Ngei Urbanus Mwanzia, Nyete Abraham Mutunga, Moses Peter Musau, Wekesa Cyrus
Department of Electrical, Electronic and Information Engineering, University of Nairobi, Nairobi, Kenya.
Department of Electrical, Electronic and Information Engineering, South Eastern Kenya University, Kitui, Kenya.
Heliyon. 2024 Nov 23;10(23):e40682. doi: 10.1016/j.heliyon.2024.e40682. eCollection 2024 Dec 15.
The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from to $ . Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from to $ . Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.
全球电力需求的增长已导致当今大多数电网过载，尤其是在撒哈拉以南非洲地区。可以通过扩展现有发电和输电系统来满足不断增长的负荷需求。然而，新电力基础设施的建设受到资金和技术限制。因此，电网只能在过载条件下运行，存在高功率损耗和许多电能质量（PQ）问题。灵活交流输电系统（FACTS）装置可以提高现有输电网络的输电能力，而无需建设新的电力基础设施。本文提出了一个包含功率损耗（PL）最小化、电压偏差（VD）最小化和运行成本（OC）最小化的多目标函数，并使用一种新颖的算法进行求解。本文提出了一种新颖的遗传算法改进粒子群优化（GA - IPSO）技术，用于优化FACTS装置的尺寸和位置。所考虑的三种FACTS装置为静止同步补偿器（STATCOM）、晶闸管控制串联电容器（TCSC）和统一潮流控制器（UPFC）。所提出的技术使用IEEE - 33节点测试系统进行了验证，该系统是一个常用的基准辐射状配电系统（RDS）。对这三种FACTS装置分别进行了优化，也进行了组合优化。在单独优化中，对单个FACTS装置的尺寸和位置进行了优化。对于组合优化，在同一测试系统中对多个装置的尺寸和位置进行了优化。在单独优化中，UPFC通过将有功功率损耗降低38.44%以及将运行成本从[具体数值1]降至[具体数值2]产生了最佳结果。在组合优化中，TCSC、STATCOM和UPFC的组合通过将有功功率损耗降低56.09%以及将运行成本从[具体数值3]降至[具体数值4]给出了更好的结果。GA - IPSO技术与其他算法（如粒子群优化（PSO）、遗传算法（GA）、改进灰狼优化（IGWO）和差分进化算法（DEA））的比较表明，所提出的混合技术在解决FACTS优化问题方面更优越、更高效。
