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For the problem of abnormal opening and closing states of high-voltage isolation switches in substations due to factors such as mechanical wear and electrical quantity changes，an atom search optimization（ASO）algorithm is proposed to identify the abnormal opening and closing status of high-voltage disconnectors in substations.Infrared and visible light cameras are used to capture the status images of high-voltage isolation switches，and the mapping relationship between image feature points is established through bilateral filtering and image registration.The joint weighted average method is used to achieve decision level fusion of images.The optimal segmentation threshold is determined by combining the gradient size and attribute vector of the centroid pixel neighborhood points of the image，and the high-voltage isolation switch feature area is extracted accordingly.Support vector machine algorithm is adopted to construct an abnormal state recognition model，and ASO algorithm is introduced to obtain model parameters，and to optimize model recognition performance，and identify the opening and closing abnormal states of the isolation switch by inputting the pixel values of the isolation switch feature area.Experiment results show that under the application of the studied method，the false positive rate of the obtained recognition results is less than 2%，and the recognition accuracy is relatively high.

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针对变电站高压隔离开关受到机械磨损和电气量变化等因素的影响，可能出现分合闸状态异常的问题，提出变电站高压隔离开关分合异常状态原子搜索优化（Atom Search Optimization，ASO）识别算法。采用红外相机和可见光相机采集高压隔离开关的状态图像，并通过双边滤波和图像配准建立图像特征点之间的映射关系，联合加权平均方法实现图像决策级融合，结合图像质心像素邻域点的梯度大小和属性向量，确定最佳分割阈值，由此提取高压隔离开关特征区域。采用支持向量机算法构建异常状态识别模型，引入ASO算法求取模型参数，优化模型识别性能，并通过输入隔离开关特征区域像素值，识别隔离开关的分合异常状态。实验结果表明，所提算法识别结果的误判率低于2%，识别精度较高。

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王德文（1988-），男，甘肃金昌人，国网甘肃省电力公司设备管理部工程师，主要研究方向为电网运检、电力系统分析、电网设备改造、电力生产成本分析。E-mail: 。

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王德文（1988-），男，甘肃金昌人，国网甘肃省电力公司设备管理部工程师，主要研究方向为电网运检、电力系统分析、电网设备改造、电力生产成本分析。E-mail: 。

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王德文（1988-），男，甘肃金昌人，国网甘肃省电力公司设备管理部工程师，主要研究方向为电网运检、电力系统分析、电网设备改造、电力生产成本分析。E-mail: 。

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tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505537402225094, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
参数取值
原子总数bm=50
总迭代次数T=300
深度权重Gp=1.0
碰撞直径δ=0.5
斥力下限h1=1.1
斥力上限ha=2.4
随机数cg=0.2
初始速度v0=2.0
), ArticleFig(id=1251505545493037882, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505537402225094, language=CN, label=表1, caption=

ASO算法的参数设定

, figureFileSmall=null, figureFileBig=null, tableContent=
参数取值
原子总数bm=50
总迭代次数T=300
深度权重Gp=1.0
碰撞直径δ=0.5
斥力下限h1=1.1
斥力上限ha=2.4
随机数cg=0.2
初始速度v0=2.0
), ArticleFig(id=1251505545581118272, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505537402225094, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
实验次数识别精度/%
所提算法文献[2]文献[3]文献[4]
198968797
298978798
399978697
499968698
598969198
699958996
798968597
899959398
999979298
1098978696
), ArticleFig(id=1251505545660810054, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505537402225094, language=CN, label=表2, caption=

识别精度对比结果

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实验次数识别精度/%
所提算法文献[2]文献[3]文献[4]
198968797
298978798
399978697
499968698
598969198
699958996
798968597
899959398
999979298
1098978696
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变电站高压隔离开关分合异常状态ASO识别算法
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王德文
西安邮电大学学报 | 计算机与自动化 2025,30(6): 77-84
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西安邮电大学学报 | 计算机与自动化 2025, 30(6): 77-84
变电站高压隔离开关分合异常状态ASO识别算法
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王德文
作者信息
  • 国网甘肃省电力公司，甘肃兰州，730030

    • 王德文（1988-），男，甘肃金昌人，国网甘肃省电力公司设备管理部工程师，主要研究方向为电网运检、电力系统分析、电网设备改造、电力生产成本分析。E-mail: 。

ASO recognition algorithm for abnormal opening and closing states of high-voltage isolation switches in substations
Dewen WANG
Affiliations
  • State Grid Gansu Electric Power Company，Lanzhou 730030，China
出版时间: 2025-11-10 doi: 10.13682/j.issn.2095-6533.2025.06.009
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摘要
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针对变电站高压隔离开关受到机械磨损和电气量变化等因素的影响，可能出现分合闸状态异常的问题，提出变电站高压隔离开关分合异常状态原子搜索优化（Atom Search Optimization，ASO）识别算法。采用红外相机和可见光相机采集高压隔离开关的状态图像，并通过双边滤波和图像配准建立图像特征点之间的映射关系，联合加权平均方法实现图像决策级融合，结合图像质心像素邻域点的梯度大小和属性向量，确定最佳分割阈值，由此提取高压隔离开关特征区域。采用支持向量机算法构建异常状态识别模型，引入ASO算法求取模型参数，优化模型识别性能，并通过输入隔离开关特征区域像素值，识别隔离开关的分合异常状态。实验结果表明，所提算法识别结果的误判率低于2%，识别精度较高。

变电站  /  高压隔离开关  /  分合异常状态  /  图像决策级融合  /  原子搜索优化算法  /  支持向量机算法

For the problem of abnormal opening and closing states of high-voltage isolation switches in substations due to factors such as mechanical wear and electrical quantity changes，an atom search optimization（ASO）algorithm is proposed to identify the abnormal opening and closing status of high-voltage disconnectors in substations.Infrared and visible light cameras are used to capture the status images of high-voltage isolation switches，and the mapping relationship between image feature points is established through bilateral filtering and image registration.The joint weighted average method is used to achieve decision level fusion of images.The optimal segmentation threshold is determined by combining the gradient size and attribute vector of the centroid pixel neighborhood points of the image，and the high-voltage isolation switch feature area is extracted accordingly.Support vector machine algorithm is adopted to construct an abnormal state recognition model，and ASO algorithm is introduced to obtain model parameters，and to optimize model recognition performance，and identify the opening and closing abnormal states of the isolation switch by inputting the pixel values of the isolation switch feature area.Experiment results show that under the application of the studied method，the false positive rate of the obtained recognition results is less than 2%，and the recognition accuracy is relatively high.

substation  /  high voltage isolation switch  /  abnormal state of splitting and merging  /  image decision level fusion  /  atom search optimization algorithm  /  support vector machine algorithm
王德文. 变电站高压隔离开关分合异常状态ASO识别算法. 西安邮电大学学报, 2025 , 30 (6) : 77 -84 . DOI: 10.13682/j.issn.2095-6533.2025.06.009
Dewen WANG. ASO recognition algorithm for abnormal opening and closing states of high-voltage isolation switches in substations[J]. Journal of Xi'an University of Posts and Telecommunications, 2025 , 30 (6) : 77 -84 . DOI: 10.13682/j.issn.2095-6533.2025.06.009
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变电站高压隔离开关能够将高压装置中需要检修的部分与其他带电部分可靠地隔离，保证高压电器及装置在检修工作时的安全。然而，在实际运行中，高压隔离开关会出现各种异常情况，如无法闭合、无法打开、触头烧坏、漏电等，不仅影响高压设备的运行和运维工作，甚至可能对维护人员的人身安全造成风险。因此，对高压隔离开关的分合异常状态进行准确识别至关重要。
文献[1]利用霍夫变换检测隔离开关的状态指示线角度和长度，并基于预设阈值判断隔离开关的分合闸状态是否存在异常。该方法可以实现隔离开关状态的实时监测，无需直接接触，避免了直接接触造成的安全隐患。然而，此方法的识别效果易受到照明、摄像头安装位置等因素的影响。文献[2]在变电站控制系统中设计和集成顺序控制模块，负责接收和执行调度中心的顺序控制指令，采用“双重确认”机制判断隔离开关分合异常状态。该方法的顺序控制模式实现了隔离开关操作的自动化和智能化，降低了操作和维护人员的劳动强度，提高了工作效率。但在强电磁干扰、极端温度等恶劣环境下，可能会影响位置信号采集和识别的准确性，从而影响识别精度。文献[3]在GIS隔离开关的分合闸过程中，使用罗氏线圈等传感器检测放电信号，并根据低频采样电压曲线中电弧信号的开始和结束时间点，确定隔离开关的分合闸状态。该文献提出了一种在低采样频率下提取高频电磁瞬态信号特征的方法，有效地减少了数据采集量，提高了处理效率。但此方法主要用于确认气体绝缘金属封闭开关设备（Gas Insnlated Switchgear，GIS）隔离开关的异常分合闸位置，其他类型的开关设备或故障类型可能需要进一步研究和验证。文献[4]使用边缘检测和特征提取等图像处理技术来检测和定位隔离开关的开关和触点，并利用几何距离测量算法计算关键部件之间的相对距离或角度。将计算出的几何参数与预设阈值或标准进行比较，以确定隔离开关的分合状态。该方法通过实时采集和处理图像，可以及时检测隔离开关状态的变化，确保电力系统的稳定运行。但该方法未考虑红外图像和可见光图像在空间中的映射关系，无法保证最终识别准确度。
为提高隔离开关分合异常状态识别的准确性，重点研究变电站高压隔离开关异常状态识别算法，通过红外和可见光图像的联合采集与融合，提高图像信息的完整性和可靠性。结合质心像素邻域点的梯度大小和属性向量，动态确定最佳分割阈值，增强特征提取的鲁棒性。创新性地引入ASO算法优化支持向量机（Support Vector Machine，SVM）模型参数，显著提高模型的分类精度和泛化能力。最后通过特征区域像素值的输入，减少数据维度，降低计算复杂度，实现隔离开关分合异常状态的快速准确识别。
1　变电站高压隔离开关分合异常状态识别算法设计
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1.1　获取高压隔离开关融合图像
利用图像融合技术将来自不同传感器的图像进行自适应整合，以获取更全面、更准确的隔离开关状态信息，以全方位反映隔离开关的温度分布和外观形态等，实现对高压隔离开关分合状态的精准监测和快速响应[5-7]，为后续的异常状态检测提供数据支持。
使用红外相机和可见光相机等传感器设备，同时采集高压隔离开关的红外图像和可见光图像，并针对图像中的随机亮点或暗点噪声，利用双边滤波方法同时考虑图像的空间邻近度和像素值相似度，抑制图像中的噪声，同时强化图像的边缘细节[8-10]，提高图像的特征表现力。计算公式为
式中：gt为双边滤波函数；ηx为滤波因子滑动模板；z0为二维尺度因子；jh为滑动窗口宽度；yr为图像质心点处的灰度值；p0为滤波系数；n为原始图像像素点数量；υc为滤波函数的标准差；hd为图像均衡化系数；aw为滤波后的图像。
为准确找到红外图像和可见光图像在空间中的映射关系，对其进行图像配准处理[11]。表达式为
式中：m′为特征描述子；βt为原图像归一化后的像素值；为可见光图像中最大的像素值；tt为中和函数；q0为匹配系数。
根据特征描述子，在参考图像和待配准图像之间建立特征点的对应关系[12]，表达式为
式中：Gs为特征点映射因子；ιc为多变量非线性函数；ud为变换参数；hq为可见光图像第q个像素点的随机矢量；ym为转换矩阵。
利用仿射变换原理构造变换函数，得到图像配准的相似性度量向量[13]，即
式中：μ0为最小化误差函数；pf为相邻两个像素点之间的汉明距离。
进而依据加权平均方法将高压隔离开关的红外图像和可见光图像进行决策级融合，得到的融合后图像可表示为
式中：eb为图像分解尺度；ζt为图像像素的二维数据序列；Rs为一阶滞后变量；Sg为融合后的图像。
采用红外相机和可见光相机采集高压隔离开关的状态图像，并通过双边滤波和图像配准建立图像特征点之间的映射关系，结合加权平均方法实现图像决策级融合，为接下来隔离开关特征区域的提取奠定基础。
1.2　提取高压隔离开关图像特征区域
在高压隔离开关状态图像基础上，对图像的特征区域进行有效提取，可以准确地从复杂的背景中识别出高压隔离开关的具体位置，从而便于隔离开关状态的实时监测和判定。
采用特征点检测算法识别图像隔离开关的角点和边缘点，并从多个尺度比较像素点与其邻域点的灰度差异，以获取图像的尺度空间极值[14-16]，表达式为
式中：α为图像高频分量；ka为相似度阈值；T0为经验常数。
为获取的图像尺度空间极值生成唯一的检测算子，由此计算图像质心像素邻域点的梯度大小和属性向量[17]，即
式中：wβ为领域点的属性向量；rα为检测算子的维度；qk为邻域点梯度大小；δs为像素索引因子；uq为衰减常数。
进一步可得到最佳分割阈值λy表达式为
式中：σ0为图像像素的平衡超参数；υr为离散系数。
利用最佳分割阈值将背景灰度值与隔离开关区域的灰度值方差达到最大[18-20]，即可将隔离开关状态特征区域从整体图像中提取出来，便于后续隔离开关分合异常状态的识别。
1.3　隔离开关分合异常状态ASO识别
基于高压隔离开关特征区域提取结果，构建隔离开关分合异常状态识别模型，并引入ASO优化模型参数，以此实现精准识别与检测。
假设图像特征区域共包含D个特征，则采用支持向量机方法确定特征的分类超平面[21]，表达式为
式中：θ为训练样本个数。
利用径向基函数作为支持向量机的核函数，并引入拉格朗日乘子训练支持向量机模型[22-23]，即
式中：mq为核函数的宽度参数；ψk为分辨系数；I0为松弛变量。
进而利用隔离开关的历史运行状态数据和特征参量测试支持向量机，进而构建异常识别模型，表达式为
式中：ζs为正则化参数。
为保证最终隔离开关分合状态异常识别的精度，采用ASO算法对识别模型的内部参数ζs进行寻优计算，得到最佳参量。ASO算法的原理是通过原子之间的相互作用力和约束力，使得原子探索系统空间解决优化问题。具体迭代寻优过程如下。
1）根据原子的最大适应值和最小适应值确定原子的初始速度，即
式中：υg为第g个原子所受的约束力；Gp为深度权重。
2）根据相邻两个原子之间的作用力，确定第j个原子和第k个原子之间的距离[24]为
式中：ψh为系数因子。
3）为避免算法收敛早熟和局部收敛问题，在原子运动过程中随机引入[0，1]区间内的随机数约束原子的位置变化，避免原子聚集在某个局部区域。则当前迭代次数下原子的加速度可表示为
式中：φx为第x个原子的质量；bm为原子总数。
4）为进一步加强最优信息在原子间的影响，引入历史原子最优信息更新原子的速度，由此输出各原子的加速度，即
式中：zc为历史原子最优信息。
5）当迭代次数达到指定的最大次数时，将具有最大速度粒子对应的加速度作为异常状态识别模型的参量ζs，以此完成模型的优化。进而将前文提取的隔离开关特征区域像素值输入模型中，得到隔离开关分合状态类别，表达式为
式中：W（U）为状态异常识别输出的隔离开关分合状态异常类型；B为隔离开关特征区域像素值；γ0为可调参数。
通过以上计算与分析，即可实现变电站高压隔离开关分合异常状态ASO识别。变电站高压隔离开关分合异常状态识别流程如图1所示。
2　实例论证分析
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本次测试主要是针对变电站高压隔离开关分合异常状态ASO识别算法的实际应用效果进行验证与研究，并通过对比实验的方式分析所提算法的性能有效性。
2.1　实验准备
本次实验以某变电站实际运行中的110kV户外高压隔离开关为研究对象，其基本概况及动、静触头等设备实况如图2所示。
采用Bobcat+320GigE400vSWIR红外相机和INFINITY-EP可见光相机采集该高压隔离开关的红外图像和可见光图像，共采集1000个样本图像，其中正常状态为400个样本。分合异常状态为600个样本（包括卡滞、不到位、接触不良等）。在1000个样本中，选择700个样本作为训练集，150个样本为验证集，150个样本为测试集。选取分合不到位的异常状态样本进行识别，并通过图像预处理和边缘检测以及特征点识别，对图像进行融合，结果如图3所示。
由图3可知，通过对红外与可见光图像的融合处理，得到的融合图像有效整合了温度分布与结构细节特征，融合后图像可显著提升图像状态识别的准确性和鲁棒性，有效抑制噪声干扰。基于融合后图像，结合图像质心像素点邻域点的梯度大小和属性，确定图像最佳分割阈值，由此提取隔离开关的特征区域，具体如图4所示。
整个实验在集成控制平台上展开，根据提取出的隔离开关特征区域，采用支持向量机方法构建隔离开关分合状态异常识别模型。设置目标函数的惩罚系数Cd=1.0，核函数系数tb=0.1，最小化误差精度er=0.001。并利用ASO算法优化模型参数，算法的参数设定如表1所示。
根据经验，将高压隔离开关分合状态异常类别划分为5种，即卡涩、拒动、误动、分合闸不到位和发热，使用A～E表示。并基于以上实验准备，利用所提方法对该高压隔离开关分合状态异常进行识别检测，根据实验结果分析所提方法的可靠性。
为了验证所提算法在变电站实时检测需求下的计算效率，以收敛性为指标，采用文献[4]基于几何测距的隔离开关分合状态异常识别方法为对比方法，收敛性对比结果如图5所示。
从图5可以看出，所提算法在50次的迭代内，收敛性已达到99%以上，而文献[4]方法则在近200次迭代后才收敛至99%，说明所提算法能够更快地找到最优解，同时在相同的计算环境下，所提算法的计算资源消耗更少，能够更快地完成任务，减少了计算时间和计算资源的占用。
2.2　高压隔离开关分合异常状态识别结果
实验采用对比的形式进行，选取当前较为成熟的文献[2]基于顺控模式的方法、文献[3]基于电磁瞬态过程的方法和文献[4]基于几何测距的隔离开关分合状态异常识别方法作为对照组方法，所提方法作为实验方法，对高压隔离开关的分合异常状态进行识别，并基于识别结果分析不同方法的实际应用性能。则基于4种方法的高压隔离开关分合异常状态识别结果如图6所示。
从图6可以看出，在不同输入样本数量条件下，应用所提算法对隔离开关分合异常状态进行识别检测，输出的状态类别与实际类型相一致，而对照组方法得到的异常状态均与实际状态存在一定偏差。由此可以证明所提算法在隔离开关分合异常状态识别中可以取得较高的识别精度。
2.3　对比实验与分析
在上述实验基础上，为体现实验结果的客观性，对不同方法的识别误判率进行统计与比较，从而进一步分析对照组方法和所提算法的识别性能。误判率对比结果如图7所示。
通过图7可知，与其他3种方法相比，基于所提算法的隔离开关分合异常状态识别结果的误判率最低，始终保持在2%以下，充分证明了所提算法在高压隔离开关分合异常状态识别误判率方面的优异性能。
为验证ASO算法在识别精度指标下的性能优势，对每种方法进行10次实验，记录每次实验的识别精度，对比结果如表2所示。
从表2可知，所提算法、文献[2]方法、文献[3]方法和文献[4]方法的标准差分别为0.8%、1.2%、1.4%和1.2%。相较于3种对比方法，所提算法的识别精度均在98%以上，且标准差仅为0.8%，说明所提算法具有更高的识别性能，且性能波动性较低，证明了ASO算法在实际应用中的可行性和有效性。
3　结语
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针对高压隔离开关的分合异常状态的识别准确性较低的问题，提出了变电站高压隔离开关分合异常状态ASO识别算法，利用图像梯度信息反映边缘特征，结合属性向量综合分析，动态确定最佳分割阈值。并通过ASO算法的全局搜索能力，找到使SVM分类性能最佳的参数组合，显著提升异常状态识别的准确率和泛化能力。实验结果表明，相较于对比方法，所提算法对隔离开关分合异常状态识别结果的误判率始终保持在2%以下，具有较高的识别准确性，有助于提高变电站运维的智能化水平。但所提方法仍具有一定的局限性，在实际运行中，高压隔离开关可能同时出现多种异常状态，在这种多种异常状态混叠的情况下，所提算法可能无法全面捕捉其特征，导致异常状态识别的困难。在之后的研究中，可以针对多种异常状态混叠的问题，采用稀疏表示、流形学习等技术，从图像中提取更具判别性的特征，以减少异常状态之间的混淆。
文献
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2025年第30卷第6期
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doi: 10.13682/j.issn.2095-6533.2025.06.009
  • 接收时间：2025-01-14
  • 首发时间：2026-04-16
  • 出版时间：2025-11-10
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    国网甘肃省电力公司，甘肃兰州，730030
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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