Abstract
Grid-connected converters are exposed to the loss of synchronisation with the grid during severe voltage sags particularly when operating under weak grid condition which introduces voltage and frequency volatility. This paper presents employing machine learning methods besides modifying the converter control scheme to enhance the transient stability of power synchronization control (PSC). For early detection of synchronization instability of PSC to provide adequate time for taking correcting control actions, an encoder stacked classifier is proposed which is trained to be robust against data corruption and added noise. Then, by integrating the proposed instability detection scheme to the synchronization loop of PSC, a phase freezing mode is introduced to avoid losing synchronism during grid faults. It is disclosed that the frozen synchronization loop, which is activated by the proposed instability detection scheme, can ensure synchronization stability of PSC. Time-domain simulations are conducted to confirm the presented findings.
Highlights
T HE growing integration of Renewable Energy Sources (RESs) into weak grids, combined with the demand for transferring power between remote points, calls for the stable interconnection of grid-connected converters with weak grids
The emulation of synchronous machine dynamics by converters has become a progressing concept for power systems with high integration level of RESs [3]. This has led to several voltage-stiff control schemes in which Power Synchronization Control (PSC), Virtual Synchronous Machine (VSM), and Synchronous Power Controller (SPC) are the leading ones [4]
These voltage-stiff control schemes have been proposed to emulate the dynamic response of synchronous machines with stiff control on voltage and frequency to address the dynamic issues of the grid
Summary
T HE growing integration of Renewable Energy Sources (RESs) into weak grids, combined with the demand for transferring power between remote points, calls for the stable interconnection of grid-connected converters with weak grids. During severe grid disturbances and transients in weak grids there are fundamental distinctions between a PSC-controlled voltage-source converter and a synchronous machine including limited current contribution in short-circuit faults and different transient response [4]. Considering the complex and challenging nature of time series forecasting, this paper employs machine learning to predict instability of PSC and proposes the related corrective control action during disturbances based on the PSC dynamics and modifies the synchronization loop. Given the aforementioned issues with the transient stability analysis of PSC under weak grid condition, the current paper aims to propose a joint instability detection and controlscheme modification in order to prevent the loss of synchronization during severe disturbances by employing machine learning. Propose a modified PSC scheme whereby the early instability detection is integrated to the synchronization loop which successfully maintains the stability of converter during grid disturbances.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
