Xian-Long Lv

This paper reports the second part of a two-part investigation into a novel Augmented Quotient Gradient System (AQGS) method for solving the general AC Optimal Power Flow (OPF) problem. The first part established the theoretical groundwork for the AQGS algorithm, providing formal justification for its advantages. In this second part, we detail the implementation of the robust numerical AQGS algorithm and introduce an enhanced version, referred to as the TJU-enhanced AQGS method, which was designed to improve computational efficiency. Both approaches demonstrate strong convergence, rapid computation, and exceptional robustness, particularly when applied to large-scale OPF problems involving 1888-, 2736-, and 13,695-bus systems. Notably, these methods consistently converge to the optimal solution within a single iteration across multiple test cases.

Due to the non-convex characteristics of the alternating current optimal power flow (ACOPF) problem, most existing methods are restricted to obtaining Karush-Kuhn-Tucker (KKT) solutions. There are significant challenges associated with these solutions, including non-convergence and computational inefficiency. This first part of a two-part paper introduces a robust Augmented Quotient Gradient System (AQGS) method designed to resolve convergence issues in solving the ACOPF problem. The AQGS method demonstrates superior convergence performance, computational efficiency, and a zero-probability occurrence of saddle solutions. A novel dynamical AQGS system is established, then the local optimal solutions of the ACOPF and the regular stable equilibrium points of the AQGS system are shown to correspond one-to-one; the AQGS is demonstrated to be completely theoretically stable. Based on this theoretical foundation, an ideal numerical property is developed. The relevant theorems are visually demonstrated through ACOPF examples, which support the high-performance algorithms introduced in the second part of this two-part paper.

Power line inspection is an important means to eliminate hidden dangers of power lines. It is a difficult research problem how to solve the low accuracy of power line inspection based on deep neural network (DNN) due to the problems of multi‐view‐shape, small‐size object. In this paper, an automatic detection model based on Feature visual clustering network (FVCNet) for power line inspection is established. First, an unsupervised clustering method for power line inspection is proposed, and applied to construct a detection model which can recognize multi‐view‐shape objects and enhanced object features. Then, the bilinear interpolation method is used to Feature enhancement method, and the enhanced high‐level semantics and low‐level semantics are fused to solve the problems of small object size and single sample. In this paper, FVCNet is applied to the MS‐COCO 2017 data set and self‐made power line inspection data set, and the test accuracy is increased to 61.2% and 82.0%, respectively. Compared with other models, especially for the categories that are greatly affected by multi‐view‐shape, the test accuracy has been improved significantly.

K-means has been widely used in solving a wide range of clustering problems arising in engineering and industrial applications, but it still suffers from several issues. To address these issues, a hierarchical K-means method enhanced by Trust-Tech (H-KTT) is presented in this paper. The proposed H-KTT method is composed of two stages. The first stage of H-KTT is a hierarchical K-means (H-K-means) method for enhancing K-means with better initial points. Second, the H-K-means method is further enhanced to find multiple high-quality clustering results by the Trust-Tech methodology. The H-KTT method was evaluated on several test datasets including the clustering of Automatic Meter Reading (AMR), popular in power grids, with promising results. In particular, the evaluation results indicate that the proposed H-KTT method can significantly improve both the quality and stability of the clustering results by the K-means method. Furthermore, while the K-means gives stochastic clustering results, the proposed H-KTT method usually gives deterministic clustering results.

In order to actively respond to the 14th Five-Year Plan, the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my countrys abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structures low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to Chinas carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithms path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithms path optimization is 15.72 less expensive than the genetic algorithms. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the 14th Five-Year energy plan.