PID controlled synchronous motor for power factor correction

Abstract

In this paper, a synchronous motor controlled by a PID based on a PIC 18F452 microcontroller has been studied under three different working conditions using varying excitation currents. Due to the complexity of PID parameters such as integrative and derivative terms, their conversion to digital systems has proven difficult. Hence, the collection of errors in a specified time period has been multiplied be means of a sampling period rather than complex integral algorithms. The difference between the error rate and its previous value has been divided into a sampling period to obtain the derivative operation. Therefore, a PID controlled algorithm has been embedded into a microcontroller which is easily implemented without complex algorithms. In addition, the design of this study includes an LCD based visual interface, allowing users to instantly monitor the current, the voltage and the power factor of the synchronous motor.

Lojik Devre Laboratuvarları için Entegre Test Devresi Tasarımı

“…AND [6]. Mikrodenetleyiciler, tıp elektroniği [7], [8], güç elektroniği [9] ve yenilenebilir enerji alanlarında [10] sıklıkla kullanılmaktadır.…”

Fuzzy Logic based Power Factor Control of Synchronous Machine

“…If this average value is zero, then all of the power being transmitted is called reactive power. Customers would not normally be charged for using reactive power because they are consuming some energy half the time, and giving it all back the other half of the time, for a net use of zero [3]. Voltage and current may not be in phase.…”

Metaheuristic linear modeling technique for estimating the excitation current of a synchronous motor

“…The relationships among the SM parameters are mostly complex and nonlinear [5,[12][13][14][15]. Researchers have suggested artificial intelligence (AI)-based nonlinear modeling techniques, such as proportional plus integral plus derivative [16], pulse width modulation [17][18][19][20], fuzzy logic [2,3], Kalman filter-based methods [7,15,21], artificial neural networks (ANNs) [22,23], particle swarm optimization (in real-time applications) [24], intuitive k-nearest neighbor (k-NN) estimator and genetic algorithm (GA) [5,25], and adaptive ANNs [4] for modeling the parameters and/or predicting the excitation current of SMs and permanent magnet synchronous machines. The modeling of SM parameters using modern AI-based methods for excitation current estimation was realized in recently published studies [4,5,17].…”