连接点接触电阻对塑壳断路器分断能力的影响分析数据

本数据聚焦于分析连接点接触电阻对塑壳断路器分断能力的影响，揭示了接触电阻参数与断路器分断性能之间的量化关系，为公司（作为生产商）及外部相关方提供了重要的决策依据，具有显著的应用价值。具体体现在以下方面： 1.优化产品开发和生产工艺：公司可通过分析连接点接触电阻对分断能力的影响，精准优化连接结构设计和表面处理工艺，科学制定接触电阻控制标准，提升断路器在长期使用过程中的导电可靠性。 1.2.推动行业科技进步：本数据可为断路器领域的科研人员和技术研发人员提供支持，帮助他们开展接触电阻分析、温升特性预测和质量控制等工作，促进断路器在高温大电流工况下的性能稳定性提升。1.数据采集： 实时记录不同连接点接触电阻条件下的塑壳断路器分断能力测试数据，包括测试样品编号、测试时间、接触电阻/μΩ、分断能力/kA等字段。2.数据预处理： （1）对采集的数据进行去噪处理，确保数据准确性。 （2）将历史采集的数据（包含本次采集）进行聚合，形成数据集X，并针对数据集X中的分断能力字段，计算出其平均值。3.计算线性回归斜率a和截距b： （1）基于数据集X（以接触电阻为自变量、分断能力为因变量），运用SLOPE函数，基于最小二乘法原理确定斜率a，运用INTERCEPT函数确定截距b。 （2）斜率a表示单位接触电阻变化对分断能力的影响程度（负相关），截距b表示基准接触电阻下塑壳断路器的分断能力值。4.结果运用： （1）计算比例系数k：k=|a/分断能力平均值|×100%。 （2）若k≥8%，则判定为"高影响"，若3%≤k＜8%，则判定为"中影响"，若k＜3%，则判定为"低影响"。This dataset focuses on analyzing the impact of contact resistance at connection points on the breaking capacity of molded case circuit breakers (MCCBs), and reveals the quantitative relationship between contact resistance parameters and the breaking performance of circuit breakers. It provides important decision-making basis for the company (as a manufacturer) and external stakeholders, with significant application value. This is specifically reflected in the following aspects: 1. Optimize product development and production processes: The company can accurately optimize the connection structure design and surface treatment processes by analyzing the impact of contact resistance at connection points on breaking capacity, scientifically formulate contact resistance control standards, and improve the electrical conductivity reliability of MCCBs during long-term use. 1.2. Promote scientific and technological progress in the industry: This dataset can provide support for researchers and technical R&D personnel in the circuit breaker field, helping them carry out work such as contact resistance analysis, temperature rise characteristic prediction, and quality control, and promote the improvement of performance stability of MCCBs under high-temperature and high-current operating conditions. 1. Data Collection: Real-time record the breaking capacity test data of MCCBs under different contact resistance conditions at connection points, including fields such as test sample ID, test time, contact resistance/μΩ, breaking capacity/kA, etc. 2. Data Preprocessing: (1) Denoise the collected data to ensure data accuracy. (2) Aggregate the historically collected data (including this collection) to form dataset X, and calculate the average value of the breaking capacity field in dataset X. 3. Calculate linear regression slope a and intercept b: (1) Based on dataset X (with contact resistance as the independent variable and breaking capacity as the dependent variable), use the SLOPE function to determine slope a based on the principle of least squares, and use the INTERCEPT function to determine intercept b. (2) Slope a represents the degree of influence of unit contact resistance change on breaking capacity (negatively correlated), and intercept b represents the breaking capacity value of MCCBs under the reference contact resistance. 4. Application of Results: (1) Calculate the proportional coefficient k: k=|a / average breaking capacity| × 100%. (2) If k ≥ 8%, it is classified as "High Impact"; if 3% ≤ k < 8%, it is classified as "Medium Impact"; if k < 3%, it is classified as "Low Impact".