String type inverter can also be referred to as modular power conversion system (PCS) or distributed energy storage inverter.

The core of this architecture lies in breaking away from the rigid configuration of traditional centralized PCS – specifically, the "multiple battery strings in parallel → single centralized PCS" model. Instead, it adopts a flexible, distributed structure of "single battery string → dedicated PCS module → parallel connection to AC bus."

The total system power is determined by the number of PCS modules, while the total energy capacity is determined by the number and capacity of the battery strings. These two aspects can be configured relatively independently, offering significant design flexibility and scalability. Customers can flexibly plan power-oriented and energy-oriented applications based on site conditions and budget, and the system supports smooth capacity expansion. This achieves a true decoupling of power and energy design.

N+X Redundancy Design: The system can be configured with more PCS modules than required for the rated power. If a single module or multiple modules fail, the system can automatically isolate them. The remaining modules continue to operate, ensuring the system functions at a derated capacity, thereby significantly enhancing system availability.

Modular Hot-Swapping: Failed PCS modules support online replacement without requiring a complete system shutdown. This enables "online maintenance," greatly reduces the Mean Time To Repair (MTTR), and optimizes operational costs throughout the entire lifecycle.

Fault Domain Isolation: The system decomposes the high-voltage, high-power DC system into multiple independent, low-voltage, low-power units. This achieves fine-grained isolation of fault domains. A failure in a single unit is less likely to propagate throughout the entire system.

Granular Battery Data Analysis: The string-level management unit provides finer-grained battery operational data. This establishes a data foundation for implementing condition-based predictive maintenance and more accurate battery health state assessment, ultimately optimizing asset management and retirement strategies.