![]() ![]() A Parameter Set has an array with field names and a single array with field values. The RAWX files contain two types of data objects: Parameter Sets and Data Tables. It will be the standard text-based data format for PSS®E power flow data exchange. In PSS®E version 35 a new RAWX file format (Extensible Power Flow Data File) based on JSON has been introduced. As many of the data items specified in the RAW file have a default value only the specific information needed should be defined in the record. The last record of each data block is a record specifying a value of zero to indicate the end of the category.Įach record in a data block contains a set of data items separated by a comma or one or more blanks where alphanumeric attributes must be enclosed in single quotes. The RAW file has multiple groups of records (data blocks), with each group containing a particular type of data needed in power flow. A PSS®E RAW file contains a collection of unprocessed data that specifies a Bus/Branch network model for the establishment of a power flow working case. One of them is the RAW file (power flow data file). PSS®E uses different types of files to exchange data about the network. Raw data generator software#We conclude that the proven feasibility of FL in our simulated distributed setting lays the groundwork for utilising this approach in realistic environments of grander scale while overcoming potential privacy concerns or logistical challenges in the setting of centralised analytics.PSS®E software from Siemens provides analysis functions for power system networks in steady-state and dynamic conditions. We compare the resulting training outcomes with the centralised model training (CL) approach and find CIIL performed similarly to CL but less stable, while FL outperformed CL by 7.5%. Raw data generator generator#We introduce a rainfall generator training procedure relying on Generative Adversarial Networks (GANs) and evaluate two DA algorithms: Federated Learning (FL) and Cyclic Institutional Incremental Learning (CIIL). As example of use, we choose the decentralised training of rainfall data generators. In this work, we propose a feasibility study evaluating the applicability of DA on hydrological data. Distributed Analytics (DA) aims to overcome these challenges through decentralised model training by bringing the algorithm to the data instead of vice versa. ![]() However, data centralisation entails challenges regarding data-stream logistics, data locality, and memory overhead. Capturing processes for rainfall data are often highly distributed, with multiple radar stations contributing to a centralised data set. Such synthetic data instances can be produced by precipitation generators trained in an adversarial setting on historical rainfall data. Newly introduced ML-based flood forecasting methods rely on high-intensity synthetic rainfall events due to the sparsity of their real counterpart. Recent heavy rainfall-induced flood events, for example in Germany, Australia and USA, have highlighted the relevance of countermeasures in saving human lives and preventing property damage. ![]()
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