Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Heuristic solution of a joint electric control reserve and wholesale market model

A mixed integer linear programming(MILP)approach for the joint simulation of electric control reserve and electricity wholesale markets is presented.This generation dispatch model extends an existing integrated grid and electricity market(IGEM)model covering the Continental European electric power system.By explicitly incorporating the markets for primary and secondary control reserves(PCR and SCR),the model can reproduce the decisions of generating unit operators on which markets get involved.Besides,the introduction of the integrality conditions allows considering start-up costs and the calculus of generating units to pass through the economically unattractive periods with low or even negative prices in order to avoid another start-up.Since this model is too large to be solved with common MILP solvers for the intended simulation time of one year,temporal and geographical interdependencies are used to solve it heuristically.The heuristic therefore splits the model into various sub-problems so that on the one hand,the number of variables,especially of integer variables,per sub-problem is reduced significantly and on the other hand,the relevant interdependencies remain considered.The heuristic is evaluated in terms of accuracy and computation time by means of two case studies.Both case studies show satisfactory accuracy and significant advantages in computation time.