Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load

Abstract

In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network.

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

“…BP (Backpropagation) Neural Network is a commonly used artificial neural network model widely applied to classification and regression problems [28]. It is a feedforward artificial neural network that learns by adjusting weights and biases through the backpropagation algorithm, gradually reducing prediction errors for model optimization and fitting.…”

Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”

“…The topics then transition to advanced energy management and control systems, exploring the integration of Internet of Things (IoT) and cloud computing for efficient demand-side management in smart grids, hybridization of Particle Swarm Optimization (PSO) for protective relays, and deep transfer learning for wind turbine fault diagnosis. Dai et al [7] introduced an intelligent model for predicting transformer losses, aiming to address the issue of low accuracy in calculating transformer losses under three-phase unbalanced loads. They optimized the prediction model, resulting in a highly accurate transformer loss prediction model.…”