| 1. |
- Zhang, Mengfan, et al.
(författare)
-
Decentralized Coordination and Stabilization of Hybrid Energy Storage Systems in DC Microgrids
- 2022
-
Ingår i: IEEE Transactions on Smart Grid. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3053 .- 1949-3061. ; 13:3, s. 1751-1761
-
Tidskriftsartikel (refereegranskat)abstract
- Hybrid energy storage system (HESS) is an attractive solution to compensate power balance issues caused by intermittent renewable generations and pulsed power load in DC microgrids. The purpose of HESS is to ensure optimal usage of heterogeneous storage systems with different characteristics. In this context, power allocation for different energy storage units is a major concern. At the same time, the wide integration of power electronic converters in DC microgrids would possibly cause the constant power load instability issue. This paper proposes a composite model predictive control based decentralized dynamic power sharing strategy for HESS. First, a composite model predictive controller (MPC) is proposed for a system with a single ESS and constant power loads (CPLs). It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system disturbances. Then, a coordinated scheme is developed for HESS by using the proposed composite MPC with a virtual resistance droop controller for the battery system and with a virtual capacitance droop controller for the supercapacitor (SC) system. With the proposed scheme, the battery only supplies smooth power at steady state, while the SC compensates all the fast fluctuations. The proposed scheme achieves a decentralized dynamic power sharing and optimized transient performance under large variation of sources and loads. The proposed approach is verified by simulations and experiments.
|
|
| 2. |
- Zhang, Mengfan, et al.
(författare)
-
Decentralized Coordination and Stabilization of Hybrid Energy Storage Systems in DC Microgrids
- 2023
-
Ingår i: 2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Hybrid energy storage system (HESS) is an attractive solution to compensate power balance issues caused by intermittent renewable generations and pulsed power load in DC microgrids. The purpose of HESS is to ensure optimal usage of heterogeneous storage systems with different characteristics. In this context, power allocation for different energy storage units is a major concern. At the same time, the wide integration of power electronic converters in DC microgrids would possibly cause the constant power load instability issue. This paper proposes a composite model predictive control based decentralized dynamic power sharing strategy for HESS. First, a composite model predictive controller (MPC) is proposed for a system with a single ESS and constant power loads (CPLs). It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system disturbances. Then, a coordinated scheme is developed for HESS by using the proposed composite MPC with a virtual resistance droop controller for the battery system and with a virtual capacitance droop controller for the supercapacitor (SC) system. With the proposed scheme, the battery only supplies smooth power at steady state, while the SC compensates all the fast fluctuations. The proposed scheme achieves a decentralized dynamic power sharing and optimized transient performance under large variation of sources and loads. The proposed approach is verified by simulations and experiments.
|
|
| 3. |
- Zhang, Mengfan, et al.
(författare)
-
Transfer Learning Based Online Impedance Identification for Modular Multilevel Converters
- 2023
-
Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 38:10, s. 12207-12218
-
Tidskriftsartikel (refereegranskat)abstract
- The large integration of modular multilevel converters (MMC) has introduced stability issues. The impedance-based stability analysis method is widely adopted, where the impedance model can be directly achieved at the terminals through nonintrusive measurement, which facilitates the black-box stability analysis of the MMC-grid interaction system. Yet, due to the limited impedance data amount in the practical application of online impedance identification, the accuracy of the identified model stability analysis cannot be guaranteed with existing methods in variable operating point scenarios. This article proposes a transfer learning based online impedance identification for MMC to address this research gap. The two-phase online impedance identification method is developed where the physical model of MMC in the offline phase is utilized to facilitate the online impedance identification. The proposed method can significantly reduce the data amount requirement in online impedance identification and achieve online stability analysis of the MMC system. The case studies confirm the effectiveness of the proposed method.
|
|
| 4. |
- Agredano Torres, Manuel, et al.
(författare)
-
Decentralized Dynamic Power Sharing Control for Frequency Regulation Using Hybrid Hydrogen Electrolyzer Systems
- 2024
-
Ingår i: IEEE Transactions on Sustainable Energy. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3029 .- 1949-3037. ; 15:3, s. 1847-1858
-
Tidskriftsartikel (refereegranskat)abstract
- Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results.
|
|
| 5. |
- Agredano Torres, Manuel, et al.
(författare)
-
Dynamic power allocation control for frequency regulation using hybrid electrolyzer systems
- 2023
-
Ingår i: 2023 IEEE Applied Power Electronics Conference And Exposition, APEC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2991-2998
-
Konferensbidrag (refereegranskat)abstract
- The increase in hydrogen production to support the energy transition in different sectors, such as the steel industry, leads to the utilization of large scale electrolyzers. These electrolyzers have the ability to become a fundamental tool for grid stability providing grid services, especially frequency regulation, for power grids with a high share of renewable energy sources. Alkaline electrolyzers (AELs) have low cost and long lifetime, but their slow dynamics make them unsuitable for fast frequency regulation, especially in case of contingencies. Proton Exchange Membrane electrolyzers (PEMELs) have fast dynamic response to provide grid services, but they have higher costs. This paper proposes a dynamic power allocation control strategy for hybrid electrolyzer systems to provide frequency regulation with reduced cost, making use of advantages of AELs and PEMELs. Simulations and experiments are conducted to verify the proposed control strategy.
|
|
| 6. |
- Guo, Guodong, et al.
(författare)
-
Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay
- 2023
-
Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 349
-
Tidskriftsartikel (refereegranskat)abstract
- The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay.
|
|
| 7. |
- Liu, Ruixu, et al.
(författare)
-
Disturbance Observer-Based Model Predictive Power Synchronization Control for Suppression of Synchronous Oscillation
- 2023
-
Ingår i: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Grid-forming (GFM) converters can achieve self-synchronization oriented by the active power balance, which is a promising solution for the high penetration of power electronics. Unfortunately, GFM control suffers from the synchronous oscillation (SO) issue, which may result in system instability. This paper proposes a disturbance observer-based model predictive power synchronization approach to suppress SOs of GFM converters. The mechanism of SO is investigated by the small-signal model of the grid-tied GFM converter, and it is revealed that the SO is induced by the electromagnetic dynamics of the power transfer in the transmission line and the power synchronization dynamics dominate this issue. Then, a model predictive power synchronization controller is proposed for mitigating SOs. In addition, a disturbance observer is developed to compensate the influence of disturbances/uncertainties in the system to improve the performance of power tracking. The proposed control approach is verified by simulations.
|
|
| 8. |
- Lu, Yizhou, et al.
(författare)
-
An Online Digital Twin based Health Monitoring Method for Boost Converter using Neural Network
- 2023
-
Ingår i: 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3701-3706
-
Konferensbidrag (refereegranskat)abstract
- This paper proposes a neural network-based digital twin for online health monitoring of vulnerable components in converters. The proposed digital twin consists of a physics-informed model with uncertain parameters, and a neural network (NN) for real-time model updating and health monitoring of components. This method is noninvasive, without extra circuits, and can identify parameters in real-time with high efficiency. Simulation and experiment are conducted to validate the effectiveness of the proposed method in accurate parameter identification and degradation monitoring of capacitor and MOSFET.
|
|
| 9. |
- Mao, Jia, et al.
(författare)
-
CNN and LSTM based Data-driven Cyberattack Detection for Grid-connected PV Inverter
- 2022
-
Ingår i: IEEE International Conference on Control and Automation, ICCA. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 704-709
-
Konferensbidrag (refereegranskat)abstract
- Growing penetration of renewables comes with increased cyber security threat due to inherent low inertia characteristic and sophisticated control and communication networks of power electronics. This paper proposes a data-driven cyberattack detection strategy for grid-connected photovoltaic (PV) inverters. Ideas of long short term memory (LSTM) and convolutional neural network (CNN) as the core of detection achieve time series classification to diagnose the target and mode of cyberattack. Input de-redundancy and hyperparameter selection are conducted to optimize the detection. Meanwhile, well-designed cyberattack toolboxes of false data injection (FDI), denial-of-service (DoS) and delay are applied upon the communication of both sampled signals and issued commands in a grid-connected inverter model. By observing system performance via electrical measurements, this case study evaluates the LSTM, CNN-LSTM and convolutional LSTM based detection and obtains stable high quality of classification.
|
|
| 10. |
- Zhang, Mengfan, et al.
(författare)
-
An MPC based Power Management Method for Renewable Energy Hydrogen based DC Microgrids
- 2023
-
Ingår i: 2023 IEEE Applied Power Electronics Conference and Exposition, APEC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 577-581
-
Konferensbidrag (refereegranskat)abstract
- The renewable energy hydrogen based dc microgrid is an attractive solution for renewables integration, as the hydrogen is a clean fuel, that extra renewable energy source generation can be stored as hydrogen through electrolysis technology, and be used later through fuel cell technology. However, the efficiency of the electrolyzer and fuel cell change significantly under the wide operation ranges, and they have different degradation mechanisms that are greatly impacted by current ripples. Moreover, to achieve consistent power supply with 100% RESs, the electrolyzer and fuel cell need to be optimally coordinated. To address the issues, this paper proposes an MPC based power management method to achieve smooth power sharing and reduce the current ripple, also can guarantee the system stability under uncertainties of the renewable energy source and load. It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system uncertainties. Both the simulation and experiment results can validate the effectiveness of the proposed method.
|
|
| 11. |
- Zhang, Mengfan, et al.
(författare)
-
Data Driven Decentralized Control of Inverter based Renewable Energy Sources using Safe Guaranteed Multi-Agent Deep Reinforcement Learning
- 2024
-
Ingår i: IEEE Transactions on Sustainable Energy. - 1949-3029 .- 1949-3037. ; 15:2, s. 1288-1299
-
Tidskriftsartikel (refereegranskat)abstract
- The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposedalgorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications
|
|
| 12. |
- Zhang, Mengfan, et al.
(författare)
-
Data Driven Decentralized Control of Inverter Based Renewable Energy Sources Using Safe Guaranteed Multi-Agent Deep Reinforcement Learning
- 2024
-
Ingår i: IEEE Transactions on Sustainable Energy. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3029 .- 1949-3037. ; 15:2, s. 1288-1299
-
Tidskriftsartikel (refereegranskat)abstract
- The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.
|
|
| 13. |
- Zhang, Mengfan, et al.
(författare)
-
Data-Driven Modeling of Power-Electronics-Based Power System Considering the Operating Point Variation
- 2021
-
Ingår i: 2021 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3513-3517
-
Konferensbidrag (refereegranskat)abstract
- Large-scale integrations of power-electronics devices have introduced the stability challenges to the conventional power system. The stability of the power-electronics-based power systems, which are modeled by a Multi-Input Multi-Output (MIMO) transfer function matrix, can be analyzed based on the Nyquist Criterion. However, since no or limited information about the internal control details, this matrix can only be obtained using the measured data. On the other hand, the elements of the matrix will change along with the operating point of each power-electronics converter, which introduces the challenge to guarantee the interaction stability of each inverter at different operating points. In this paper, a data-driven method is proposed to overcome this operating-point dependent challenge. An artificial neural network (ANN) is used to characterize the operating-point dependent model of power-electronics-based power systems. The comparison results confirm the accuracy of the impedance model obtained by this data-driven modeling method.
|
|
| 14. |
- Zhang, Mengfan, et al.
(författare)
-
DNN Assisted Projection based Deep Reinforcement Learning for Safe Control of Distribution Grids
- 2024
-
Ingår i: IEEE Transactions on Power Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8950 .- 1558-0679. ; 39:4, s. 5687-5698
-
Tidskriftsartikel (refereegranskat)abstract
- Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.
|
|
| 15. |
- Zhang, Mengfan, et al.
(författare)
-
Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System
- 2022
-
Ingår i: 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728193885
-
Konferensbidrag (refereegranskat)abstract
- With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.
|
|
| 16. |
- Zhang, Mengfan, et al.
(författare)
-
Physics-Informed Neural Network Based Online Impedance Identification of Voltage Source Converters
- 2023
-
Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 70:4, s. 3717-3728
-
Tidskriftsartikel (refereegranskat)abstract
- The wide integration of voltage source converters (VSCs) in power grids as the interface of renewables causes the converter-grid interaction stability challenge. The black-box impedance of VSCs identified at the converter terminal is the key to facilitate the study of converter-grid interaction stability. However, since the limited impedance data amount in online measurement, the existing impedance identification methods cannot accurately capture characteristics of the impedance model in various operating scenarios with the changing profiles of renewables and loads. In this article, a physics-informed neural network based impedance identification is proposed to fill this research gap. The physics knowledge of the VSC is used to compress the artificial neural network, which can reduce the calculation burden of online impedance identification. Meanwhile, the two-steps impedance identification is developed with the inspiration of the transfer learning theory to further increase the online impedance identification efficiency. This method can significantly reduce the required data amount used in online impedance identification for the online stability analysis with the changing operating points. The case studies confirm the effectiveness of the proposed method.
|
|
| 17. |
- Zhang, Mengfan, et al.
(författare)
-
Review of online learning for control and diagnostics of power converters and drives : Algorithms, implementations and applications
- 2023
-
Ingår i: Renewable & sustainable energy reviews. - : Elsevier BV. - 1364-0321 .- 1879-0690. ; 186
-
Forskningsöversikt (refereegranskat)abstract
- Power converters and motor drives are playing a significant role in the transition towards sustainable energy systems and transportation electrification. In this context, rich diversity of new power converters and motor drive products are developed and commissioned by the industry every year. However, to achieve efficient, reliable and stable operation of power converter and drive systems, there are challenges in condition monitoring, fault diagnosis, lifecycle estimation, stability evaluation and control. Online learning is an emerging technology that can serve as a powerful remedy to these challenges. This paper aims to provide a systematic study of algorithms, implementations, and applications of online learning for control and diagnostics in the area of power converters and drives. First, online learning problems are formulated for condition monitoring, fault detection, online stability assessment, model predictive control for power converter and drive applications. Next, guidelines are provided about how to develop online learning models and algorithms for these applications. Practical case studies are presented with experimental demonstrations. Finally, challenges and future opportunities are discussed about online learning for power converter and drive applications.
|
|
