Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent
The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid con...
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2024-12-01
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| author | Shyamal S. Chand Branislav Hredzak Maurizio Cirrincione |
| author_facet | Shyamal S. Chand Branislav Hredzak Maurizio Cirrincione |
| author_sort | Shyamal S. Chand |
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| description | The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink<sup>®</sup> 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the <i>d</i> and <i>q</i> axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the <i>d</i> and <i>q</i> axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach. |
| format | Article |
| id | doaj-art-8a31e65779b541ed8a996d8891cae038 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8a31e65779b541ed8a996d8891cae0382025-01-10T13:16:55ZengMDPI AGEnergies1996-10732024-12-011814410.3390/en18010044Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient AgentShyamal S. Chand0Branislav Hredzak1Maurizio Cirrincione2School of Electrical Engineering and Telecommunications (EET), University of New South Wales (UNSW), Sydney, NSW 2052, AustraliaSchool of Electrical Engineering and Telecommunications (EET), University of New South Wales (UNSW), Sydney, NSW 2052, AustraliaUniversité Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, F-90010 Belfort, FranceThe elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink<sup>®</sup> 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the <i>d</i> and <i>q</i> axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the <i>d</i> and <i>q</i> axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach.https://www.mdpi.com/1996-1073/18/1/44reinforcement learningtwin delayed deep deterministic policy gradientdata-driven controlgrid fault ride-throughvoltage-oriented controlasymmetrical and symmetrical faults |
| spellingShingle | Shyamal S. Chand Branislav Hredzak Maurizio Cirrincione Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent Energies reinforcement learning twin delayed deep deterministic policy gradient data-driven control grid fault ride-through voltage-oriented control asymmetrical and symmetrical faults |
| title | Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent |
| title_full | Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent |
| title_fullStr | Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent |
| title_full_unstemmed | Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent |
| title_short | Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent |
| title_sort | multi fault tolerant operation of grid interfaced photovoltaic inverters using twin delayed deep deterministic policy gradient agent |
| topic | reinforcement learning twin delayed deep deterministic policy gradient data-driven control grid fault ride-through voltage-oriented control asymmetrical and symmetrical faults |
| url | https://www.mdpi.com/1996-1073/18/1/44 |
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