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相关概念视频

Power System Distribution01:25

Power System Distribution

264
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
264
PID Controller01:19

PID Controller

144
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
144
PI Controller: Design01:24

PI Controller: Design

331
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
331
Energy and Power Signals01:17

Energy and Power Signals

338
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
338
Temperature Measurement Sites01:14

Temperature Measurement Sites

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A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

166
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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智能物联网平台用于多个光伏电站监控

Ida Bagus Krishna Yoga Utama1, Radityo Fajar Pamungkas1, Muhammad Miftah Faridh1

  • 1Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种物联网平台,用于监控多个光伏 (PV) 电厂,通过人工智能驱动的发电预测和实时传感器异常检测来增强运营.

关键词:
物联网平台物联网平台物联网平台的物联网.检测异常检测异常检测监控 监控 监控 监控 监控 监控太阳能光伏发电是如何实现的预测 预测 预测 预测

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科学领域:

  • 可再生能源系统可再生能源系统
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.
  • 人工智能 (AI) 是一种人工智能.

背景情况:

  • 太阳能 (PV) 电厂行业的快速扩张需要有效管理地理分布的设施.
  • 当前的运营和维护策略与监控多个远程光伏电站的复杂性作斗争.

研究的目的:

  • 开发一个整合的物联网 (IoT) 平台,用于集中监控多个光伏 (PV) 电站.
  • 通过预测发电和实时异常检测功能来增强平台的功能.

主要方法:

  • 实施一个物联网平台,用于汇总来自多个光伏厂的数据.
  • 开发和比较五个人工智能 (AI) 模型,用于下一天的发电预测.
  • 适应适应值隔离森林算法的应用,用于检测光伏电站内的传感器异常.

主要成果:

  • 开发的物联网平台成功实现了对多个光伏发电厂的集中监控.
  • 该BiLSTM人工智能模型在发电预测方面表现出卓越的性能,实现了低误差指标 (MSE: 0.0072,MAPE: 0.1982,MAE: 0.0542) 和高精度 (R2: 0.9664).
  • 适应值隔离森林在识别传感器异常时实现了高精度 (0.9517).

结论:

  • 拟议的物联网平台为管理多个光伏电站提供了有效的解决方案.
  • 由人工智能驱动的预测和异常检测显著提高了光伏能源系统的运行效率和可靠性.