Rapid generation of control parameters of Multi-Infeed system through online simulation

Abstract

Simulated Self-Generated - Particle Swarm optimization (SSG-PSO) toolbox that automatically generates PI control parameters very quickly in PSCAD is designed. This toolbox operates by utilizing transient simulation to evaluate objective function and converges the fitness values of objective function through PSO algorithm during run time simulation of Multi-infeed HVDC systems. Integral Square Error-Objective Function (ISE-OF) is used to accomplish the task. To make the toolbox faster, ranges are set for PSO generated value that limit the time of data acquisition for the objective function by only considering transition time of a system. This toolbox has a capability to optimize multiple controllers at same time. The PI values are generated faster and the results are showing markedly improved performance of a system during startup and under fault condition. The experimental results are presented in this paper.

Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

“…Finally, it is suggested that all objective functions with soft computing techniques (SA or PSO) can be used for tuning purposes of controller, but selection of one OF is necessary. When comparing the optimized results of active-reactive powers of SSG-PSO (Aazim et al, 2017) and SG-SA parameters, responses of ISE-PSO are slightly better than ITAE-SA. From all these performances of system, it might be possible that PSO with ITAE-OF can produce surprising results.…”

“…The simplex algorithm is one of the convergence algorithms to obtain optimized control values, in order to produce the smallest fitness value of an objective function through a soft computing optimization technique. This technique is used in this work to get the optimal values of multiple PI (Aazim et al, 2017) controller parameters (Saravanakumar et al, 2015). Several runs are executed to minimize the fitness value of an objective function , as shown in Figure 1.…”

“…These objective functions are individually used to find the fitness value that is further fed into simplex algorithm to obtain optimization parameters of multiple PI controllers. The difference of reference current (Iord) and measured current (Im) is error current (CERRR) (Aazim et al, 2017;Saravanakumar et al, 2015).…”

“…There are a lot of soft computing techniques used to tune the gains and time constant of the PI or PID controller (Stojic et al, 2017). Some examples are simplex algorithm (Gole et al, 2003;Zhao et al, 2007), linear programing based tuning (Oliveira et al, 2014), Modified Genetic Algorithm (Y. P. Wang et al, 2002), offline tuning of discrete-time fractional-order to minimize the cost function (Merrikh-Bayat et al, 2015), Hybrid PID-Artificial Neural Network (ANN) controller to control PWM signal for DC to DC conversion (Muruganandam and Madheswaran, 2013), fuzzy logic algorithm (Sahin and Altas, 2017;Ilyas et al, 2013;Kassem, 2013), State Transition Algorithm (STA) (Saravanakumar et al, 2015), Particle Swarm Optimization (PSO) (Aazim et al, 2017;Rajagopal and Ponnusamy, 2014), etc. With the help of these techniques, it became easy to select parameters intelligently and give optimal results in a very short time compared with the arbitrary or logarithmic searches.…”

“…Stability issues of this system have also been resolved by simulated self-generated (SSG) PSO (Aazim et al, 2017), but the question arose for the selection of compatible OF with PSO in the process of research and implementation. For this purpose, ISE-OF was selected after survey.…”