# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation  
### Line Loss Indicator Model Based on Principal Component Analysis  
The Guangxi Power Grid Company fulfils the stipulations set forth by Southern Power Grid Company's Line Loss Management Measures. In alignment with the tenets of comprehensive coverage, collectability, and efficacy in loss reduction, the company has divided the evaluation process into four distinct phases: planning loss reduction, management loss reduction, operational loss reduction, and technology loss reduction. This comprehensive approach encompasses the entirety of the line loss management process. Line loss management is a comprehensive indicator that reflects the level of high-quality development of the power grid. It involves a range of disciplines, including grid planning and construction, technology, operation and marketing, and other professions. In essence, it can be considered a \"comprehensive physical examination\" of the power grid. The causes of \"abnormal line loss\" in power grid equipment may be attributed to a multitude of factors, including the age of the lines, the length of the power supply radius, low voltage, heavy overload, and unstable trend operation mode. These factors often necessitate a significant investment of resources to investigate individually, with the aim of mitigating the risks posed to the power grid. The traditional method of calculating the line loss rates involves the use of meter readings. The traditional method of calculating line loss rates is based on meter readings taken over a period of time that may not be representative of the actual situation. This can result in significant monthly fluctuations, making it difficult to draw accurate conclusions and prescribe effective solutions. In order to demonstrate the practicality of the indicator selection method proposed in this paper, the line loss indicator data of a city-level power grid enterprise in the Southern Power Grid will be used as an illustrative example. In order to ascertain the comprehensive line loss rate and the industry's evaluation of power grid enterprise line loss, a composite evaluation analysis method is selected for the calculation. The example employs statistical calculation data of 104 power supply enterprises' line loss reduction impact indicators, with each sample comprising 37 indicators from four principal categories: planning reduction, management reduction, operational reduction, and technical reduction. The influence of the initial 13 kernel principal components is illustrated in Figure 6. From the figure, it can be observed that as the Gaussian kernel function's \(\sigma\) increases, the contribution rate of the principal components also increases. When \(\sigma=9\), the contribution rate of the principal components increases and becomes stable, indicating that the dimensionality reduction effect of the input data set space is effective and that the overfitting phenomenon is less likely to occur. In conclusion, the application of the Gaussian kernel function parameter with a standard deviation of 9 in the kernel principal component analysis (KPCA) method has been found to yield the most effective reduction in the dimensionality of the input dataset. The cumulative contribution rate of the 37 principal components is 100.00%. The initial 13 principal components account for 71.211% of the total contribution. When the cumulative contribution rate exceeds 70%, the higher the variance percentage corresponding to each extracted factor, the larger the eigenvalue of that factor, and the greater its role in explaining the original variables. In particular, the eigenvalues decrease in magnitude from the 13 th factor onwards, indicating that these factors have a diminished capacity to explain the original variables and can thus be excluded from further consideration.