Jae-Sub Ko

Maximum Torque Control of an IPMSM Drive Using an Adaptive Learning Fuzzy-Neural Network

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter’s rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

Development of a Novel Tracking System for Photovoltaic Efficiency in Low Level Radiation

This paper proposes a novel tracking algorithm considering radiation to improve the power of a photovoltaic (PV) tracking system. The sensor method used in a conventional PV plant is unable to track the sun’s exact position when the intensity of solar radiation is low. It also has the problem of malfunctions in the tracking system due to rapid changes in the climate. The program method generates power loss due to unnecessary operation of the tracking system because it is not adapted to various weather conditions. This tracking system does not increase the power above that of a power of tracking system fixed at a specific position due to these problems. To reduce the power loss, this paper proposes a novel control algorithm for a tracking system and proves the validity of the proposed control algorithm through a comparison with the conventional PV tracking method.

Maximum Power Point Tracking Control of PV System for DC Motors Drive with Neural Network

This paper presents an application of a Neural Network(NN) for Maximum Power Point Tracking (MPPT) of PV supplied DC motor. A variation of solar irradiation is most important factor in the MPPT of PV system. That is nonlinear, aperiodic and complicated. NN was widely used due to easily solving a complex math problem. The paper consists of solar radiation source, DC-DC converter, DC motor and load(cf, pump). NN algorithm apply to DC-DC converter through an Adaptive control of Neural Network, calculates Converter-Chopping ratio using an Adaptive control of NN. The results of an Adaptive control of NN compared with the results of Converter-Chopping ratio which are calculated mathematical modeling and evaluate the proposed algorithm. The experimental data show that an adequacy of the algorithm was established through the compared data.

Development of a Thermoelectric Cooling System for a High Efficiency BIPV Module

This paper proposes a cooling system using thermoelectric elements for improving the output of building integrated photovoltaic (BIPV) modules. The temperature characteristics that improve the output of a BIPV system have rarely been studied up to now but some researchers have proposed a method using a ventilator. The efficiency of a ventilator depends mainly on the weather such as wind, irradiation etc. Because this cooling system is so sensitive to the velocity of the wind, it is unable to operate in the nominal operating cell temperature (NOCT) or the standard test condition (STC) which allow it to generate the maximum output. This paper proposes a cooling system using thermoelectric elements to solve such problems. The temperature control of thermoelectric elements can be controlled independently in an outdoor environment because it is performed by a micro-controller. In addition, it can be operated around the NOCT or the STC through an algorithm for temperature control. Therefore, the output of the system is increased and the efficiency is raised. This paper proves the validity of the proposed method by comparing the data obtained through experiments on the cooling systems of BIPV modules using a ventilator and thermoelectric elements.

Hybrid Artificial Intelligent Control for Speed Control of Induction motor

This paper is proposed hybrid artificial intelligent controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response

Efficiency Optimization Control of SynRM Drive

This paper proposes an efficiency optimization control algorithm for a synchronous reluctance motor (SynRM) which minimizes the copper and iron losses. Also, this paper presents a speed estimated control scheme of SynRM using artificial neural network (ANN). There exists a variety of combinations of and -axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of and -axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

Efficiency Optimization Control of SynRM Drive using Multi-AFLC

Optimal efficiency control of synchronous reluctance motor (SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller (AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of and d-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of and -axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

Efficiency Analysis of PV Tracking System with PSA Algorithm

This paper analyzes efficiency of photovoltaic(PV) tracking system using position solar algorithm(PSA). Solar location tracking system is needed for efficiently and intensively using PV system independent of environmental condition. PV tracking system of program method is presented a high tracking accuracy without the wrong operating in rapidly changing insolation by the clouds and atmospheric condition. Therefore, this paper analyzes efficiency of PV system using PSA algorithm for more correct position tracking of solar. Also, controlled altitude angle and azimuth angle by applied algorithm is compared with data of korea astronomy observatory. And this paper analyzes the tracking error and generation efficiency then proves the validity of applied algorithm.

Development of Novel Algorithm for Anti-Islanding of Grid-Connected PV Inverter

This paper proposes novel algorithm for anti-islanding of grid-connected photovoltaic(PV) inverter. The islanding of PV systems can cause a variety of problems such as deterioration in power quality and electric shock. To prevent islanding, many anti-islanding methods are researched. Typical methods of anti-islanding are active frequency drift(AFD) and active frequency drift positive feedback(AFDPF). However, the AFD has problem that widely exists non diction zone(NDZ). The AFDPF is a method that improves the AFD method and is detected islanding by changing the chopping fraction(cf). However, The AFDPF does not detect when cf is very small and does not satisfy the IEEE Std. 929-2000 when cf is very big. Therefore, this paper proposes novel anti-islanding method that is simple to implement using virtual resister. The anti-islanding method proposed in this paper is compared with conventional method. The validity of this paper is proved using this result.

Maximum torque control of IPMSM drive with Multi-MFC

This paper proposes maximum torque control of IPMSM drive using multi model reference adaptive fuzzy controller (Multi-MFC) and artificial neural network (ANN). This control method is applicable over the entire speed range which considered the limits of the inverters current and voltage rated value. For each control mode, a condition that determines the optimal-axis current for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Multi-MFC and ANN controller. The hybrid combination of adaptive and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed and current control of IPMSM using MFC and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system and the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Multi-MFC and ANN controller.