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| 1 | Autonomous Power Management for Interlinked AC-DC Microgrids显示文摘The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinked microgrids based on their loading conditions,while other schemes regulate the voltage of the interlinked microgrids without considering the specific loading conditions.However,the existing schemes cannot achieve both objectives efficiently.To address these issues,an autonomous power management scheme is proposed,which explicitly considers the specific loading condition of the DC microgrid before importing power from the interlinked AC microgrid.This strategy enables voltage regulation in the DC microgrid,and also reduces the number of converters in operation.The proposed scheme is fully autonomous while it retains the plug-nplay features for generators and tie-converters.The performance of the proposed control scheme has been validated under different operating scenarios.The results demonstrate the effectiveness of the proposed scheme in managing the power deficit in the DC microgrid efficiently and autonomously while maintaining the better voltage regulation in the DC microgrid. | Inam Ullah Nutkani Lasantha Meegahapola Loh Poh Chiang Andrew Frede Blaabjerg | 2018 | CSEE Journal of Power and Energy Systems2018，4，1： | 4 |
| 2 | Capability con- straints to mitigate voltage fluctuations from DFIG wind farms when delivering ancillary services to the network 显示文摘 | Lasantha Meegahapola Sarath Perera | 2014 | Electrical Power and Energy Systems2014，，62： | 1 |
| 3 | Multi-level current reinjection in double-bridge self-commutated current source conversion 显示文摘 | Lasantha B Perera Yonghe H Liu Neville R Watson | 2005 | Power Delivery IEEE Transactions2005，20，2： | 1 |
| 4 | Automatic Generation Control of Multi-area Power System with Network Constraints and Communication Delays显示文摘Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes. | Ragini Patel Lasantha Meegahapola Liuping Wang Xinghuo Yu Brendan McGrath | 2020 | Journal of Modern Power Systems and Clean Energy2020，8，3： | 1 |
| 5 | Capability constraints to mitigate voltage fluctuations from DFIG wind farms when delivering ancillary services to the network显示文摘 | Lasantha M Sarath P | 2014 | Electrical Power and Energy Systems2014，，62： | 1 |
| 6 | Capability curve based enhanced reactive power control strategy for stability enhancement and network voltage management显示文摘 | Lasantha M Tim L Sarath P | 2013 | Electrical Power and Energy Systems2013，，52： | 1 |
| 7 | Capability curve based enhanced reactive power control strategy for stability enhancement and network voltage management显示文摘 | Meegahapola Lasantha Tim Littler | 2013 | Intemational Journal of Electrical Power & Energy Systems2013，，52： | 1 |
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