| Autor: |
Shariq Shaikh, Adeel Arif, Muhammad Mohsin Aman |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Engineering Proceedings, Vol 46, Iss 1, p 25 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4591 |
| DOI: |
10.3390/engproc2023046025 |
| Popis: |
Technical losses in transmission systems are crucial for short-term planning and decision-making, particularly in complex systems. Traditional deterministic methods like Newton–Raphson prove ineffective in handling systems with a large number of buses and intricate topologies. Consequently, there is a growing interest in employing heuristic and intelligent algorithms to achieve faster and more accurate technical loss estimations. This paper introduces the Neural Network Prognosis Algorithm (NNPA), a simple and robust technique that exclusively relies on technical parameters derived from a Newton–Raphson-based load flow analysis, specifically active and reactive power, for 22 buses. The algorithm demonstrates promising progress, exhibiting notable convergence toward the desired mean squared error and achieving a commendable correlation coefficient of 0.999. Despite incorporating randomized data points with Gaussian noise, the algorithm’s results present compelling evidence of its effectiveness and validation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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