A new gumption approach for economic dispatch problem with losses effect based on valve-point active power

High Performance Control of Grid Connected Cascaded H-Bridge Active Rectifier Based on Type II-Fuzzy Logic Controller with Low Frequency Modulation Technique

“…For m= 5,7,11,13,17,19,23, 25 To reach goals in the equations (1-3), Particle Swarm Optimization (PSO) approach has been used to optimize the nonlinear transcendental equations of the SHE-PWM method. Figure 3 shows that the obtained solutions by the PSO optimization techniques are so complete and cover wide range for the control purposes.…”

“…It should be mentioned that the switching transitions of the active rectifier applied by using optocoupler or isolation transformer. Theses components are required, because signal of the controller should be isolated from the voltage that is imposed to the gate driver of the Power MOSFET or IGBT switches [15][16][17].…”

“…In the recent years, the Fuzzy Logic Controller has been employed in various applications. One of the most important reasons which convinced the scientist to employ Fuzzy logic systems is the high capability of decision making in Fuzzy Controller [13][14][15][16][17].…”

“…5] and[16][17][18]. The main difference is using Type II fuzzy logic controller instead of PI controllers to improve the performance of the Optimal SHE-PWM technique.…”

Robust, fast and optimal solution of practical economic dispatch by a new enhanced gradient‐based simplified swarm optimisation algorithm

“…To overcome these insufficiencies, the second meta‐heuristic optimisation techniques have been introduced to solve the various ED problems. The recent state‐of‐the art literatures published in 2012 in the area of second group are GA‐API [1], modified group search optimiser (MGSO) [10], Firefly algorithm (FA) [11], iteration particle swarm optimisation with time varying acceleration coefficients (IPSO‐TVAC) [12], improved PSO (IPSO) [13], continuous quick group search optimiser (CQGSO) [14], differential harmony search (DHS) [15], modified PSO [16], fuzzy adaptive chaotic ant swarm optimisation (FCASO) [17], augmented Lagrange hopfield network (ALHN) [18], hybrid chaotic PSO and sequential quadratic programming (CPSO‐SQP) [19], seeker optimisation algorithm (SOA) [20], enhanced augmented Lagrange hopfield network (EALHN) [21], hybrid accelerated biogeography‐based optimisation and modified differential evolution (aBBOmDE) [22] and a new gumption approach [23]. The solution to the ED problems using these meta‐heuristic optimisation methods proposed in the aforementioned literatures consumes huge executing time.…”