A Disturbance Localization Method for Power System Based on Group Sparse Representation and Entropy Weight Method

This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method.Three different electrical quantities are selected as observations in the compressed sensing algorithm.The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels.Subsequently,by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,an improved Joint Generalized Orthogonal Matching Pursuit(J-GOMP)algorithm is utilized for reconstruction.The reconstructed sparse vectors are divided into three parts.If at least two parts have consistent node identifiers,the node is identified as the disturbance node.If the node identifiers in all three parts are inconsistent,further analysis is conducted considering the weights to determine the disturbance node.Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method,utilizing electrical quantity information from only 8 measurement points,effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.

A Disturbance Source Location Method on the Low Frequency Oscillation With Time-varying Steady-state Points

The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods have poor adaptability to the low frequency oscillation with time-varying steady-state points because of the limitations in the location criterion derivation.A disturbance source location method on a low frequency oscillation with good generality is presented in the paper.Firstly,the reasons why the steady-state points are time-varying on a low frequency oscillation are analyzed.Then,based on the energy function construction form,the branch transmission energy is decomposed into state energy,reciprocating energy and dissipation energy by mathematical derivation.The flow direction of the dissipation energy shows the source and destination of the disturbance energy,and the specific location of a disturbance source can be identified according to its flow direction.Meanwhile,to meet the needs of energy calculation,a recognition method on the electrical quantities steady-state points is also presented by using the cubic spline interpolation.Simulation results show the correctness of the derivation and analysis on energy structure in the paper,and the disturbance source can be located accurately according to the dissipation energy.

Multiple Impact Factor Based Accuracy Analysis for Power Quality Disturbance Detection

Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.