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Improved Particle Filter for Non-Gaussian Forecasting-aided State Estimation

现代电力系统与清洁能源学报(英文) 2023年第4期11卷 1075-1085页

作者： lyuzerui yuan Jie Gu Honglin Wen Zhijian Jin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy.In this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distributions,is applied to formulate the non-Gaussian forecasting-aided state estimation problem.To address the problem,an improved particle filter is proposed,which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle filter.The AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation,through considering both state transitions and latest measurements.For rapid particle generation from the AGGD proposal function,an efficient inverse cumulative distribution function(CDF)sampling method is employed based on the derived approximation of inverse CDF of AGGD.Numerical simulations are carried out on a modified balanced IEEE 123-bus test system.The results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness,whether in Gaussian,non-Gaussian,or abnormal measurement errors.

关键词： State estimation particle filter asymmetric generalized Gaussian distribution non-Gaussian noise

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Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

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Protection and Control of Modern Power Systems 2023年第2期8卷 244-259页

作者： Yaojian Wang Jie Gu lyuzerui yuan Department of Electrical Engineering School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong University209 of Smart Grid Building800 Dong Chuan RdShanghai 200240China

Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

关键词： State estimation Pseudo measurement Recurrent neural network Attention mechanism Time-frequency domain analysis Distribution network

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Optimal Network Partition and Edge Server Placement for Distributed State Estimation

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1637-1647页

作者： lyuzerui yuan Jie Gu Jinghuan Ma Honglin Wen Zhijian Jin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation.A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment,operation,and maintenance,avoid the difference in the partition sizes,reduce the level of coupling between connected partitions,and maximize the inner cohesion of each partition.Capacities of edge server are constrained against underload and overload.To efficiently solve the problem,an improved non-dominated sorting genetic algorithm III(NSGA-III)is developed,with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence.Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs,and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution.The optimized result contributes significantly to real-time distributed state estimation.

关键词： Network partition edge server placement distributed state estimation edge computing non-dominated sorting genetic algorithm(NSGA)

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