## 1 Introduction
The effectiveness of the suggested method is demonstrated by an improved Error-MPP profile and decreased dynamic oscillation at the DG (Distributed Generation) coupling bus. In [24], an extreme learning machine (ELM) with Neural Networks was incorporated in Cooperative Spectrum Sensing (CSS) for cognitive radio networks (CRN) for detecting false alarms. The findings show that the NN-ELM approach offers a superior balance of training duration and detection performance. The overfitting issue raised in the ELM approach was overcome in [25]. It combines bound optimization theory with Variational Bayesian (VB) inference to create new L1 norm-based ELMs. It exhibited the best prediction performance on the testing dataset. To reduce the memory and complicated data issues, principal component analysis (PCA) is employed on complex datasets. To understand the PCA in its raw form and its applications, the paper written by [PERSON] et al. [26] is helpful as it contains a detailed explanation of the step-by-step procedure of the PCA with an explanation. In [26], authors defined PCA as a multivariate approach for analyzing a data set in which observations are characterized by numerous inter-correlated quantitative dependent variables. Its purpose is to extract key information from statistical data and express it as a set of new orthogonal variables known as principal components. The application of PCA was mostly found in image processing, so some extensions are known as 2 DPCA and modular PCA are developed that are better than PCA in terms of their accuracy in feature extraction and facial recognition, respectively [27; 28]. A comparison between ICA (Individual Component Analysis) and PCA is performed in terms of facial recognition [29]. The application of PCA for its very purpose, i.e., to reduce dimensions of data, is performed on bio-medical data [30]. PCA can also be used in power plants for fault detection and identification [31]. Ref. [32] shows that the application of PCA to detect faults in superheaters using the temperature data from tubes is successful, and an extended PCA method known as Kernel PCA works better for non-linear data. PCA and wavelet approaches were combined in [33] and extended for fault analysis in electrical machines. The other capability of PCA is an estimation. Ref. [34] is an example of estimating sag from data obtained from 17 sub-stations. The method has been used in various fields such as image processing [25], medical [30], in which PCA had been used both as a reduction method and a data filter, chemical processing [31], and facial recognition [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. [PERSON] suggested that the PCA-ELM algorithm in [37] train any SLFN with hidden nodes that has linear activation functions. With the information acquired via PCA on the training dataset, it calculates the hidden node parameters. The network structure can be simplified in this way to enhance prediction precision. [PERSON] [38] found that the rationale for combining PCA and ELM is to make use of the two algorithms' strengths, such that the combined method has the neural network's stability and learning capacity while reducing the input neurons' complexity. The applications of a PCA-ELM-based prediction model in different domains are:  
[PERSON] [39] used the PCA-ELM network model to predict the blast furnace gas utilization rate to optimize the operation of a blast furnace. [PERSON] [40] suggested the usage of terahertz time-domain technology in conjunction with the PCA-GA-ELM model to assess the thickness of TBCs. The research by [PERSON] [41] offered a unique hybrid model that combines principal component analysis (PCA) and a regularized extreme learning machine (RELM) to predict carbon dioxide emissions in China from 1978 to 2014. To recapitulate the novelty of the work in this paper, the idea is to investigate the prediction of boiler output power in a thermal power plant using an extreme learning machine.