An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case

Abstract

Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized kernel extreme learning machine (KELM). First, the time- and frequency-domain features of electrical equipment (active and reactive power) are extracted from the power data after data cleaning, and the initial feature pool is established. Next, a two-stage feature selection algorithm is proposed to generate the smallest features, leading to superior classification accuracy. In the initial selection phase, each feature weight is calculated using ReliefF technology, and the features with smaller weights are removed to obtain the candidate feature set. In the reselection stage, the k-nearest neighbor classifier (KNN) based on the MPA is designed to obtain the superior combination of features from the candidate feature set concerning the classification accuracy and the number of feature inputs. Third, the IMPA-KELM classifier is developed as a load identification model. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. Compared with the MPA, IMPA has a faster convergence speed and more robust global search capability. In this paper, actual data from the cement industry within China are used as a research case. The experimental results show that after two-stage feature selection, the initial feature set reduces the feature dimensionality from 58 dimensions to 3 dimensions, which is 5.17% of the original. In addition, the proposed IMPA-KELM has the highest overall recognition accuracy of 93.39% compared to the other models. The effectiveness and feasibility of the proposed method are demonstrated.