Comparison of Results of Economic Load Dispatch Using Various Meta-Heuristic Techniques
Bibliographic record
Abstract
The power sector of India is in a huge catastrophe in satisfying the energy requirement of the public due to incessant exhaustion of fossil fuels.The nonstop exhaustion of fossil fuels, rising power needs and increasing production cost of power requires economic operation at the generation side and economic utilization at the consumer side.Economic dispatch is the process of determining the optimal power output from 'n' number of generators to meet the demand at low cost subject to certain constraints.Economic dispatch ensures the optimal generation of power at low cost from thermal power plants.The mathematical formulation of economic dispatch problems is usually done by the piecewise quadratic fitness function.This article compares the results generated from various techniques such as Lambda Iteration (LI) method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quantum Particle Swarm Optimization (QPSO) and Shuffled Frog Leaping Approach (SFLA).LI method is a traditional method of solving economic load dispatch which works on the concept of equal incremental cost (λ).GA works on Darwin's theory of evolution, where the population of individual solutions is modified repeatedly to obtain the optimal solution in the population.PSO is derived from the concept of swarm intelligence, where the best solution is found using the values of personal best and global best in the population.QPSO is basically derived from the PSO.SLFA is obtained from the concept of food-frogs used to find an accurate solution to our power system problem.In this paper, the best fuel cost and execution time was found from QPSO, SFLA compared with LI, GA and PSO methods.These approaches are applied for three and thirteen generator system and the convergence characteristics, heftiness was explored through comparisons from different approaches discussed earlier.The results are hopeful and it suggests that shuffled frog leaping algorithm is very effectual in terms of both the minimized fuel cost obtained and the execution time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".