RBF neural network-based probability power flow online calculation method
A probabilistic power flow, neural network technology, applied in the field of power system and its automation, can solve problems such as speeding up the power flow solution speed, difficult online analysis, etc., to achieve the effect of high-precision online calculation, improved accuracy, and good parallelism
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Embodiment 1
[0065] A probabilistic power flow online calculation method based on RBF neural network mainly includes the following steps:
[0066] 1) Establish the RBF neural network probability power flow model.
[0067] Further, an RBF neural network probability power flow model mainly includes an input layer, a hidden layer and an output layer.
[0068] The data in the input vector X of the input layer mainly includes active power and reactive power of all new energy nodes and load nodes in the power system.
[0069] The hidden layer uses RBF Kernel to make nonlinear transformation on the input, so that the output layer can train the linear classifier.
[0070] The data in the output vector y of the output layer mainly includes power flow solvability, node voltage active power, node voltage reactive power, branch active power and branch reactive power.
[0071] The number of nodes in the input layer is set to N. The number of nodes in the hidden layer is I. The nodes of the output l...
Embodiment 2
[0124] An experiment for calculating the probability power flow of a power system using an RBF neural network-based probabilistic power flow online calculation method mainly includes the following steps:
[0125] 1) Establish the RBF neural network probability power flow model.
[0126] 2) Obtain the training samples x of the RBF neural network probabilistic power flow model by monitoring the power system in real time, simulating and experimenting the power system, record the power flow values of all training samples x, and mark the unsolvable training samples of the power flow.
[0127]The basic data of the system in this embodiment refers to the IEEE118 standard system. It is assumed that the random characteristics of the loads of each node obey the normal distribution, and its standard deviation is 10% of the expected value of the load of each node; the wind speed obeys the two-parameter Weibull distribution, and the scale parameter is 2.016 , with a shape parameter of 5....
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