A. Nazer
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This article introduces an innovative parallel differential power processing (PDPP) architecture designed to mitigate the effect of mismatch among photovoltaic (PV) strings. The proposed PV to virtual bus PDPP architecture leverages a virtual bus as the input for all string-level converters. Notably, this approach allows for a reduction in the voltage rating of components since the virtual bus voltage can be set lower than the main bus or PV strings voltage. In this architecture, crucial requirements for the string-level converters (SLCs) include the capability to generate positive and negative output voltages and to provide isolation. To fulfill these requirements, a dual active bridge converter connected to a bridgeless converter as the PDPP SLCs is considered. In this architecture, while SLCs ensure maximum power point tracking (MPPT) for each PV string using conventional MPPT algorithms, the central converter controls the virtual bus voltage. Experimental results validate the performance of the proposed PV to virtual bus PDPP architecture with a system efficiency ranging from 96.4% to 99%.
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This article introduces an innovative parallel differential power processing (PDPP) architecture designed to mitigate the effect of mismatch among photovoltaic (PV) strings. The proposed PV to virtual bus PDPP architecture leverages a virtual bus as the input for all string-level converters. Notably, this approach allows for a reduction in the voltage rating of components since the virtual bus voltage can be set lower than the main bus or PV strings voltage. In this architecture, crucial requirements for the string-level converters (SLCs) include the capability to generate positive and negative output voltages and to provide isolation. To fulfill these requirements, a dual active bridge converter connected to a bridgeless converter as the PDPP SLCs is considered. In this architecture, while SLCs ensure maximum power point tracking (MPPT) for each PV string using conventional MPPT algorithms, the central converter controls the virtual bus voltage. Experimental results validate the performance of the proposed PV to virtual bus PDPP architecture with a system efficiency ranging from 96.4% to 99%.
Photovoltaic (PV) systems are frequently subject to voltage and current mismatches caused by various factors, such as partial shading, differing panel tilt angles, dust accumulation, and cell degradation among PV elements. These mismatches can significantly reduce the overall efficiency of PV systems by preventing individual modules or strings from operating at their maximum power point (MPP). This article introduces a novel architecture termed PV to virtual bus series–parallel differential power processing, which effectively mitigates mismatches in both series-connected PV modules (i.e., current mismatches) and parallel-connected PV strings (i.e., voltage mismatches). The proposed architecture employs a combination of string-level converters (SLCs) and module-integrated converters (MICs) that process only a fraction of the total power. Notably, the architecture leverages virtual buses on the primary side of both SLCs and MICs, leading to reduced voltage rating requirements for SLCs and lower power rating demands for MICs. This design reduces the stress on individual components, making the system more cost-effective and reliable. The article provides a comprehensive analysis of the requirements for SLCs and MICs, along with a detailed explanation of how the proposed architecture ensures that PV modules consistently operate at their respective MPPs. In addition, it explains how the virtual bus voltage is balanced through mathematical power flow equations, ensuring stable and efficient operation. Finally, the architecture’s effectiveness is validated through real-time simulation results with two PLECS real-time (RT) boxes, which demonstrate its capability to address mismatch issues and optimize the performance of PV systems.
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Photovoltaic (PV) systems are frequently subject to voltage and current mismatches caused by various factors, such as partial shading, differing panel tilt angles, dust accumulation, and cell degradation among PV elements. These mismatches can significantly reduce the overall efficiency of PV systems by preventing individual modules or strings from operating at their maximum power point (MPP). This article introduces a novel architecture termed PV to virtual bus series–parallel differential power processing, which effectively mitigates mismatches in both series-connected PV modules (i.e., current mismatches) and parallel-connected PV strings (i.e., voltage mismatches). The proposed architecture employs a combination of string-level converters (SLCs) and module-integrated converters (MICs) that process only a fraction of the total power. Notably, the architecture leverages virtual buses on the primary side of both SLCs and MICs, leading to reduced voltage rating requirements for SLCs and lower power rating demands for MICs. This design reduces the stress on individual components, making the system more cost-effective and reliable. The article provides a comprehensive analysis of the requirements for SLCs and MICs, along with a detailed explanation of how the proposed architecture ensures that PV modules consistently operate at their respective MPPs. In addition, it explains how the virtual bus voltage is balanced through mathematical power flow equations, ensuring stable and efficient operation. Finally, the architecture’s effectiveness is validated through real-time simulation results with two PLECS real-time (RT) boxes, which demonstrate its capability to address mismatch issues and optimize the performance of PV systems.
Distributed Maximum Power Point Tracking Architecture for Photovoltaic Systems
Photovoltaic to Virtual Bus Differential Power Processing
This thesis introduces and develops advanced Differential Power Processing (DPP) architectures to enhance the performance and efficiency of photovoltaic (PV) systems by addressing mismatches among PV modules and strings. The work focuses on mitigating power losses due to voltage and current mismatches due to factors such as partial shading, panel misalignment, dust, and degradation. The research proposes novel architectures designed to reduce component voltage and power ratings, potentially lowering costs while maintaining high efficiency.
PV to Virtual Bus Parallel Differential Power Processing (PV2VB PDPP) Architecture: A new PV2VB PDPP architecture is introduced, leveraging a virtual bus as the input for string-level converters (SLCs). This design allows for reduced components’ voltage ratings by operating the virtual bus at a lower voltage than the main bus or PV strings. The architecture employs Dual Active Bridge converters connected to Bridgeless converters as SLCs to provide isolation and handle both positive and negative outputs. Experimental results demonstrate system efficiency ranging from 96.4% to 99%.
Dynamic Analysis and Stability: The thesis includes a comprehensive dynamic analysis of the PV2VB PDPP architecture, deriving small-signal models, transfer functions, and frequency responses. These analyses aid in understanding the system’s dynamic behavior, enabling effective controller design and stability studies. Experimental validation confirms fast stabilization of the virtual bus voltage (0.6 seconds) and intermediate bus voltages (15 milliseconds), ensuring efficient Maximum Power Point Tracking (MPPT) for each PV string.
Battery Integration in PV2VB PDPP Architecture: The work extends the PDPP architecture to include battery integration at the virtual bus, facilitating energy storage and management while performing MPPT. The battery integration reduces component voltage ratings and allows for efficient charging and discharging control by the central converter. Experimental evaluations show system efficiencies between 95.5% and 99%.
PV to Virtual Bus Series-Parallel Differential Power Processing (PV2VB SPDPP) Architecture: To address mismatches in both series-connected modules and parallel-connected strings, a PV2VB SPDPP architecture is proposed. This architecture uses a combination of SLCs and module-integrated converters (MICs), processing only a fraction of the total power. By leveraging virtual buses for both SLCs and MICs, the architecture reduces voltage and power stress on components, improving cost-effectiveness and reliability. Real-time simulations validate the system’s ability to balance power flow, ensure stable operation, and optimize PV module performance under mismatch conditions.
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PV to Virtual Bus Parallel Differential Power Processing (PV2VB PDPP) Architecture: A new PV2VB PDPP architecture is introduced, leveraging a virtual bus as the input for string-level converters (SLCs). This design allows for reduced components’ voltage ratings by operating the virtual bus at a lower voltage than the main bus or PV strings. The architecture employs Dual Active Bridge converters connected to Bridgeless converters as SLCs to provide isolation and handle both positive and negative outputs. Experimental results demonstrate system efficiency ranging from 96.4% to 99%.
Dynamic Analysis and Stability: The thesis includes a comprehensive dynamic analysis of the PV2VB PDPP architecture, deriving small-signal models, transfer functions, and frequency responses. These analyses aid in understanding the system’s dynamic behavior, enabling effective controller design and stability studies. Experimental validation confirms fast stabilization of the virtual bus voltage (0.6 seconds) and intermediate bus voltages (15 milliseconds), ensuring efficient Maximum Power Point Tracking (MPPT) for each PV string.
Battery Integration in PV2VB PDPP Architecture: The work extends the PDPP architecture to include battery integration at the virtual bus, facilitating energy storage and management while performing MPPT. The battery integration reduces component voltage ratings and allows for efficient charging and discharging control by the central converter. Experimental evaluations show system efficiencies between 95.5% and 99%.
PV to Virtual Bus Series-Parallel Differential Power Processing (PV2VB SPDPP) Architecture: To address mismatches in both series-connected modules and parallel-connected strings, a PV2VB SPDPP architecture is proposed. This architecture uses a combination of SLCs and module-integrated converters (MICs), processing only a fraction of the total power. By leveraging virtual buses for both SLCs and MICs, the architecture reduces voltage and power stress on components, improving cost-effectiveness and reliability. Real-time simulations validate the system’s ability to balance power flow, ensure stable operation, and optimize PV module performance under mismatch conditions.
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This thesis introduces and develops advanced Differential Power Processing (DPP) architectures to enhance the performance and efficiency of photovoltaic (PV) systems by addressing mismatches among PV modules and strings. The work focuses on mitigating power losses due to voltage and current mismatches due to factors such as partial shading, panel misalignment, dust, and degradation. The research proposes novel architectures designed to reduce component voltage and power ratings, potentially lowering costs while maintaining high efficiency.
PV to Virtual Bus Parallel Differential Power Processing (PV2VB PDPP) Architecture: A new PV2VB PDPP architecture is introduced, leveraging a virtual bus as the input for string-level converters (SLCs). This design allows for reduced components’ voltage ratings by operating the virtual bus at a lower voltage than the main bus or PV strings. The architecture employs Dual Active Bridge converters connected to Bridgeless converters as SLCs to provide isolation and handle both positive and negative outputs. Experimental results demonstrate system efficiency ranging from 96.4% to 99%.
Dynamic Analysis and Stability: The thesis includes a comprehensive dynamic analysis of the PV2VB PDPP architecture, deriving small-signal models, transfer functions, and frequency responses. These analyses aid in understanding the system’s dynamic behavior, enabling effective controller design and stability studies. Experimental validation confirms fast stabilization of the virtual bus voltage (0.6 seconds) and intermediate bus voltages (15 milliseconds), ensuring efficient Maximum Power Point Tracking (MPPT) for each PV string.
Battery Integration in PV2VB PDPP Architecture: The work extends the PDPP architecture to include battery integration at the virtual bus, facilitating energy storage and management while performing MPPT. The battery integration reduces component voltage ratings and allows for efficient charging and discharging control by the central converter. Experimental evaluations show system efficiencies between 95.5% and 99%.
PV to Virtual Bus Series-Parallel Differential Power Processing (PV2VB SPDPP) Architecture: To address mismatches in both series-connected modules and parallel-connected strings, a PV2VB SPDPP architecture is proposed. This architecture uses a combination of SLCs and module-integrated converters (MICs), processing only a fraction of the total power. By leveraging virtual buses for both SLCs and MICs, the architecture reduces voltage and power stress on components, improving cost-effectiveness and reliability. Real-time simulations validate the system’s ability to balance power flow, ensure stable operation, and optimize PV module performance under mismatch conditions.
PV to Virtual Bus Parallel Differential Power Processing (PV2VB PDPP) Architecture: A new PV2VB PDPP architecture is introduced, leveraging a virtual bus as the input for string-level converters (SLCs). This design allows for reduced components’ voltage ratings by operating the virtual bus at a lower voltage than the main bus or PV strings. The architecture employs Dual Active Bridge converters connected to Bridgeless converters as SLCs to provide isolation and handle both positive and negative outputs. Experimental results demonstrate system efficiency ranging from 96.4% to 99%.
Dynamic Analysis and Stability: The thesis includes a comprehensive dynamic analysis of the PV2VB PDPP architecture, deriving small-signal models, transfer functions, and frequency responses. These analyses aid in understanding the system’s dynamic behavior, enabling effective controller design and stability studies. Experimental validation confirms fast stabilization of the virtual bus voltage (0.6 seconds) and intermediate bus voltages (15 milliseconds), ensuring efficient Maximum Power Point Tracking (MPPT) for each PV string.
Battery Integration in PV2VB PDPP Architecture: The work extends the PDPP architecture to include battery integration at the virtual bus, facilitating energy storage and management while performing MPPT. The battery integration reduces component voltage ratings and allows for efficient charging and discharging control by the central converter. Experimental evaluations show system efficiencies between 95.5% and 99%.
PV to Virtual Bus Series-Parallel Differential Power Processing (PV2VB SPDPP) Architecture: To address mismatches in both series-connected modules and parallel-connected strings, a PV2VB SPDPP architecture is proposed. This architecture uses a combination of SLCs and module-integrated converters (MICs), processing only a fraction of the total power. By leveraging virtual buses for both SLCs and MICs, the architecture reduces voltage and power stress on components, improving cost-effectiveness and reliability. Real-time simulations validate the system’s ability to balance power flow, ensure stable operation, and optimize PV module performance under mismatch conditions.
In photovoltaic (PV) systems, unavoidable factors, such as partial shading, nonoptimal mounting angles of PV modules, and accumulation of dust result in mismatches, consequently diminishing energy yield. A promising solution to mitigate these issues is to use distributed maximum power point tracking (DMPPT) architectures. To alleviate mismatch-related losses, many DMPPT architectures, including full power processing (FPP) and differential power processing (DPP), have been documented in the literature. FPP encompasses techniques, such as microinverters, modular multilevel cascade inverters, and dc architectures, such as parallel, series, and total cross-tied. DPP variants include series DPP, parallel DPP, and series–parallel DPP architectures. Moreover, novel DMPPT architectures, such as hybrid and hierarchical architectures, along with advancements in converter topologies and control strategies, continue to emerge, aiming to improve levelized cost of energy. Each novel solution brings distinct advantages and challenges, but the extensive number of architectures, power converters topologies, and control methods have led to confusion and complexity in navigating the literature. This article systematically categorizes, reviews, and compares various DMPPT architectures, associated converters, and control strategies, providing a comprehensive overview of the evolving landscape of DMPPT development. By elucidating existing advancements and identifying gaps for further research, this review aims to offer clarity and guidance in advancing DMPPT technology for enhanced PV system performance.
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In photovoltaic (PV) systems, unavoidable factors, such as partial shading, nonoptimal mounting angles of PV modules, and accumulation of dust result in mismatches, consequently diminishing energy yield. A promising solution to mitigate these issues is to use distributed maximum power point tracking (DMPPT) architectures. To alleviate mismatch-related losses, many DMPPT architectures, including full power processing (FPP) and differential power processing (DPP), have been documented in the literature. FPP encompasses techniques, such as microinverters, modular multilevel cascade inverters, and dc architectures, such as parallel, series, and total cross-tied. DPP variants include series DPP, parallel DPP, and series–parallel DPP architectures. Moreover, novel DMPPT architectures, such as hybrid and hierarchical architectures, along with advancements in converter topologies and control strategies, continue to emerge, aiming to improve levelized cost of energy. Each novel solution brings distinct advantages and challenges, but the extensive number of architectures, power converters topologies, and control methods have led to confusion and complexity in navigating the literature. This article systematically categorizes, reviews, and compares various DMPPT architectures, associated converters, and control strategies, providing a comprehensive overview of the evolving landscape of DMPPT development. By elucidating existing advancements and identifying gaps for further research, this review aims to offer clarity and guidance in advancing DMPPT technology for enhanced PV system performance.
This paper proposes a simplified parallel differential power processing (PDPP) architecture for photovoltaic (PV) systems that reduces hardware complexity by eliminating one dual active bridge (DAB) converter and one intermediate bus capacitor from the previously introduced PV2VB PDPP two-string architecture. In the reference PV2VB PDPP architecture, each PV string is interfaced with its own string-level converter (SLC), comprising a DAB and a bridgeless (BL) converter, for maximum power point tracking (MPPT) and differential power exchange via a shared virtual bus. The proposed topology maintains the use of bridgeless converters for each PV string but replaces the two DAB stages with a single dual-bridge DAB converter, which connects one intermediate bus on one side and the virtual bus on the other. This approach maintains isolation and bidirectional power flow, while significantly reducing the number of magnetic components, switches, and control loops. Simulation results demonstrate that the system can reach and sustain steady-state operation of the virtual bus under various mismatch conditions, validating the effectiveness of the proposed architecture in ensuring power balance and enabling string-level MPPT. The results suggest that the proposed scheme preserves the key benefits of the PV2VB PDPP framework, while simultaneously reducing overall system cost.
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This paper proposes a simplified parallel differential power processing (PDPP) architecture for photovoltaic (PV) systems that reduces hardware complexity by eliminating one dual active bridge (DAB) converter and one intermediate bus capacitor from the previously introduced PV2VB PDPP two-string architecture. In the reference PV2VB PDPP architecture, each PV string is interfaced with its own string-level converter (SLC), comprising a DAB and a bridgeless (BL) converter, for maximum power point tracking (MPPT) and differential power exchange via a shared virtual bus. The proposed topology maintains the use of bridgeless converters for each PV string but replaces the two DAB stages with a single dual-bridge DAB converter, which connects one intermediate bus on one side and the virtual bus on the other. This approach maintains isolation and bidirectional power flow, while significantly reducing the number of magnetic components, switches, and control loops. Simulation results demonstrate that the system can reach and sustain steady-state operation of the virtual bus under various mismatch conditions, validating the effectiveness of the proposed architecture in ensuring power balance and enabling string-level MPPT. The results suggest that the proposed scheme preserves the key benefits of the PV2VB PDPP framework, while simultaneously reducing overall system cost.
Photovoltaic (PV) to virtual bus parallel differential power processing (PDPP) architecture can mitigate mismatch losses among PV strings. This article presents a comprehensive dynamic analysis by deriving a small-signal model of the PDPP architecture based on its state space model. Subsequently, the corresponding transfer functions and frequency response are obtained, offering valuable insights into the dynamic behavior of the architecture. To validate the accuracy of the derived model, the frequency response has also been achieved by observed data from both PLECS simulation and experiment through system identification. Besides, this article discusses the design considerations of the discrete controllers' parameters for both virtual and intermediate bus voltages and studies the stability of the architecture. Experimental measurements confirm the ability of the central controller to stabilize the virtual bus voltage to the desired level within 0.6 seconds, while the intermediate bus voltages settle within 15 ms, enabling proper maximum power point tracking of each PV string.
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Photovoltaic (PV) to virtual bus parallel differential power processing (PDPP) architecture can mitigate mismatch losses among PV strings. This article presents a comprehensive dynamic analysis by deriving a small-signal model of the PDPP architecture based on its state space model. Subsequently, the corresponding transfer functions and frequency response are obtained, offering valuable insights into the dynamic behavior of the architecture. To validate the accuracy of the derived model, the frequency response has also been achieved by observed data from both PLECS simulation and experiment through system identification. Besides, this article discusses the design considerations of the discrete controllers' parameters for both virtual and intermediate bus voltages and studies the stability of the architecture. Experimental measurements confirm the ability of the central controller to stabilize the virtual bus voltage to the desired level within 0.6 seconds, while the intermediate bus voltages settle within 15 ms, enabling proper maximum power point tracking of each PV string.
Photovoltaic (PV) systems are often exposed to mismatch caused by partial shading, different mounting angles, dust accumulation, cell degradation, and so on. This paper proposes a novel parallel differential power processing (P-DPP) configuration to minimize mismatch-related losses among PV strings. The proposed configuration, called PV to Virtual Bus P-DPP, uses a virtual bus as an input for all P-DPP converters. Since the virtual bus voltage can be selected lower than the DC Bus voltage, components’ voltage rating can be reduced. An essential feature of the proposed configuration is the ability of the converters to generate both positive and negative output voltage. Therefore, a bidirectional flyback converter connected to a bridgeless converter is proposed as the P-DPP converter. To find the MPP of each PV string, the Perturb and Observe (P&O) algorithm is implemented. Moreover, a proportional–integral feedback controller controls the virtual bus voltage through the central converter. The benefits of the proposed configuration are discussed, and the operation of the proposed structure is further verified through simulations with the software PLECS.
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Photovoltaic (PV) systems are often exposed to mismatch caused by partial shading, different mounting angles, dust accumulation, cell degradation, and so on. This paper proposes a novel parallel differential power processing (P-DPP) configuration to minimize mismatch-related losses among PV strings. The proposed configuration, called PV to Virtual Bus P-DPP, uses a virtual bus as an input for all P-DPP converters. Since the virtual bus voltage can be selected lower than the DC Bus voltage, components’ voltage rating can be reduced. An essential feature of the proposed configuration is the ability of the converters to generate both positive and negative output voltage. Therefore, a bidirectional flyback converter connected to a bridgeless converter is proposed as the P-DPP converter. To find the MPP of each PV string, the Perturb and Observe (P&O) algorithm is implemented. Moreover, a proportional–integral feedback controller controls the virtual bus voltage through the central converter. The benefits of the proposed configuration are discussed, and the operation of the proposed structure is further verified through simulations with the software PLECS.