Electrical and Electronics Engineering Department, University of Gaziantep, Şahinbey, 27310 Gaziantep, Turkey.
Comput Intell Neurosci. 2018 Apr 26;2018:6381610. doi: 10.1155/2018/6381610. eCollection 2018.
Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO.
不同的优化技术被用于前馈神经网络的训练和微调，以估计 STATCOM 的电压和无功功率。在第一部分，本文提出了使用静态补偿器（STATIC）调节 IEEE 总线电压，并讨论了通过潮流方程解决配备 STATCOM 的电力系统的有效方法。潮流方程使用牛顿-拉夫逊法等迭代算法求解。在第二部分，本文重点介绍了基于人工神经网络的估计技术作为迭代方法的替代方法。不同的训练算法已被用于训练人工神经网络的权重；这些方法包括反向传播、粒子群优化、随机青蛙跳跃算法和遗传算法。对这些方法中的每一种方法在 IEEE 总线数据上的性能进行了分析，以检验每种算法的效率。结果表明，SFLA 在训练 ANN 方面优于其他技术，其次是 PSO。
