chapttertertertetertgergbxdf hh r-1.pptx
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The document discusses a research study on optimizing and applying predictive maintenance to energy storage systems using deep learning and sensor data. It outlines the objectives of developing robust deep learning models to predict failures, optimizing performance, and enhancing maintenance frameworks, while reviewing existing literature and methodologies. Limitations include data quality issues and limited real-world testing, but the findings indicate significant improvements in system performance and cost reduction through predictive maintenance.
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chapttertertertetertgergbxdf hh r-1.pptx
- 1. Optimization And Predictive Maintenance of Energy Storage Systems for Renewable Energy Using Sensor Data and Deep Learning Presented by Mr Rajesh R Waghulde ( Research Scholar ) Guided By : Dr Vijeta Yadav Madhyanchal Professional University, BHOPAL ( M.P. ) India Co- Guide : Dr Milind Rane Department of E & TCVishwakarma Institute of Technology, Pune, India
- 2. Traditional Vs. Predictive Maintenance Method
- 3. Objectives of the Research • The objectives of this research are to: 1. Develop robust deep learning models that can analyze sensor data to detect anomalies and predict failures in energy storage systems. 2. Optimize energy storage system performance by identifying key operational parameters and implementing real-time control strategies. 3. Enhance predictive maintenance frameworks by integrating sensor data with advanced analytics to forecast maintenance needs and prevent unexpected failures.
- 4. Literature Review Study Methodology Dataset Used Limitations Results Wang et al., 2020 Applied deep learning models (LSTM) for predicting the Remaining Useful Life (RUL) of lithium-ion batteries. Data from real-world lithium-ion battery operation, including voltage, current, and temperature. Limited to a specific battery type (Li-ion), doesn't generalize to other ESS technologies. Achieved accurate RUL predictions, improving maintenance schedules and reducing unexpected failures. Zhang et al., 2021 Utilized deep reinforcement learning (DRL) to optimize charging/discharging cycles of ESS. Simulation data for ESS connected to a renewable energy grid. The model was tested in simulation rather than real-world scenarios, limiting the practical applicability. Optimized the charging and discharging cycle, reducing operational costs and improving energy utilization. Wang, S., et al., 2019 Machine learning techniques (SVM, Random Forest) for predictive maintenance. Data from ESS sensors including voltage, SOC, and temperature. The dataset had a limited number of faults, limiting the model's ability to detect rare failures. Successfully predicted failures, achieving better maintenance efficiency compared to traditional methods. Tan et al., 2020 Real-time monitoring system integrated with machine learning models for predictive maintenance. Data from a real-time monitoring system of ESS with temperature, current, and voltage sensors. Inconsistent data quality and potential sensor errors could affect prediction accuracy. Improved predictive maintenance capabilities, reducing downtime and maintenance costs. Zhang, Y., et al., 2020 Applied CNN and LSTM models to predict battery degradation and failures. Data from battery tests, including temperature, voltage, and charge/discharge cycles. Limited dataset size, and environmental factors were not fully accounted for. Successfully predicted battery degradation and extended battery life by optimizing maintenance schedules.
- 5. Literature Review Study Methodology Dataset Used Limitations Results Lee et al., 2021 Cloud-based predictive maintenance system using ensemble machine learning techniques. Real-time data from ESS including temperature, voltage, and SOC. Dependency on cloud infrastructure and potential latency issues. Significant improvements in predictive maintenance and operational reliability with reduced downtime. Raj et al., 2020 Real-time predictive maintenance using machine learning algorithms (KNN, Random Forest). Data from a large-scale ESS deployment, including voltage and temperature sensors. High computational overhead and complexity, limiting scalability. Achieved real-time predictive maintenance with reduced maintenance costs and system downtime. Xiao et al., 2021 DRL for optimization of ESS operation to balance performance and cost. Simulated ESS data linked to renewable energy sources. Simulation results may not reflect real-world variability and uncertainties. Optimized ESS charging/discharging cycles, improving energy efficiency and reducing operational costs. Huang et al., 2020 Big data analytics for predictive maintenance using advanced machine learning techniques. Large-scale sensor data from multiple ESS deployments. The model's dependence on high-quality and large datasets can be a limitation in real-world applications. Improved predictive accuracy for ESS failures, though challenges remain with data quality and model generalization.
- 6. Literature Review Methodology: Most studies focus on applying deep learning techniques (such as LSTM, CNN, DRL) combined with traditional machine learning methods (e.g., SVM, Random Forest) for predictive maintenance and optimization. Dataset: Datasets typically consist of real-time sensor data from ESS (voltage, current, temperature, SOC). However, many studies use simulated data, which may limit the generalization of the results. Limitations: Common limitations include small sample sizes, lack of real-world testing, high computational overhead, and issues with data quality. Results: The results across studies show that predictive maintenance models can significantly improve ESS performance, reduce operational costs, and extend system lifespan. However, challenges like real-time implementation, model generalization, and data quality remain.