Power quality composite disturbance deep feature extraction and classification based on SCG optimized SSAE-FFNN

Abstract

With the development of the smart grid, power quality issues have been widespread in the power grid and it threaten the safety and stability of the power grid. The monitoring data of power quality disturbances (PQDs) increase rapidly, and it is of great significance to achieve deep feature extraction and intelligent classification of PQDs in large-scale systems for power system pollution detection and management. To this end, stacked sparse auto encoder (SSAE) and feedforward neural network (FFNN) based method for composite PQDs classification is proposed in this paper. Firstly, a PQDs simulation model is constructed based on IEEE standard. Then, a PQDs classification model based on SSAE-FFNN is established, and the scaled conjugate gradient (SCG) algorithm is used to optimize the model, in order to accelerate gradient descent and improve training efficiency. Next, to reduce the reconstruction loss of the stacked network and extract deep low-dimensional features, the layer-wise training and fine-tuning strategy of SSAE are constructed. Finally, the examples are used to verify the classification effect, robustness, generalization and applicable scenario scale of the proposed method. The results show that the method can effectively identify composite PQDs and it has a high accuracy even for both error-containing disturbances and 21 sets of measured disturbance data of a local municipal grid.

Keywords

• power quality
• composite disturbance classification
• stacked sparse auto encoder (ssae)
• deep feature extraction
• scaled conjugate gradient (scg)
• feedforward neural network (ffnn)