacitors’ sizes are obtained by implementing particle swarm optimization algorithm (PSO). Two main aspects are considered in this comparison: (i) the number of locations for capacitors to be installed at, and (ii) the type of the three-phase capacitors (balanced or unbalanced three-phase capacitors). The optimization problem is basically a minimization of three objective functions, namely: power loss, voltage deviation and voltage unbalancing. The comparative analysis is executed on the 25 bus unbalanced system and then simulation results are discussed and evaluated.

ACS Style

Rabea Jamil Mahfoud; Yongjie Zhong; Nizar Faisal Alkayem; Yonghui Sun. Optimal Allocation and Sizing of Shunt Capacitors in Unbalanced Radial Distribution Systems: A Comparative Study. International Conference on Communication, Computing and Electronics Systems 2019, 641 -652.

AMA Style

Rabea Jamil Mahfoud, Yongjie Zhong, Nizar Faisal Alkayem, Yonghui Sun. Optimal Allocation and Sizing of Shunt Capacitors in Unbalanced Radial Distribution Systems: A Comparative Study. International Conference on Communication, Computing and Electronics Systems. 2019; ():641-652.

Chicago/Turabian Style

Rabea Jamil Mahfoud; Yongjie Zhong; Nizar Faisal Alkayem; Yonghui Sun. 2019. "Optimal Allocation and Sizing of Shunt Capacitors in Unbalanced Radial Distribution Systems: A Comparative Study." International Conference on Communication, Computing and Electronics Systems , no. : 641-652.

Journal article
Published: 31 July 2019 in IEEE Access
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This paper develops an adaptive robust cubature Kalman filter (ARCKF) that is able to mitigate the adverse effects of the innovation and observation outliers while filtering out the system and measurement noises. To develop the ARCKF dynamic state estimator, a batch-mode regression form in the framework of cubature Kalman filter is first established by processing the predicted state and measurement data information simultaneously. Subsequently, based on the regression form, the outliers can be detected and downweighted by the robust projection statistics approach. Then, the adverse effects of innovation and observation outliers can be effectively suppressed by the generalized maximum likelihood (GM)-type estimator utilizing the iteratively reweighted least squares approach. Finally, an adaptive strategy is developed to adjust the state estimation error covariance matrix under different conditions. Extensive simulation results obtained from the IEEE New England 10-machine 39-bus test system under various operating conditions demonstrate the effectiveness and robustness of the proposed method, which is able to track the transients of power system in a more reliable way than the conventional cubature Kalman filter (CKF) and the unscented Kalman filter (UKF).

ACS Style

Yi Wang; Yonghui Sun; Venkata Dinavahi; Shiqi Cao; Dongchen Hou. Adaptive Robust Cubature Kalman Filter for Power System Dynamic State Estimation Against Outliers. IEEE Access 2019, 7, 105872 -105881.

AMA Style

Yi Wang, Yonghui Sun, Venkata Dinavahi, Shiqi Cao, Dongchen Hou. Adaptive Robust Cubature Kalman Filter for Power System Dynamic State Estimation Against Outliers. IEEE Access. 2019; 7 ():105872-105881.

Chicago/Turabian Style

Yi Wang; Yonghui Sun; Venkata Dinavahi; Shiqi Cao; Dongchen Hou. 2019. "Adaptive Robust Cubature Kalman Filter for Power System Dynamic State Estimation Against Outliers." IEEE Access 7, no. : 105872-105881.

Research article
Published: 16 May 2019 in IET Generation, Transmission & Distribution
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This study considers the dynamic state estimation of power systems with model uncertainties that might be caused by the unknown noise statistics or unpredicted changes to the model parameters. To deal with these issues, an innovation-based estimator that is able to dynamically revise the statistics of system and measurement noise is proposed firstly. Then, based on the criteria for bounding the adverse influences on the estimation error of model uncertainties and unscented transform technique, an adaptive strategy is developed to adjust the estimation error covariance matrix under various conditions. Finally, by incorporating the proposed approaches and filter theory, a novel adaptive unscented filter is established to realise dynamic state estimation of power system against model uncertainties. Extensive simulation results obtained from the IEEE-39 bus test system are presented to illustrate the effectiveness and robustness of the proposed method.

ACS Style

Yi Wang; Yonghui Sun; Venkata Dinavahi; Kaike Wang; Dongliang Nan. Robust dynamic state estimation of power systems with model uncertainties based on adaptive unscented filter. IET Generation, Transmission & Distribution 2019, 13, 2455 -2463.

AMA Style

Yi Wang, Yonghui Sun, Venkata Dinavahi, Kaike Wang, Dongliang Nan. Robust dynamic state estimation of power systems with model uncertainties based on adaptive unscented filter. IET Generation, Transmission & Distribution. 2019; 13 (12):2455-2463.

Chicago/Turabian Style

Yi Wang; Yonghui Sun; Venkata Dinavahi; Kaike Wang; Dongliang Nan. 2019. "Robust dynamic state estimation of power systems with model uncertainties based on adaptive unscented filter." IET Generation, Transmission & Distribution 13, no. 12: 2455-2463.

Journal article
Published: 09 May 2019 in Electric Power Systems Research
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The active power imbalance caused by renewable energies (REs) and loads is usually viewed as a main reason for the grid frequency variation. Private electric vehicles (EVs) participating in integrated power system frequency regulation of REs has great realistic significance, the bidirectional wireless power transfer system for EVs is analyzed in this paper, a fuzzy adaptive PI controller is proposed to achieve the charge and discharge control of EVs at any power value. Then a Markov model with adaptive Markov transition probability is developed to analyze the stochastic distribution of EVs. Considering the basic travel demands of the owners and the EV battery state of charge (SOC), a new advanced frequency regulation strategy is further proposed, and the regulation ability of EVs is quantitatively described. Finally, the effectiveness of the proposed frequency regulation strategy is verified by simulation results.

ACS Style

Yonghui Sun; Suwei Zhai; Hantao Cui; Dongliang Nan; Kaike Wang. Frequency regulation strategy for private EVs participating in integrated power system of REs considering adaptive Markov transition probability. Electric Power Systems Research 2019, 173, 291 -301.

AMA Style

Yonghui Sun, Suwei Zhai, Hantao Cui, Dongliang Nan, Kaike Wang. Frequency regulation strategy for private EVs participating in integrated power system of REs considering adaptive Markov transition probability. Electric Power Systems Research. 2019; 173 ():291-301.

Chicago/Turabian Style

Yonghui Sun; Suwei Zhai; Hantao Cui; Dongliang Nan; Kaike Wang. 2019. "Frequency regulation strategy for private EVs participating in integrated power system of REs considering adaptive Markov transition probability." Electric Power Systems Research 173, no. : 291-301.