Journal: Scientific reports

Article Title: Novel technique for precise derating torque of induction motors using ANFIS.

doi: 10.1038/s41598-025-92821-z

Figure Lengend Snippet: Fig. 9. Configuration of ANFIS Controller main parts in Simulink environment.

Article Snippet: Configuration of ANFIS Controller main parts in Simulink environment.

Techniques:

Journal: Ain Shams Engineering Journal

Article Title: NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

doi: 10.1016/j.asej.2017.05.006

Figure Lengend Snippet: Fig. 4. Membership functions and value gaps of CF1 and CFac of the input variables of ANFIS. (a) Input membership functions, CF1 and (b) input membership functions, CFac.

Article Snippet: The basic steps for the ANFIS controller design in MATLAB/ Simulink environment (Fig. 1) are mentioned below [6,27] (1) Model and simulate the test system with simulink and fuzzy logic controller with the given rule base. (2) Collect the training data while simulating the model with fuzzy logic controller. (3) The two inputs, i.e., CF1 and CFac and the output signal provides the training data. (4) Use anfisedit to generate the ANFIS .fis file. (5) Arrange the training data collected in Step 2 and generate the FIS with Gaussian membership function. (6) Trains the collected data with the generated FIS up to a par- ticular no. of Epochs. (7) Save the FIS.

Techniques:

Journal: Ain Shams Engineering Journal

Article Title: NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

doi: 10.1016/j.asej.2017.05.006

Figure Lengend Snippet: Fig. 11. Excess raise/lower signals of GENCO-1 with three different controllers. (a) Speed changer raise/lower signals of GENCO-1 with three different controllers. (b) Excess raise/lower signals of GENCO-1 of PID controller over NERC standards-based ANFIS controller. (c) Excess raise/lower signals of GENCO-1 of ANFIS controller over NERC standards-based ANFIS controller.

Article Snippet: The basic steps for the ANFIS controller design in MATLAB/ Simulink environment (Fig. 1) are mentioned below [6,27] (1) Model and simulate the test system with simulink and fuzzy logic controller with the given rule base. (2) Collect the training data while simulating the model with fuzzy logic controller. (3) The two inputs, i.e., CF1 and CFac and the output signal provides the training data. (4) Use anfisedit to generate the ANFIS .fis file. (5) Arrange the training data collected in Step 2 and generate the FIS with Gaussian membership function. (6) Trains the collected data with the generated FIS up to a par- ticular no. of Epochs. (7) Save the FIS.

Techniques:

Journal: Ain Shams Engineering Journal

Article Title: NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

doi: 10.1016/j.asej.2017.05.006

Figure Lengend Snippet: Fig. 12. Excess raise/lower signals of Genco-2 with three different controllers. (a) Speed changer raise/lower signals of GENCO-2 with three different controllers. (b) Excess raise/lower signals of GENCO-2 of PID controller over NERC standards-based ANFIS controller. (c) Excess raise/lower signals of GENCO-2 of ANFIS controller over NERC standards-based ANFIS controller.

Article Snippet: The basic steps for the ANFIS controller design in MATLAB/ Simulink environment (Fig. 1) are mentioned below [6,27] (1) Model and simulate the test system with simulink and fuzzy logic controller with the given rule base. (2) Collect the training data while simulating the model with fuzzy logic controller. (3) The two inputs, i.e., CF1 and CFac and the output signal provides the training data. (4) Use anfisedit to generate the ANFIS .fis file. (5) Arrange the training data collected in Step 2 and generate the FIS with Gaussian membership function. (6) Trains the collected data with the generated FIS up to a par- ticular no. of Epochs. (7) Save the FIS.

Techniques:

Journal: Ain Shams Engineering Journal

Article Title: NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

doi: 10.1016/j.asej.2017.05.006

Figure Lengend Snippet: Fig. 13. Excess raise/lower signals of GENCO-3 with three different controllers. (a) Speed changer raise/lower signals of GENCO-3 with three different controllers. (b) Excess raise/lower signals of GENCO-3 of PID controller over NERC standards-based ANFIS controller. (c) Excess raise/lower signals of GENCO-3 of ANFIS controller over NERC standards-based ANFIS controller.

Article Snippet: The basic steps for the ANFIS controller design in MATLAB/ Simulink environment (Fig. 1) are mentioned below [6,27] (1) Model and simulate the test system with simulink and fuzzy logic controller with the given rule base. (2) Collect the training data while simulating the model with fuzzy logic controller. (3) The two inputs, i.e., CF1 and CFac and the output signal provides the training data. (4) Use anfisedit to generate the ANFIS .fis file. (5) Arrange the training data collected in Step 2 and generate the FIS with Gaussian membership function. (6) Trains the collected data with the generated FIS up to a par- ticular no. of Epochs. (7) Save the FIS.

Techniques:

Journal: Ain Shams Engineering Journal

Article Title: NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

doi: 10.1016/j.asej.2017.05.006

Figure Lengend Snippet: Fig. 14. Excess raise/lower signals of GENCO-4 with three different controllers. (a) Speed changer raise/lower signals of GENCO-4 with three different controllers. (b) Excess raise/lower signals of GENCO-4 of PID controller over NERC standards-based ANFIS controller. (c) Excess raise/lower signals of GENCO-4 of ANFIS controller over NERC standards-based ANFIS controller.

Article Snippet: The basic steps for the ANFIS controller design in MATLAB/ Simulink environment (Fig. 1) are mentioned below [6,27] (1) Model and simulate the test system with simulink and fuzzy logic controller with the given rule base. (2) Collect the training data while simulating the model with fuzzy logic controller. (3) The two inputs, i.e., CF1 and CFac and the output signal provides the training data. (4) Use anfisedit to generate the ANFIS .fis file. (5) Arrange the training data collected in Step 2 and generate the FIS with Gaussian membership function. (6) Trains the collected data with the generated FIS up to a par- ticular no. of Epochs. (7) Save the FIS.

Techniques: