Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Department of Electronic and Electrical Engineering, Nha Trang University, Nha Trang, Khanh Hoa, Vietnam.
Comput Intell Neurosci. 2022 Aug 25;2022:6379141. doi: 10.1155/2022/6379141. eCollection 2022.
This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to predict the leakage current of insulators. The developed algorithm was utilized for the online monitoring systems, which were completely installed on the 69 kV and 161 kV transmission towers in Taiwan. This hybrid algorithm utilizes the local meteorological data as input parameters combined with the extracted enhanced data: the percentage of spark discharge areas and the brightness change in the image of the discharge phenomenon. These data with a high correlation with the leakage current are utilized as input vectors to improve the accuracy and effectiveness of the developed hybrid model. The performance of the developed algorithm is compared with a traditional PSO-based neural network and backpropagation neural network (BPNN) to evaluate and analyze. The comparative simulation results prove the effectiveness of the combination of hybrid PSO-GA-based neural network and surface discharge data, which achieved a maximum improvement of 38.54% MSE, 10.62% MAPE, and 3.41% square for 161 kV data and 39.28% MSE, 12.62% MAPE, and 1.61% square for 69 kV data. Moreover, the data with enhanced inputs outperform the traditional data in most benchmark factors, improving the accuracy and effectiveness in defining the deteriorative insulators. The developed methodology with a noticeable improvement was utilized in the online monitoring system to reduce the operational and maintenance cost of transmission lines in Taiwan Power Company.
本研究提出了一种新的混合优化算法，即基于粒子群算法（PSO）和遗传算法（GA）的神经网络，用于预测绝缘子的泄漏电流。所开发的算法用于在线监测系统，这些系统完全安装在台湾的 69kV 和 161kV 输电塔上。该混合算法利用局部气象数据作为输入参数，并结合提取的增强数据：火花放电区域的百分比和放电现象图像的亮度变化。这些与泄漏电流高度相关的数据被用作输入向量，以提高开发的混合模型的准确性和有效性。将所开发的算法的性能与传统的基于 PSO 的神经网络和反向传播神经网络（BPNN）进行比较，以进行评估和分析。比较模拟结果证明了混合 PSO-GA 神经网络与表面放电数据相结合的有效性，对于 161kV 数据，最大改进了 38.54%均方误差（MSE）、10.62%平均绝对误差（MAPE）和 3.41% 平方，对于 69kV 数据，最大改进了 39.28%MSE、12.62%MAPE 和 1.61% 平方。此外，增强输入数据在大多数基准因素中优于传统数据，提高了定义劣化绝缘子的准确性和有效性。该具有显著改进的方法已用于在线监测系统中，以降低台湾电力公司输电线路的运行和维护成本。
