Research On Equivalent Method Of Generator Based On Data Drive

Under the guidance of the 14 th Five-Year Plan and the "carbon neutral" strategy,the proportion of clean energy power generation will continue to increase year by year.For power systems with large-scale new energy sources,it is time-consuming to carry out complete electromagnetic transient simulation modeling.Dynamic equivalence technology is used to effectively simplify the scale of the power system and reduce the number of generators,which is an effective method for safety and stability analysis of large systems.Generator aggregation is the core steps of power system dynamic equivalence.It refers to synchronous generator grouping and then parameter aggregation,so as to obtain parameters of the equivalence model.However,in the context of the rapid development of wind power,the traditional equivalent work with synchronous generators as the core also urgently needs to add new content to adapt to the development trend of the power supply side.The data-driven concept provides a new idea for solving generator equivalence,that is,there is no need to establish a detailed physical model,through data mining,the use of deep learning algorithms and data analysis methods to build an equivalent model to approximate the real model.Based on the above concepts,this article expands the framework of the traditional generator equivalence method,and starts the equivalence study of synchronous generators and wind turbines from two steps: grouping and parameter aggregation.For synchronous generators,considering the use of deep learning algorithms to distinguish the coherence of generators based on the data under disturbance.Parameter aggregation aims at keeping the node data consistent,and continuously fitting parameters of the equivalence model in the time domain.For wind turbines,physical quantities that can characterize their dynamic characteristics are derived,and their similarities are quantified by data analysis methods and used for grouping.Equivalent wind turbine models aggregate parameters with output power as the weight.Compared with the traditional method of generator aggregation and equivalent,the data-driven method proposed in this article has advantages in speed,repeatability and accuracy.Based on the above research ideas,this article mainly completed the following tasks:(1)A data-driven three-stage clustering method for synchronous generators is proposed.First,using the historical data to obtain practical power grid topology and quickly complete generator pre-grouping based on constraints such as geographic location and separation of thermal power and hydropower generators.Then,a method for secondary grouping of generators based on Convolutional Neural Network(CNN)is proposed,which takes pregrouped coherent generator fault information as input features and group number as a label to train the CNN model.After training,the CNN model is used to predict the groups of generators.If the threshold requirement is met,it will be included in the existing group.Finally,the remaining generators are clustered using the K-means algorithm,and the Gap statistic algorithm is used to determine the optimal number of clusters.The obtained reasonable coherent grouping results lay a solid foundation for subsequent parameters aggregation and network simplification.(2)A time-domain-based electromagnetic loop parameters identification method and the controller alternate optimization method are proposed.This paper proposes a parameter identification method based on the time domain of the equivalent machine based on the above grouping.The input and output consistency of the boundary node is the identification target.This method can better maintain the characteristics of boundary nodes and meet the requirements of dynamic equivalence.At the same time,this paper proposes an aggregation method of alternately optimizing sensitive parameters and nonsensitive parameters of the equivalent generator controller.First,the trajectory sensitivity is used to distinguish the sensitive parameters and the non-sensitive parameters.The sensitive parameters are optimized using the improved particle swarm optimization algorithm with the dominant generator parameters as the initial value,and then the non-sensitive parameters are optimized.The obtained controller parameters have higher accuracy and better maintain the dynamic characteristics of the original system.(3)The equivalent method of doubly-fed wind farm based on equivalent power angle and power weight is proposed.This paper proposes a data analysis method based on Equivalent Power Angle(EPA)for the grouping of doubly-fed wind farms,and uses the similarity of EPA as the basis for grouping.EPA is derived from the physical model of the doubly-fed wind turbine,which can reflect the operating characteristics of different operating conditions under various control modes,that is,it can characterize the dynamic characteristics of the doubly-fed wind turbine and can be used as a criterion for coherence.A data analysis method based on the Prony algorithm is proposed to measure the EPA value of each wind farm.When the EPA similarity between the wind farms meets the threshold requirement,the equivalent aggregation can be performed.Finally,an algorithm based on power weighting is proposed for the aggregation of equivalent wind turbine parameters,which can effectively maintain the dynamic characteristics of large wind farm and is closer to the original system characteristics.Finally,using the actual China Southern Power Grid data as an example,the above method is used to complete the equivalence of all synchronous generators and wind farms in the power system.Compared with the traditional generator equivalence method,the methods in this paper are effective and accurate.This paper introduces the concept of data-driven and carries out research on generator aggregation methods.It solves the problems of reliance on physical models,long time-consuming and low accuracy in generator coherent grouping and parameter aggregation,which satisfies the requirements of dynamic equivalence in practical AC and DC power systems.