# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
This is then subjected to adaptive adjustment through the utilization of tree node search methods and node path algorithms. Study [7] further proposes the integration of machine learning into the adaptive protection scheme, thereby leveraging the vast datasets inherent to intelligent microgrids. In a further contribution to the field, study [8] puts forward an adaptive positive sequence current interruption protection scheme for IBDG-containing distribution networks, which represents a significant enhancement in terms of protection performance. Study [9] proposes a new adaptive overcurrent protection scheme. This scheme is based on the amount of current mutation to address the impact of load self-starting on overcurrent protection in distribution networks. It is particularly aimed at the period following the removal of external faults along the line. Study [10] employs artificial intelligence to enhance protection, although this may be susceptible to power system oscillations. In study [11], the suggestion is made that a convolutional neural network (CNN) and a long short-term memory (LSTM) unit should be used to create a deep learning model for the purpose of identifying power system faults and making intelligent decisions. In study [6], the suggestion is made that an LSTM should be used for processing time series data in the power system. This results in enhanced protection system sensitivity, reliability, and stability. Nevertheless, the deep learning model has yet to achieve complete adaptation to the dynamics and nonlinear characteristics inherent to the power system. In study [12], adaptive technology is employed to enhance the efficacy of power system relay protection. The protection device is made more adaptable and capable of handling faults through the utilization of switching information, real-time information, substation-integrated information, and remote information. However, the information in question must be provided in a timely and reliable manner. The literature proposes a novel three-phase inverter for photovoltaic (PV) systems and employs energy-efficient control methodologies to mitigate common mode voltage. Study [13] presents a graphical representation of the distribution network protection control system, based on dynamic topology analysis, which offers a solution to enhance the adaptive capability of protection control. In their study, study [14] proposed an adaptive protection scheme based on the principle of directional blocking. This scheme has been demonstrated to meet the requisite standards for protection in radial active distribution networks. Nevertheless, the majority of studies focus on the control strategy of IIDG and the macroscopic impact of the grid, with relatively few investigations dedicated to the fault current characteristics and their influence on the sensitivity of current protection. In particular, the real-time output parameters of IIDGs must be combined in order to enable the dynamic adjustment of protection setting values and enhance the selectivity and sensitivity of protection in practical engineering applications. This is a pressing issue that requires immediate attention. In order to address the aforementioned issues, this study proposes an adaptive current protection optimization strategy based on the real-time output parameters of the IIDG for dynamic adjustment. The remainder of the paper is structured as follows: Section 2 presents a comprehensive overview of the power model for inverter-based distributed generation (IIDG), elucidating the intricacies of the system design and control scheme. Section 3 provides an analysis of the characteristics of high-capacity distributed power and its impact on the connection to the urban distribution grid. Section 4 will examine the particular impact of IIDG access to the distribution grid on current protection sensitivity, including the issues of time-limited current interruption protection and fixed-time overcurrent protection.