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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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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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使用机器学习预测光伏系统参数的系统性审查.

Md Jobayer1, Md Al Hasan Shaikat1, Md Naimur Rashid1

  • 1BRAC University, Dhaka 1212, Bangladesh.

Heliyon
|June 22, 2023
PubMed
概括

机器学习 (ML) 方法提供准确和快速的光伏 (PV) 参数估计. 这份对2020-2022年研究的综述表明,神经网络是光伏系统分析中最受欢迎的ML方法.

科学领域:

  • 可再生能源系统可再生能源系统
  • 在工程领域的人工智能.
  • 材料科学 材料科学 材料科学

背景情况:

  • 鉴于日益增长的能源需求,评估光伏 (PV) 系统的性能至关重要.
  • 传统的光伏参数估计方法 (如卫星数据,IV特性) 缺乏足够的可靠性.
  • 机器学习 (ML) 为光伏系统参数估计提供了更快,更准确的替代方案.

研究的目的:

  • 系统地审查和分析2020年至2022年期间发表的基于机器学习的光伏 (PV) 参数估计研究.
  • 在最近的光伏研究中确定最常见的ML算法,数据源和评估指标.
  • 为研究人员和利益相关者提供见解,以优化光伏系统性能并指导未来的研究方向.

主要方法:

  • 2020-2022年基于ML的PV参数估计研究的系统文献综述.
  • 基于ML算法,数据源 (硬件与模拟),样本大小和错误指标的选择研究的分析.
  • 用于不同ML算法和评估指标的频率的定量评估.

主要成果:

  • 神经网络是最常用的ML算法 (32.55%),其次是随机向量功能链接 (13.95%) 和支持向量机器 (9.30%).
  • 计算机模拟是主要的数据来源 (66%),同时还使用了硬件测试 (18%) 和组合方法 (16%).
关键词:
机器学习 机器学习太阳能光伏发电是如何实现的系统参数估计系统参数估计系统性审查 系统性审查

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  • 根平均平方误差 (29.1%),平均绝对误差 (17.5%) 和确定系数 (15.9%) 是最常见的误差指标.
  • 结论:

    • 机器学习,特别是神经网络,是光伏参数估计的主要和有效的方法.
    • 模拟数据的普及凸显了对光伏系统的ML更多基于硬件的验证的需求.
    • 本综述有助于理解ML算法对光伏系统的有效性,为政策,投资和优化策略提供信息.