A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms

Abstract

In order to reduce the negative impact of the large-scale grid connection of residential photovoltaic (PV) equipment on the distribution network, it is of great significance to realize the real-time accurate identification of the grid connection state and its switching of residential PV equipment from the distribution network side. This paper introduces a non-intrusive method for identifying residential PV systems using transient features, leveraging the temporal convolutional network (TCN) model with attention mechanisms. Firstly, the discrimination and redundancy of transient features for residential PV devices are measured using a feature selection method based on the semi-Fisher score and maximal information coefficient (MIC). This enables the construction of a subset of identification features that best characterize the PV devices. Subsequently, a sliding window two-sided cumulative sum (CUSUM) event detection algorithm, incorporating a time threshold, is proposed for the real-time capturing of PV state switching and grid connection behavioral events. This algorithm effectively filters out disturbances caused by the on/off cycles of low-power residential devices and captures the transient time windows of PV behaviors accurately. On this basis, a TCN model with attention mechanisms is proposed to match the discerned event features by assigning varying weights to different types of characteristics, thereby facilitating the precise recognition of a PV grid connection and state-switching events. Finally, the proposed method is validated on a custom-designed non-intrusive experimental platform, demonstrating its precision and real-time efficiency in practical applications.