Fuzzy logic–based rooted tree optimization algorithm for strategic incorporation of DG and DSTATCOM

Abstract

Summary In this study, a hybrid approach based on fuzzy logic and rooted tree optimization (RTO) algorithm is proposed for the strategic incorporation of distributed generation (DG) and distributed static compensator (DSTATCOM) in radial distribution system (RDS) to improve voltage profile, reduce losses, and maximize economic and environmental benefits. Fuzzy logic is applied to bring the objectives in the same range, and using RTO technique, the optimal site and size of the devices are determined. Considering the uncertainty in DG's output and time‐varying system load, the proposed technique is tested on 69‐bus and 118‐bus RDSs, and to check the accuracy and feasibility, the simulation results are compared with other existing techniques. The Wilcoxon signed rank test (WSRT) is applied to confirm the superiority of the proposed algorithm. Furthermore, comparative performance analysis is conducted between different planning schemes for facilitating the distribution system planner to select the most suitable one.

Cost-Benefit of Optimal Allocation of DSTATCOM in Distribution Networks Using Ant-Lion Optimization Algorithm

“…In a bid to resolve this allocation issue, researchers have utilized various optimization techniques considering various objectives taking into account various constraints. Some of the optimization techniques include hybrid genetic and ant colony algorithm [11], Fuzzy logic-based rooted tree optimization algorithm [12], differential evolution algorithm [13], Harmony Search Algorithm (HSA) [14], Particle Swarm Optimization (PSO) algorithm [15], Cuckoo Search Algorithm [16] and several other population-based search optimization techniques. Furthermore, a comprehensive performance analysis has been conducted between various placement scenarios, which will assist in the planning and establishment of a competent decision-making process in order to arrive at the best plan possible depending on the utility long-term objectives.…”

Simultaneous allocation of photovoltaic DG and DSTATCOM for techno‐economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

“…In 2019, applied Rooted Tree Optimization (RTO) algorithm with Hourly variation of DG power output‐based annual average of APL, system voltage improvement index, environmental benefit enhancement index, and net economic savings, 20 Grey Wolf Optimizer (GWO) algorithm for APL minimization with various load levels, 21 Chaotic Stochastic Fractal Search (CSFS) method to minimize the APL in distribution systems, 22 Improved Crow Search Algorithm (ICSA) to improve system voltage, reduce line losses, maximize economic benefit, and decrease pollutants' emission, 23 Modified Bat Algorithm (MBA) applied for mitigate power loss, bus voltage development stability betterment and operating cost minimization, 24 Multi‐Verse Optimizer (MVO) algorithm to minimize three technical‐economic system indices, which are apparent power loss, TVV, and annual losses cost, 25 Artificial Fish Swarm Optimization Algorithm (AFSOA) based to minimize the total APL in the EDS, 26 Ant Lion optimization (ALO) algorithm to minimize APL, RPL, and the new VSI, 27 Optimal coordination and management of OLTC, DSTATCOM, and DG to maximize annual cost savings using hybrid optimization algorithm, 28 and this paper reports a comprehensive review of the currently available methodologies on simultaneous DG and DSTATCOM allocation 29 …”

A New Multicarrier Sinusoidal Pulse Width Modulation (SPWM) Strategy based on Rooted Tree Optimization (RTO) Algorithm for Reducing Total Harmonic Distortion (THD) of Switched-Capacitor Nine-level Inverter in Grid-connected PV systems

“…The wetness degree (WD) of the root head determines the roots' orientation, they move at random in search of water, but when one or more roots detect moisture, they signal to others to update their way to be closer to the source of water, i.e. the best solution (Benamor et al, 2019;Sannigrahi et al, 2019).…”