CN113067352B - 一种基于eeac和elm的电网临界切除时间预测方法 - Google Patents

一种基于eeac和elm的电网临界切除时间预测方法 Download PDF

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CN113067352B
CN113067352B CN202110355089.3A CN202110355089A CN113067352B CN 113067352 B CN113067352 B CN 113067352B CN 202110355089 A CN202110355089 A CN 202110355089A CN 113067352 B CN113067352 B CN 113067352B
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李峰
王�琦
汤奕
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

本发明提出一种基于EEAC和ELM的电网临界切除时间预测方法，属于电网安全稳定分析技术领域。该方法以基于简化模型的扩展等面积法则为基础，采用极限学习机理论校正扩展等面积法则的输出结果，建立了电网中相关故障临界切除时间预测框架，可依赖***模型在线分析结果和在线量测数据，实现对电网故障临界切除时间的快速预测。本发明能够在满足在线计算速度要求的前提下，保证电网暂态稳定指标评估的准确性和可靠性；本发明对扰动后电网临界切除时间的预测，能够为相关控制策略的制定提供依据，有助于运行人员快速、准确地制定电网控制措施，防止事故扩大。

Description

一种基于EEAC和ELM的电网临界切除时间预测方法
技术领域
本发明涉及电网安全稳定分析方法领域，具体是一种基于EEAC和ELM的电网临界切除时间预测方法。
背景技术
由于区域电网互联、新能源接入和电网运行经济性政策的影响，现代电力***的运行往往接近于其运行极限，任何微小的故障扰动，都有可能破坏电力***的稳定运行。因此，在***受扰后，对***的暂态稳定裕度进行快速准确预测，对制定***的安全稳定控制策略具有重要意义。
传统的电力***暂态稳定分析方法主要有时域仿真法、EEAC法(Extended EqualArea Criterion,扩展等面积法则)、暂态能量函数法(Transient Energy Fuction,TEF)，和人工智能方法。时域仿真法虽然计算结果准确，但是计算速度慢，难以达到在线计算的要求。EEAC法和TEF法都属于直接法，在计算速度上有所进步，也在实际工程中得以应用，但是其在计算过程中包含对电力***模型的简化假设，导致其计算结果的准确性有所降低。人工智能方法，是近年来广受关注的电力***暂态稳定分析方法之一，其能够快速地出力高维数据，而且在非线性拟合方面具有非常大的优势，但其实施的效果受样本质量、数量及训练算法的影响，若能在人工智能方法中引入传统的物理模型方法作支撑，将使得电力***暂态稳定分析预测的准确性和可靠性大幅提高。
因此，本专利综合EEAC法和人工智能方法的特点，提出了基于EEAC和ELM(ExtremeLearning Machine,极限学习机)的电网临界切除时间预测方法，该方法首先基于电网信息和电网模型，采用IEEAC(Integrated EEAC,集成EEAC)方法对电网中的故障进行分析，计算临界切除时间，然后，综合IEEAC方法计算的临界切除时间结果，电网运行信息和电网暂态信息，利用训练好的ELM模型，对临界切除时间结果进行校正。
发明内容
本发明的目的在于提供一种基于EEAC和ELM的电网临界切除时间预测方法，本发明预测方法提出ELM模型建立方法、电网临界切除时间预测模型建立方法。其中，ELM模型构建方法，可利用IEEAC方法计算结果与实际结果的误差样本，以IEEAC方法计算结果的输出结果以及电网运行信息和电网暂态信息为输入，以实际临界切除时间结果为输出，通过ELM算法进行训练，建立ELM模型。其中，IEEAC方法基于二阶经典发电机模型的假设进行计算；电网运行信息为故障前发电机的有功和无功出力信息；电网暂态信息为故障发生瞬间发电机节点电压相位和幅值的瞬时变化信息,采用ELM方法挖掘EEAC模型简化与计算结果误差间的关联关系，从而构建基于ELM的误差校正模型，提高电网临界切除时间预测的精度。
本发明的目的可以通过以下技术方案实现：
基于电网临界切除时间预测模型，本发明提供一种基于EEAC和ELM的电网临界切除时间预测方法，预测方法包括如下步骤：
S1：收集电力***运行信息，并以固定的周期更新电网分析使用的模型；
S2：监测到故障发生后，识别故障基本信息；收集故障发生前的电网运行信息；收集故障发生瞬间的电网暂态信息；将故障基本信息、电网运行信息和电网暂态信息上传至电网调控中心；
S3：基于更新的电网分析模型和识别出的电网故障基本信息，采用扩展等面积法则对该故障下的临界切除时间进行计算，并记录结果；
S4：根据EEAC计算的临界切除时间结果、电网运行信息和电网暂态信息，采用极限学习机模型校正EEAC计算的临界切除时间结果，预测当前故障下的临界切除时间。
进一步的，所述S2中故障基本信息包括：故障形式和故障地点；电网运行信息为故障前发电机的有功和无功出力信息；电网暂态信息为故障发生瞬间发电机节点电压相位和幅值的瞬时变化信息。
进一步的，所述S3中电网分析模型采用发电机模型采用二阶经典模型进行计算，EEAC方法的选用IEEAC方法。
进一步的，所述S4中ELM方法以基于经典模型的IEEAC结果和实际结果的误差样本，以IEEAC输出结果、电网运行信息和电网暂态信息为输入，以故障临界切除时间为输出，通过ELM算法进行训练，建立ELM校正模型。
本发明的有益效果：
本发明预测方法在保留EEAC机理模型的基础上，采用人工智能方法挖掘模型简化与结果误差间的关联关系，构建基于ELM的误差校正模型，能够弥补单独依靠EEAC方法或ELM模型的电网暂态稳定分析方法在实际中的不适应性，有效提高计算结果的精度和可靠性。基于此，本发明对扰动后电网临界切除时间的预测，能够为相关控制策略的制定提供依据，有助于运行人员快速、准确地制定电网控制措施，防止事故扩大。
附图说明
下面结合附图对本发明作进一步的说明。
图1是本发明在线应用实现流程图；
图2是本发明所测试***的电力网络拓扑图；
图3是本发明与IEEAC方法的结果准确性对比图；
图4是本发明与ELM方法在不同样本数量场景下准确性指标对比图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
一种基于EEAC和ELM的电网临界切除时间预测方法,预测方法所测试电力***包含10台发电机，46条输电线路，图2为电力***网络拓扑图,采用Monte-Carlo(蒙特卡罗)方法生成测试所需的1500组样本，设置整体负荷水平服从某一区间均匀分布，取值为[0.8，1.2](标幺值，下同)，同时设置所有节点注入功率服从正态分布，取值为N(1,0.1)；为模拟不同扰动情况，设置***中除平衡节点外任意节点发生不平衡功率扰动事件概率相同，扰动大小和持续时间分别服从[0.1,1.2]的均匀分布和N(0.1,0.03)的正态分布。
针对图2所示电力***，在仿真平台上利用本发明的方法进行临界切除时间预测，验证扰动后电网暂态频率特征预测性能。选用平均绝对误差(Mean Absolute Error，MAE)，平均绝对误差百分比(Mean Absolute Percentage Error，MAPE)，回归问题性能度量常用的均方根误差(Root-Mean Squared Error，RMSE)和R平方(R-squared)四个指标对方法的预测精度进行评价。
Figure BDA0003002140830000041
Figure BDA0003002140830000042
其中D＝{(x1,y1),(x2,y2),…,(xm,ym)}为样本集，m为测试样本个数，yi是xi的真实值；f为预测方法函数。各项指标的分析结果如下：
(1)本发明方法与IEEAC方法对比
在测试算例中，随机选取样本进行测试，IEEAC方法与本发明方法预测结果的绝对误差对比，如图3所示，本发明的方法对每个测试样本的预测精度均大幅领先于IEEAC方法，综合评估指标对比如下表所示。
表1测试***所提方法与IEEAC方法预测结果准确性对比
Figure BDA0003002140830000051
从上表中针对IEEAC方法和本发明方法指标对比，可以看出，本发明的方法比基于经典二阶模型的IEEAC方法具备更高精度。
(2)本发明方法与ELM方法对比
在测试算例中，分别在200样本、600样本、1000样本和1500样本时，将本发明方法与仅ELM方法对比，MAE,MAPE和RMSE指标的比较如图4所示，从图中可以看出，本发明的方法在不同样本数量场景下，相对于仅ELM方法，具有更好的准确性。
在本说明书的描述中，参考术语“一个实施例”、“示例”、“具体示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上显示和描述了本发明的基本原理、主要特征和本发明的优点。本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是说明本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明范围内。

Claims (4)

1.一种基于EEAC和ELM的电网临界切除时间预测方法，其特征在于，预测方法包括如下步骤：
S1：收集电力***运行信息，并以固定的周期更新电网分析使用的模型；
S2：监测到故障发生后，识别故障基本信息；收集故障发生前的电网运行信息；收集故障发生瞬间的电网暂态信息；将故障基本信息、电网运行信息和电网暂态信息上传至电网调控中心；
S3：基于更新的电网分析模型和识别出的电网故障基本信息，采用扩展等面积法则对该故障下的临界切除时间进行计算，并记录结果；
S4：根据EEAC计算的临界切除时间结果、电网运行信息和电网暂态信息，采用极限学习机模型校正EEAC计算的临界切除时间结果，预测当前故障下的临界切除时间。
2.根据权利要求1所述的基于EEAC和ELM的电网临界切除时间预测方法，其特征在于，所述S2中故障基本信息包括：故障形式和故障地点；电网运行信息为故障前发电机的有功和无功出力信息；电网暂态信息为故障发生瞬间发电机节点电压相位和幅值的瞬时变化信息。
3.根据权利要求1所述的基于EEAC和ELM的电网临界切除时间预测方法，其特征在于，所述S3中电网分析模型采用发电机模型采用二阶经典模型进行计算，EEAC方法的选用IEEAC方法。
4.根据权利要求1所述的基于EEAC和ELM的电网临界切除时间预测方法，其特征在于，所述S4中ELM方法以基于经典模型的IEEAC结果和实际结果的误差样本，以IEEAC输出结果、电网运行信息和电网暂态信息为输入，以故障临界切除时间为输出，通过ELM算法进行训练，建立ELM校正模型。
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