Implementation Plan:
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Step 1: Initially, we construct a hydropower system model in simulink.
Step 2: Then, we collect simulated data and identify faults using the virtual twin algorithm .
Step 3: Next, we preprocess the data using the Seasonal Trend Decomposition method and extract the features using multi-scale feature extraction network methods.
Step 4: Next, we detect anomalies using Hybrid VMD-HTM-LSTM fusion technique for signal decomposition and deep learning to detect abnormal behaviours based on collected data.
Step 5: Then, we mitigate the anomalies using Adaptive Fault‑Tolerant Control algorithm based on collected data.
Step 6: Next, we implement Combined Tailings Storage Facility based predictive Analytic System for efficient predictive analytics based on collected data.
Step 7: Finsally, we plot performance for the following metrics
7.1: Number of Epochs vs. Precision (%)
7.2: Number of Epochs vs. Root Mean Square Error (RMSE)
7.3: Number of Epochs vs. Mean Absolute Error (%)
7.4: Number of Epochs vs. Fault Detection Rate (%)
7.5: Number of Epochs vs. Detection (%)
7.6: Number of Epochs vs. False Alarm Rate (%)
Software requirement
1. Development Tool: MatlabR2023a/Simulink or above
2. Operating System: Windows-10 (64-bit) or above
Note:
1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.
2) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only.
We perform with an Existing Approach: Ref-4 : Title:-A multi-model predictive framework for unsupervised anomaly detection in univariate time series data from hydraulic turbine units
