The Experts below are selected from a list of 105129 Experts worldwide ranked by ideXlab platform
Hongming Shen - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Optimisation of short-term frequency stability controls for a power system with renewables and multi-infeed HVDCs
IET Renewable Power Generation, 2018Co-Authors: Luping Wang, Xiaorong Xie, Xiaoliang Dong, Ying Liu, Hongming ShenAbstract:Short-term frequency stability (STFS) is becoming a great concern for a regional receiving-end power system such as the East China Power Grid (ECPG) mainly for two reasons: (i) the increasing percentage of power injected from multi-infeed (ultra-) high-voltage DCs (HVDCs) with the skyrocketed capacities; (ii) the replacement of traditional generation by the continuously growing inertia-less renewables. However, the existing emergency control is not adaptive enough to maintain STFS under varying operation conditions. In this study, a Real-Time optimised control is proposed to solve the issue more efficiently. It combines emergency power boosting (EPB) of HVDC and emergency demand response (EDR) in a coordinated way. The optimal control problem is formulated based on the online-updated models that reflect the current state of the system and the dynamic responses of multiple types of generators. By converting the non-linear constraint into a linear matrix inequality, the allocation of EPB and EDR can be optimised in a Real-Time and coordinated way. The performance of the proposed control and its advantages over the existing one are verified by simulation studies on the model of ECPG with low, normal and high penetrations of renewables.
Luping Wang - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Optimisation of emergency demand response and HVDC power modulation to improve short-term frequency stability of the receiving-end power systems
Institution of Engineering and Technology, 2019Co-Authors: Luping Wang, Xiaorong Xie, Long Peng, Yuan ZhaoAbstract:In recent years, large-capacity high-voltage direct currents (HVDCs) have been employed to transfer electricity from the west to the east of China. However, the frequent frequency drops due to HVDC blockings seriously threaten the short-term frequency stability of receiving-end power systems. The existing emergency frequency control strategy is not adaptive to the varying operation conditions and may cause excessive or deficient actions, which could result in inefficient control or high risk of frequency instability. Therefore, a Real-Time coordinated control strategy is proposed in this study based on the online-updated frequency response model. The model is designed to incorporate multiple types of generators and to reflect the dynamic frequency response of loads. The new control strategy combines multiple control resources, including the emergency demand response and HVDC power modulation, to improve the short-term frequency stability after HVDC failures. By online data preparation and cubic fitting, the nadir of the frequency is expressed as an analytic function of the control variables. A Real-Time Optimisation of emergency controls is achieved to improve the short-term frequency dynamics. Case studies show that the proposed scheme is robust to the varying operation conditions and has lower control cost than the existing control strategy
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Real-Time Optimisation of short-term frequency stability controls for a power system with renewables and multi-infeed HVDCs
IET Renewable Power Generation, 2018Co-Authors: Luping Wang, Xiaorong Xie, Xiaoliang Dong, Ying Liu, Hongming ShenAbstract:Short-term frequency stability (STFS) is becoming a great concern for a regional receiving-end power system such as the East China Power Grid (ECPG) mainly for two reasons: (i) the increasing percentage of power injected from multi-infeed (ultra-) high-voltage DCs (HVDCs) with the skyrocketed capacities; (ii) the replacement of traditional generation by the continuously growing inertia-less renewables. However, the existing emergency control is not adaptive enough to maintain STFS under varying operation conditions. In this study, a Real-Time optimised control is proposed to solve the issue more efficiently. It combines emergency power boosting (EPB) of HVDC and emergency demand response (EDR) in a coordinated way. The optimal control problem is formulated based on the online-updated models that reflect the current state of the system and the dynamic responses of multiple types of generators. By converting the non-linear constraint into a linear matrix inequality, the allocation of EPB and EDR can be optimised in a Real-Time and coordinated way. The performance of the proposed control and its advantages over the existing one are verified by simulation studies on the model of ECPG with low, normal and high penetrations of renewables.
Trung Q. Duong - One of the best experts on this subject based on the ideXlab platform.
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Spectrum-Sharing UAV-Assisted Mission-Critical Communication: Learning-Aided Real-Time Optimisation
IEEE Access, 2021Co-Authors: Minh-hien T. Nguyen, Long D. Nguyen, Emiliano Garcia-palacios, Tan Do-duy, Son T. Mai, Trung Q. DuongAbstract:We propose an unmanned aerial vehicle (UAV) communications scheme with spectrum-sharing mechanism to provide mission-critical services such as disaster recovery and public safety. Specifically, the UAVs can serve as flying base stations to provide extended network coverage for the affected area under spectrum-sharing cognitive radio networks (CRNs). To cope with the effects of network destruction in a disaster, we propose a Real-Time Optimisation framework for resource allocation (e.g., power and number of UAVs) for CRNs assisted by UAV relays. The proposed Optimisation scheme aims at optimising the network throughput of primary and secondary networks under the stringent constraint of maximum tolerable interference impinged on the primary users. We also propose a deep neural network (DNN) model to significantly reduce the execution time under Real-Time solution of mixed-integer UAV deployment problems. For both primary and secondary networks, our Real-Time Optimisation algorithms impose low computational complexity, hence, have a low execution time in solving throughput Optimisation problems, which demonstrates the benefit of our approached proposed for spectrum-sharing UAV-assisted mission-critical services.
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GLOBECOM - Learning-Aided Realtime Performance Optimisation of Cognitive UAV-Assisted Disaster Communication
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Trung Q. Duong, Long D. Nguyen, Hoang Duong Tuan, Lajos HanzoAbstract:In this work, we propose efficient Optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time Optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced Optimisation techniques and training deep neural networks, our solutions become capable of supporting Real-Time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time Optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.
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IWCMC - Practical Optimisation of Path Planning and Completion Time of Data Collection for UAV-enabled Disaster Communications
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019Co-Authors: Trung Q. Duong, Long D. Nguyen, Loi Kim NguyenAbstract:In this work, we propose efficient Optimisation methods for embedded relay-assisted unmanned ariel vehicles (UAVs) in wireless sensor networks (WSNs) to cope with the hazardous effect of natural disaster. Particularly, by using advanced Optimisation techniques, our low-complexity procedures are suitable applied to internet-of-things (IoT) applications when the execution time is strictly governed in disaster scenarios. Our model considers Real-Time Optimisation in embedded UAV-WSN communication for tracking and gathering sensor data. Our algorithms are low computational complexity with fast deployment and low execution time for solving our problem in milliseconds. Numerical results are shown to demonstrate the benefit of our proposed approaches for UAV-WSN.
Xiaorong Xie - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Optimisation of emergency demand response and HVDC power modulation to improve short-term frequency stability of the receiving-end power systems
Institution of Engineering and Technology, 2019Co-Authors: Luping Wang, Xiaorong Xie, Long Peng, Yuan ZhaoAbstract:In recent years, large-capacity high-voltage direct currents (HVDCs) have been employed to transfer electricity from the west to the east of China. However, the frequent frequency drops due to HVDC blockings seriously threaten the short-term frequency stability of receiving-end power systems. The existing emergency frequency control strategy is not adaptive to the varying operation conditions and may cause excessive or deficient actions, which could result in inefficient control or high risk of frequency instability. Therefore, a Real-Time coordinated control strategy is proposed in this study based on the online-updated frequency response model. The model is designed to incorporate multiple types of generators and to reflect the dynamic frequency response of loads. The new control strategy combines multiple control resources, including the emergency demand response and HVDC power modulation, to improve the short-term frequency stability after HVDC failures. By online data preparation and cubic fitting, the nadir of the frequency is expressed as an analytic function of the control variables. A Real-Time Optimisation of emergency controls is achieved to improve the short-term frequency dynamics. Case studies show that the proposed scheme is robust to the varying operation conditions and has lower control cost than the existing control strategy
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Real-Time Optimisation of short-term frequency stability controls for a power system with renewables and multi-infeed HVDCs
IET Renewable Power Generation, 2018Co-Authors: Luping Wang, Xiaorong Xie, Xiaoliang Dong, Ying Liu, Hongming ShenAbstract:Short-term frequency stability (STFS) is becoming a great concern for a regional receiving-end power system such as the East China Power Grid (ECPG) mainly for two reasons: (i) the increasing percentage of power injected from multi-infeed (ultra-) high-voltage DCs (HVDCs) with the skyrocketed capacities; (ii) the replacement of traditional generation by the continuously growing inertia-less renewables. However, the existing emergency control is not adaptive enough to maintain STFS under varying operation conditions. In this study, a Real-Time optimised control is proposed to solve the issue more efficiently. It combines emergency power boosting (EPB) of HVDC and emergency demand response (EDR) in a coordinated way. The optimal control problem is formulated based on the online-updated models that reflect the current state of the system and the dynamic responses of multiple types of generators. By converting the non-linear constraint into a linear matrix inequality, the allocation of EPB and EDR can be optimised in a Real-Time and coordinated way. The performance of the proposed control and its advantages over the existing one are verified by simulation studies on the model of ECPG with low, normal and high penetrations of renewables.
Richard Koech - One of the best experts on this subject based on the ideXlab platform.
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A Real-Time Optimisation system for automation of furrow irrigation
Irrigation Science, 2014Co-Authors: Richard Koech, R. J. Smith, M. H. GilliesAbstract:An automated Real-Time Optimisation system for furrow irrigation was developed and tested in this study. The system estimates the soil infiltration characteristics in real time and utilises the data to control the same irrigation event to give optimum performance for the current soil conditions. The main components of the system are as follows: the sensing of flow rate and a single advance time to a point approximately midway down the field, a system for scaling the soil infiltration characteristic and a hydraulic simulation program based on the full hydrodynamic model. A modem is attached to a microcomputer enabling it to receive signals from the flow meter and advance sensor via a radio telemetry system. Sample data from a furrow-irrigated commercial cotton property are used to demonstrate how the system works. The results demonstrate that improvements in on-farm water use efficiency and labour savings are potentially achievable through the use of the system.
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Evaluating the performance of a Real-Time Optimisation system for furrow irrigation
Agricultural Water Management, 2014Co-Authors: Richard Koech, Rod Smith, Malcolm GilliesAbstract:This paper reports the performance evaluations undertaken on a Real-Time Optimisation system for furrow irrigation (AutoFurrow). Trials for the system were undertaken on commercial furrow-irrigated cotton properties near St George and Dalby, Queensland Australia. The system performed robustly in the field and demonstrated its potential for substantial water savings; however, the results suggested that there was further scope for improvement in performance. To identify opportunities for improvement, the surface irrigation simulation model SISCO was used to investigate the effect of varying: the objective function, flow rate, irrigation deficit, infiltration scaling process and the model infiltration curve. It was found that a simple objective function that aims to maximise application efficiency (AE) can deliver accurate prediction of the irrigation performance and potentially add to the robustness of the Optimisation process. It was also demonstrated that if a suitable flow rate is selected initially, then no further change is warranted. The predicted time to cut-off (TCO) was relatively insensitive to the irrigation deficit; however, any change in the irrigation deficit altered the AE predicted.
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Automated real time Optimisation for control of furrow irrigation
2012Co-Authors: Richard KoechAbstract:Furrow irrigation is one of the oldest techniques of surface irrigation and is the most popular method for the irrigation of row crops. In Australia the method is widely used for the irrigation of cotton and in some years it has accounted for about 95% of the total production. The furrow system is however often associated with high labour requirement and low water used e�ciency. In furrow irrigation the soil is used both as a medium for infiltration and also for conveyance of water from one end of field to the other. However, the spatial and temporal soil infiltration variability causes non-uniformity in water absorption rates and furrow stream advance rates. This significantly reduces water use e�ciency because current design and management practices do not take this variability into account. Most operations in the furrow system are undertaken manually, and are hence labour-intensive. Real time Optimisation and control of furrow irrigation has been proposed for the management of infiltration variability. The system estimates the soil infiltration characteristics in real time and uses the data to control the same irrigation, potentially leading to improvement of water use e�ciency. The major goal of this research was therefore to develop, prove and demonstrate an automated system for real time Optimisation of furrow irrigation. The hypotheses of the research were that: (i) use of real time Optimisation and control in furrow irrigation can lead to signi�cant improvement in irrigation performance, and (ii) automation of furrow irrigation is feasible. The system developed in this research is an integration of a simulation model and associated automation hardware and consists of �five main components: (i) a water delivery system, (ii) an inflow measurement system, (ii) a water sensor to monitor advance of water along the furrow, (iv) computer running the simulation model (AutoFurrow), and (v) a radio telemetry system to facilitate communication among the system components. AutoFurrow uses a scaling technique to adjust the soil infiltration characteristic and determine the soil conditions prevailing for the particular irrigation. Hence it optimises the current irrigation to satisfy the soil moisture deficit and other user-defined objectives (for example target effi�ciency, uniformity and run-o�) and determines the time to end the irrigation in suffi�cient time for effective control of the irrigation. Trials to test and prove the new system were undertaken on two separate commercial cotton properties over two consecutive irrigation seasons. The system implemented for the field trials was not fully automated, and operations such as starting and cutting of flow was achieved manually. Apart from evaluations of the Optimisation system, full advance data and other measurements were taken for all trials to enable a post-irrigation complete (actual) irrigation evaluation to be undertaken. Performances expected as per the grower's irrigation management practices were also evaluated. The SISCO simulation model was used for analysis of data. The results suggested that the Optimisation system was successful in delivering irrigation performance significantly better than achieved by the grower. However, in the 2010/11 irrigation season this performance (predicted by the Optimisation system) was found to be slightly higher than the actual performance and much less than that suggested by a post irrigation Optimisation undertaken using the full measured data for each irrigation. This suggested that the system had not reached its full potential and further improvements were necessary. Factors investigated for their possible contribution to performance of the real time Optimisation system were: flow rate, objective function, selection of the model curve, and the infiltration scaling process. The investigations involved an exhaustive series of simulations using the SISCO model, varying each of these factors in turn. The key changes in the evaluation methodology effected as a result of these investigations and used for the 2011/12 irrigation season trials were: the adoption of a simpler objective function consisting only of RE, and (ii) taking the average shape of the previous in�filtrations curves and using it as the model curve. The benefit of these changes was clearly evident in the results obtained from the 2011/12 trials - the performance of the Optimisation system improved and the difference between the actual performance predicted by the Optimisation system was reduced to � 4%. This research has therefore achieved its overall goal of designing and testing a real time Optimisation system for furrow irrigation. It has also successfully demonstrated the potential benefits of real time Optimisation and shown that the automation of the furrow system is feasible. Further research has been recommended including a comprehensive economic analysis and the trialling of the system in bay irrigation.
