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The Convergence for (IEEE 14-Bus) System With Simple ( PSO ) Algorithm.
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... among Simple PSO and WIPSO with other optimization methods in the literature such as EP and SARGA [31], which are reported below in Table II . From this table it's clear that the reduction in PL from the base case are 9.6% at WIPSO, 9.1% at PSO, 1.5% at Evolutionary Programming (EP)and 2.5% at Self-Adaptive Real coded Genetic ( SARGA) algorithms. Fig.2 and Fig.3 shown the convergence of Simple PSO and WIPSO algorithms for this system and Fig. 4 demonstrates the voltage profile for this system with Simple PSO and WIPSO algorithms. Fig.4 it's explain that the average voltage at initial was about 1.048 and at PSO is about 1.059 and at WIPSO was about 1.082. ...
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... among Simple PSO and WIPSO with other optimization methods in the literature such as EP and SARGA [31], which are reported below in Table II . From this table it's clear that the reduction in PL from the base case are 9.6% at WIPSO, 9.1% at PSO, 1.5% at Evolutionary Programming (EP)and 2.5% at Self-Adaptive Real coded Genetic ( SARGA) algorithms. Fig.2 and Fig.3 shown the convergence of Simple PSO and WIPSO algorithms for this system and Fig. 4 demonstrates the voltage profile for this system with Simple PSO and WIPSO algorithms. Fig.4 it's explain that the average voltage at initial was about 1.048 and at PSO is about 1.059 and at WIPSO was about 1.082. ...
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... The IEEE 14-bus standard test is illustrated in Fig 7. Line and bus data for the system are available in Appendix A, as well as in the references [75]. The system comprises three transformers and five generators located on buses 1, 2, 3, 6, and 8, with a total demand of 259 MW. ...
The improvement of electrical power system efficiency by solving the Optimal Power Flow problem involves identifying optimal controllable setpoints. This study presents a methodology that integrates a Particle Swarm Optimization algorithm with Artificial Neural Networks to minimize losses and costs. The objective is to reduce both generation fuel costs and transmission line losses while adhering to system constraints. The PSO algorithm is employed iteratively to generate initial results, which are then refined through ANN training and evaluation. This iterative process aids in determining the optimal parameters for the final PSO model.. The proposed approach is applied to the IEEE 30-bus and IEEE 14-bus standard system, resulting in a fuel cost of (797.25 /h), (2.79 MW) for IEEE-14 bus. Experimental findings underscore the method's effectiveness compared to other approaches documented in the recent literature in terms of minimizing the objective function.
... The improved genetic algorithm (IGA) is proposed to solve the problem that the simple GA is inefficient in the application of power system reactive power optimization, where the coding method, fitness function, initial population generation, and crossover and mutation strategy are modified (Chang and Zhang, 2017;Liu et al., 2022). Particle swarm optimization (PSO) is also widely applied to solve this problem (Li et al., 2017;Khan et al., 2020;Shri et al., 2021). An improved particle swarm optimization and Pareto archive algorithm are combined to solve the multi-objective reactive power optimization problem, and it outperforms the non-dominated sorting genetic algorithm II (NSGA-II) . ...
The integration of renewable power generation introduces randomness and uncertainties in power systems, and the reactive power optimization with interval uncertainty (RPOIU) problem has been constructed to acquire the voltage control strategy. However, the large amount of uncertain data and the coexistence of discrete and continuous control variables increase the difficulty of solving the RPOIU problem. This paper proposes a data-driven hybrid interval reactive power optimization based on the security limits method (SLM) and the improved particle swarm optimization (IPSO) to solve the RPOIU problem. In this method, the large amount of historical uncertain data is processed by data-driven to obtain the boundary of optimal uncertainty set. The control variable optimization is decomposed into continuous variable optimization and discrete variable optimization. The continuous variables are optimized by applying the SLM with the discrete variables fixed, and the discrete variables are optimized by the IPSO with the continuous variables fixed. The two processes are applied alternately, and the values of the control variables obtained by each method are used as the fixed variables of the other method. Based on simulations carried out for the IEEE 30-bus system with three optimization methods, we verified that the voltage control strategy obtained by the data-driven hybrid optimization could ensure that the state variable intervals satisfied the constraints. Meanwhile, the values of the real power losses obtained by the proposed method were smaller than those obtained by the SLM and IPSO. The simulation results demonstrated the effectiveness and value of the proposed method.
