光伏/风电混合供电智能负荷管理系统

Syafii, Muhardika, Darwison, Witri Onanda
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引用次数: 1

摘要

光伏发电和风力发电在利用环保技术从大自然中收集能量的过程中可以解决未来的电力能源危机,使它们成为最发达和可靠的替代方案。然而,太阳能/风能的转换高度依赖于阳光和风速的可用性。因此,有必要对以增加和保持对负荷供电连续性为目标的光伏/风负荷进行研究。通过考虑剩余的可用电池电压,负载电源管理遵循晴天、阴天、雨天或傍晚天气下太阳能和风能的可用性。用ANFIS方法对数据进行比较,确定系统约束条件。在用ANFIS测试数据时,使用3mf(高、中、低)。从总共4003个数据中发现了26%的误差,然后将训练数据与测试数据进行比较。将实际数据与经ANFIS处理后的训练数据进行数据对比测试后,可以得出光伏/风力发电可提供的最大负荷有更多的选择。这对混合光伏/风能独立的性能有影响,这在负载方面更具杠杆作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Loading Management System for Hybrid Photovoltaic/Wind Power Supply
Photovoltaic and wind turbine generation using environmentally friendly technology in the process of harvesting energy from the nature can be a solution to future electrical energy crises so that they become the most developed and reliable alternative. However, the conversion of solar/wind energy is highly dependent on the availability of sunlight and wind speed. Therefore, it is necessary to study the PV /wind loading which aims to increase and maintain the continuity of the electricity supply to the load. Load power management follows the availability of solar and wind energy in sunny, cloudy, rainy, or evening weather by considering the remaining usable battery voltage. Comparison of data is done to determine the system constraints with the ANFIS method. In testing the data with ANFIS performed with 3 MF (High, Medium, Low). From a total of 4003 data and an error of 26% was found, the training data was then compared with the test data. After testing the data comparison between the actual data and the training data that has been processed with ANFIS, it is obtained that there are more options for the maximum load that can be supplied by PV /wind generation. This has an impact on the performance of the hybrid PV /wind standalone which is more leverage on the loading side.
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