Figures index

From

Neuro-Fuzzy Logic Applications for Grid Energy Management

Cooper R. Wade, Daniel Kelly-B Danquah, Hossein Salehfar, Olusegun S. Tomomewo

American Journal of Systems and Software. 2023, 6(1), 1-10 doi:10.12691/ajss-6-1-1
  • Figure 1. Illustration of how neuro-fuzzy systems work indicating X-input layer, W-weight-hidden layer, and Z output layer
  • Figure 2. Illustration of neuro-fuzzy network data processing
  • Figure 3. Illustration of conventional power flow on the electric grid
  • Figure 4. Block diagram of a conventional PID controller for grid energy management
  • Figure 5. Illustration is a representation of a simple distribution grid indicated in a one-line diagram style showing substations A,B,C,D,E, and F with varying distribution circuits with either DER-G (DER generation source) and combinations of Load R (residential load), C (commercial load), and I (industrial load)
  • Figure 6. Illustration is a representation of a simple transmission grid indicated in a one-line diagram style showing substations A,B,C,D,E, and F interconnected with a regional sub-transmission network, with three external transmission feeds from other power generation resources labeled with “OTHER TL”
  • Figure 7. Illustration is a representation of the combination of Figures V and VI of a one-line diagram style showing substations A,B,C,D,E, and F with varying distribution circuits with either DER-G (DER generation source) and combinations of Load R (residential load), C (commercial load), and I (industrial load)
  • Figure 8. Illustration is a representation of a simple distribution grid indicated in a one-line diagram style showing substations A,B,C,D,E, and F with varying distribution circuits with either DER-G (DER generation source) and combinations of Load R (residential load), C (commercial load), and I (industrial load)