Design of Multi-Renewable Energy Storage and Management System Using RL-ICSO Based MPPT Scheme for Electric Vehicles
Bibliographic record
Abstract
Nowadays, traditional power systems are being developed as an emergence for the use of smart grids that cover the integration of multi-renewable energy sources with power electronics converters. Efforts were made to design power quality controllers for multi-renewable energy systems (photovoltaic (PV), Fuel Cell and Battery) to meet huge energy demands. Though there have been several techniques employed so far, the power quality issue is a major concern. In this paper, a multi-objective optimal energy management system for electric vehicles (EVs) is proposed using a reinforcement learning mechanism. Furthermore, the maximum power point tracking (MPPT)-based Reinforcement Learning-Iterative cuckoo search optimization algorithm (RL-ICSO) along with the Proportional Integral Derivative (PID) controller is incorporated. For this, a renewable energy source is considered as input for eliminating voltage and current harmonics. Similarly, a DC to AC inverter using a Model Predictive Control (MPC) controller-based pulse generation process was carried out to incorporate the power quality compensation of multi-renewable energy microgrid harmonics in three-phase systems. The generated energy is checked for any liabilities by adding a fault in the transmission line and thereby rectifying the fault by means of the Unified Power Quality Controller (UPQC) device. Thus, the fault-rectified power is stored in the grid, and the transmitting power can be used for EV charging purposes. Thus, the energy storage system is effective in charging and storing the needed power for EVs. The performance estimation is carried out by estimating the simulation outcome on Total Harmonic Distortion (THD) values, parameters, load current and voltage. In addition, the performance estimation is employed, and the outcomes attained are represented. The analysis depicts the effectiveness of the power and energy management ability of the proposed approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".