PLC Fuzzy PID Controller of MPPT of Solar Energy Converter

Abstract

Development of the Maximum-Power-Point-Tracking (MPPT) scheme for solar mounts and rectifiers leftovers interesting. We design of high-sensitive fuzzy (HSF) Proportional-Integral-Derivative (PIDC) controller by means of Matlab and Programmable-Logic-Controllers (PLCs) for an adjusted of the MPPT scheme. This proposal is founded on a synergistic mixture of the Radial-Basis-Function-Neural Network (RBF-NN), Genetic Algorithm (GA), and Sugeno-Fuzzy-Logic (SFL) systems. The finest limits of PIDC and MPPT are strong-minded through optimization, wherever RBF-NN is adjusted by means of GA to reach the best key. Also, RBF-NN is rummage-sale to improve the PID limits (got from GA) for scheming HSFL-PIDC of the MPPT scheme. The HSFL-PIDC controller is extra planned to transfer in PLCs (STEP 75.5) for implementing the photovoltaic (PV) scheme. The all-inclusive scheme is extra tuned by solar limits under numerous operational circumstances to advance the solar recital in terms of accusing and correcting. The recital of the planned analog employed MPPT controller is assessed by interfacing it with a hardware prototype of dual photovoltaic (PV) scheme. The mixture controller configured the regulator signal founded on the interaction and in that way reduces the voltage mistake and the oscillation in the voltage regulator process. The achieved scheme is established to be well-organized and robust in refining solar charging and rectifying capability .

Research on Coal Volume Detection and Energy-Saving Optimization Intelligent Control Method of Belt Conveyor Based on Laser and Binocular Visual Fusion

“…Assuming a period of time, the coal flow volume changes in the interval of [q 0 , q 1 ], the corresponding belt given speed The fuzzy control query table obtained in the design of the fuzzy controller [19], which gives the frequency setting signal f and the speed deviation e and deviation variable change ec in order to ensure the PLC-based fuzzy control [20] of the velocity deviation e and the velocity deviation rate ec. The real-time reliability of the device is obtained by the vague control of the query table and storing the offline query table in the PLC memory card.…”

Design of vacuum annealing furnace temperature control system based on GA-Fuzzy-PID algorithm

“…Through genetic algorithm for global optimization, the decoding situation is shown in Fig 10, which represents the complete rule table of a parameter (such as kp). By applying it to the fuzzy controller, it can be seen from Eqs (20), (21), and ( 22) that the fuzzy controller outputs the optimal parameter increment at this time, calculates the updated values of kp, ki, and kd, and then implements PID control until the iteration number ends, retaining the optimal parameter combination as the optimal solution.…”

“…In recent years, although the economy has developed rapidly and people's living standards have improved, environmental pollution has also received increasing attention from the public, and air pollution and water pollution are urgent problems to be solved [18][19][20]. In the vacuum annealing process, reasonable and effective control of heating temperature and vacuum degree can be achieved to reduce system energy consumption, reduce pollution, and improve the ecological environment [21].…”

Control of direct current motor by using artificial neural networks in Internal model control scheme

“…For example, it was demonstrated in [7] that FLC represents a better option than traditional PID controllers. A comparison between PID, FLC, and fuzzy PID controllers revealed that fuzzy PID guarantees superior performance to the other two controllers [8]. An even better solution can be produced by applying an adaptive fuzzy PID controller [9].…”