Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators

Pseudocode 2

Pseudocode for DE-IDWNN controller.

Start
Phase I:
Initialize the IDWNN weights, learning rate and inertia factor.
Randomly generate weight values.
Present the input samples to the network model.
Compute net input of the network considered.
Apply activations to compute output of calculated net input.
Perform the above process for hidden layer to hidden layer and hidden layer to output layer.
Finally compute the output from the output layer.
Perform weight updation till stopping condition met.
Phase II:
Present the computed outputs from IDWNN model into DE.
Invoke DE
Perform Mutation
Perform Crossover
Perform Selection
Do carry out generations until fitness criteria met.
Note the points at which best fitness is achieved.
Present the points of best fitness back to IDWNN.
Phase III:
Invoke IDWNN with the inputs from the output of Phase II
Tune the weights to this IDWNN employing DE
Invoke DE
Perform Phase I process for tuned weights of DE.
Continue Phase II.
Repeat until stopping condition met (Stopping conditions can be number of iterations/generations or
reaching the optimal fitness value)
Stop