Modeling and Simulink of prediction and prevention of faults in a multi source smart electrical grid
Implementation plan:
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First version :- Using fuzzy logic (GAN-GDA-FLFD) for fault prediction and detection
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Scenario 1:(photovoltaic source )
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Step 1: Initially, We construct a smart electrical grid with 14 Bus,PV and BESS in Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 2:(Wind Power source)
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Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and wind power source in Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 3:(Diesel source)
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Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and Diesel source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 4:(photovoltaic source with Wind Powersource)
***************************************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Wind power source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 5:(photovoltaic source with Diesel source)
*********************************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Diesel source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 6:(photovoltaic source with Diesel source and Wind source )
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Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Diesel and wind source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Second version :- using network reconfiguration, backup sources, and load management
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Scenario 1:(photovoltaic source )
************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,PV and BESS in Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 2:(Wind Power source)
************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and wind power source in Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 3:(Diesel source)
******************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and Diesel source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 4:(photovoltaic source with Wind Powersource)
***************************************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Wind power source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 5:(photovoltaic source with Diesel source)
********************************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Diesel source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Scenario 6:(photovoltaic source with Diesel source and Wind source )
**************************************************************************
Step 1: Initially, We construct a smart electrical grid with 14 Bus,BESS and PV with Diesel and wind source Simulink Model
Step 2: Then, we collect voltage, current, power and temperatures data and preprocess using noise reduction and normalization techniques.
Step 3: Next, we monitor the current leakage using Particle Swarm Optimization and Genetic Algorithm (PSO-GA).
Step 4: Next, we predict the faults using IF-SVM with Blue Whale (BWO) Optimization technique for efficient prediction.
Step 5: Next, we prevent the faults using Gaussian Discriminant Analysis and Fuzzy Logic Based Fault Detection (GAN-GDA-FLFD) method.
Step 6: Next, we optimize the data using Stackelberg game-theoretic framework with Guide-Waterwheel Plant Algorithm (SDTF-Guide-WWPA).
Step 7: Next, we implement Primal-Dual and Distributed Averaging Proportional-Integral (PD-DAPI) protocols to control current based regulation.
Step 8: Finally, we plot performance metrics for the following
8.1: Time vs Current (A)
8.2: Time vs Voltage (V)
8.3: Time vs SOC (%)
8.4: Time vs Fault prediction (%)
8.5: Number of Epochs/Iteration vs Loss (%)
Software Requirements:
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1. Development Tool: Matlab-R2023a/Simulink or above
2. Operating System: Windows-10 (64-bit) or above
Note:
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
We perform with an existing approach Reference 4 :- Title: A novel approach to predicting the stability of the smart grid utilizing MLP-ELM technique
