Data
grid_stability

grid_stability

active ARFF CC BY 4.0 Visibility: public Uploaded 22-12-2022 by Sebastian Fischer
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  • Machine Learning Physical Sciences study_353
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Data Description The local stability analysis of the 4-node star system (electricity producer is in the center) implementing Decentral Smart Grid Control concept was performed. This dataset contains simulations regarding electrical grid stability. The model is composed of a generator model and an economic model. The analysis is performed for different sets of input values. Several input values are kept the same: averaging time - 2s, coupling strength - 8s^-2, damping - 0.1s^-1. The goal is to estimate the stability of the system. Attribute Description 14 features describing the system: 1. *tau[1-4]* - reaction time of participant (real from the range [0.5,10]s), tau1 - value for electricity producer 2. *p[1-4]* - nominal power consumed(negative) / produced(positive)(real). For consumers from the range [-0.5,-2]s^-2; p1 = abs(p2 + p3 + p4) 3. *g[1-4]* - coefficient (gamma) proportional to price elasticity (real from the range [0.05,1]s^-1), g1 - the value for electricity producer 4. *stab* - the maximal real part of the characteristic equation root (if positive - the system is linearly unstable), target feature 5. *stabf* - the stability label of the system (categorical: stable/unstable), alternate target feature for a classification task

13 features

stab (target)numeric10000 unique values
0 missing
tau1numeric10000 unique values
0 missing
tau2numeric10000 unique values
0 missing
tau3numeric10000 unique values
0 missing
tau4numeric10000 unique values
0 missing
p1numeric10000 unique values
0 missing
p2numeric10000 unique values
0 missing
p3numeric10000 unique values
0 missing
p4numeric10000 unique values
0 missing
g1numeric10000 unique values
0 missing
g2numeric10000 unique values
0 missing
g3numeric10000 unique values
0 missing
g4numeric10000 unique values
0 missing
stabf (ignore)string2 unique values
0 missing

19 properties

10000
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
13
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.96
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: stab
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