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# Performance analysis of electronic power transformer based on neuro-fuzzy controller

- Hakan Acikgoz
^{1}, - O. Fatih Kececioglu
^{2}, - Ceyhun Yildiz
^{2}, - Ahmet Gani
^{2}and - Mustafa Sekkeli
^{2}Email authorView ORCID ID profile

**Received:**23 March 2016**Accepted:**29 July 2016**Published:**15 August 2016

## Abstract

In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 V_{rms} AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions.

## Keywords

- Transformers
- Power electronic transformer
- Neuro-fuzzy controller
- PWM rectifier
- DAB converter

## Background

Generation, transmission and distribution of electrical energy are the most important factors in modern energy systems and transformers provide the most important role in these systems. Transformers which carry out many fundamental tasks such as galvanic isolation, voltage transformation, noise decoupling are widely used in electric power systems. It is well-known that classic (50/60 Hz) transformers have many positive features such as high efficiency, low cost and high reliability (Ronan et al. 2002; Yang et al. 2015; Zhao et al. 2013; Wang et al. 2007; Hwang et al. 2013). However, these conventional transformers have many undesirable drawbacks. These drawbacks include: (1) Conventional transformers have large size and weight because of their copper windings and iron core. (2) Conventional transformers are a passive component between the high and low voltages. Therefore, when voltage sags and swells occur at the input side, the output side is affected by these conditions. Harmonics in the output currents affect the input currents of transformers. In this case, harmonics can spread to the grid or can increase losses in the primary winding. Therefore, transformers have poor voltage regulation and low harmonic isolation. (3) Mineral oils used in transformers can be harmful when exposed to the environment in case of any fault in the transformer.

In recent years, with rapid advances in microprocessors and power electronics devices, many studies have been realized in order to improve performance of transformers. A new transformer was proposed by McMurray (1970). These transformers were called as electronic power transformer (EPT) or solid state transformer (SST). The main feature of these transformers is having ability to perform the same tasks with conventional transformers. Besides, EPTs possess many advantages over conventional transformers such as voltage sag and swell compensations, fixed AC output voltage, instantaneous voltage regulation, power factor correction, reactive power compensation, harmonic isolation and all of these advantages can be realized on a single circuit (Bifaretti et al. 2011; Dujic et al. 2013; Kang et al. 1999; Kececioglu et al. 2016; Grider et al. 2011; Xu et al. 2014; Acikgoz and Sekkeli 2014; Zhao et al. 2014; Yang et al. 2015). Many studies which focus on design and control of EPT structures have been realized by many researchers and institutes in the literature. In generally, two approaches were proposed for EPT structure; with DC-link and without DC-link (Falcones et al. 2010). EPT structure with DC-link consisting input, isolation and output stages has several key features such as reactive power and voltage sag/swell compensations (Yang et al. 2015; Lai et al. 2005). Pulse width modulation (PWM) rectifiers are widely used at input stage of these EPT structures to convert AC voltage into DC voltage because of their good dynamic response, unity power factor and regulated DC bus voltage. Isolation stage has DC–DC converter and high frequency (HF) transformer, and output stage has single or three-phase inverter which generates the desired output voltage and power (Falcones et al. 2010; Yang et al. 2015; Hwang et al. 2013).

During the last decades, intelligent control systems have been used in various applications. Neuro and fuzzy controllers have been outstanding intelligent control systems. Complexity and uncertainty of systems have promoted researchers to develop intelligent and adaptive control systems. Within this scope, many studies have been carried out to analyze performances of intelligent control systems (Jang et al. 1997). These control systems have been applied to many control systems and it has been obtained successful results. Development of intelligent control systems has milestones. Zadeh (1965) proposed fuzzy sets concept. This concept has led up the development of control systems that have of human reasoning capability. McCulloch and Pitts (1943) developed mimic biological neural systems computational abilities. Control systems have gained learning capability by this technique. Another approach is neuro-fuzzy controller (NFC). NFC that has nonlinear, robust structure and based on FLC whose functions are realized by ANN is one of these intelligent controllers (Jang et al. 1997; Mohagheghi et al. 2007; Tuncer and Dandil 2008). The most important feature of this controller is that it does not need the mathematical model of the controlled system.

In control of PWM rectifiers, DC bus voltage and dq-axis currents are commonly controlled by using Proportional-Integral (PI) controller because of its simple structure (Dannehl et al. 2009; Blasko and Kaura 1997). However, PI controller has undesirable features including slow response, large overshoots, oscillations, and it needs a mathematical model of the system to be controlled. Recently, intelligent and robust controllers, based on fuzzy logic controller (FLC), linear quadratic regulator (LQR), sliding mode controller (SMC), Robust H∞ controller and predictive control (PC), have been successfully used in many studies. To obtain a good performance from EPT structure, intelligent controllers can be used in transient and steady-state conditions (Bouafia and Krim 2008; Bouafia et al. 2010; Yu et al. 2010; Brando et al. 2010; Zhao et al. 2012; Djerioui et al. 2014; Liu et al. 2009; Hooshmand et al. 2012).

In this paper, robust and nonlinear control strategy based NFC controller is proposed for EPT structure in order to achieve a good dynamic response. Designed NFCs have two inputs, single output and six layers. This paper is organized as follows. Power circuit and mathematical model of EPT structure is given in section two. The description of the NFC and its training algorithm are explained in section three. The simulation results related to the proposed EPT structure are comprehensively presented in section four. Section five provides the conclusions of this study.

## Mathematical model of EPT

_{1}and ϴ

_{1}are amplitude modulation index and modulation angle for PWM rectifier used in the input stage. m

_{2}and ϴ

_{2}are amplitude modulation index and modulation angle for PWM inverter used in the output stage. u

_{sa}, u

_{sb}and u

_{sc}are input voltages. u

_{La}, u

_{Lb}and u

_{Lc}are output voltages. U

_{dc}is DC bus voltage of input stage. u

_{0a}, u

_{0b}and u

_{0c}are AC voltages in the output stage. u

_{la}, u

_{lb}and u

_{lc}are AC voltages in the input stage (Liu et al. 2009; Hooshmand et al. 2012; Acikgoz et al. 2015). According to three-phase stationary reference frame a-b-c, dynamic model of proposed EPT structure can obtained by Eqs. (1) to (7). However, the parameters of the dynamic differential equations are time-varying and should be transformed to the synchronously rotating reference frame using Park’s transformer in order to obtain time-invariant equations (Liu et al. 2009; Hooshmand et al. 2012). Thus, the dynamic equations in the d-q rotating reference frame are as follows:

## Implementation and design of neuro-fuzzy controller

_{i}is the input variable, y is the output variable, linguistic variables of prerequisites with A

_{i}

^{j}µ

_{Ai}

^{j}(x

_{i}) membership function and the A

_{i}

^{j}ϵ R are the coefficients of linear f

_{i}= (x

_{1}, x

_{2}, …, x

_{n}) function. Structure of NFC which is used in control algorithms is shown in Fig. 3. As seen in Fig. 3, NFC has two inputs, one output and six layers. Five membership functions were chosen for each input (Jang et al. 1997; Tuncer and Dandil 2008; Buckley and Hayashi 1994).

_{ij}and m

_{ij}, which are input parameters, represent the parameters of membership functions to be adapted. X

_{i}represents the input of ith cell of second layer. Similar to FLC, the third layer of NFC consists of rule base and fuzzy rules are determined in this layer.

_{j}

^{3}here represents the input of jth cell of the third layer. The output of the system defined by using central clarification for Mamdani fuzzy logic:

As shown in Fig. 3, inputs of NFC were selected as the error and the change of error. Five membership functions are used for each input. In the proposed NFC structure, precondition parameters of membership layer have been trained in the simulation model. During the simulation studies, output parameters have been trained using back-propagation learning algorithm. These parameters are adapted until the desired performance is reached.

### Control of the input stage

_{q}and V

_{d}values are obtained from the outputs of NFCs. These voltages are sent to PWM block, which generates required signals for driving the semiconductor-switching element. Moreover, an anti-wind up integrator is used to limit the output of NFC and compensate for steady state error (Liu et al. 2009; Hooshmand et al. 2012).

Parameters of PI controllers

Parameters | Voltage control | Current control |
---|---|---|

Proportional gain (K | 0.2 | 4 |

Integral gain (K | 20 | 60 |

### Control of the isolation stage

Parameters of PI controller for DAB

Parameters | Value |
---|---|

Proportional gain (K | 0.002 |

Integral gain (K | 6 |

_{DAB1}and U

_{DAB2}are input and output DC voltages of DAB converter, f

_{DAB}is switching frequency of DAB converter, L

_{DAB}is leakage inductance, d

_{DAB}is ratio of time delay between two bridges to one-half of switching period (Yang et al. 2015; Zhao et al. 2013). Figure 10 shows small signal model of DAB converter.

### Control of the output stage

_{d}, V

_{q}) in the synchronous rotating d-q reference frame. Then, these voltages are compared with the reference values of V

_{d}and V

_{q}. The outputs of PI controller are transformed to U

_{α}− U

_{β}voltage which is used to generate inverter gate pulses (Liu et al. 2009; Hooshmand et al. 2012). Equations of these transformations are given as follows:

_{d}and d

_{q}are duty cycles corresponding to the dq-axes respectively. Equations (40) and (41) are obtained (Hiti et al. 1994; Tuomas et al. 2015);

_{dc2}is DC voltage of three-phase inverter, D

_{d}is d-axis duty cycle, D

_{q}is q-axis duty cycle, i

_{d}is d-axis current, i

_{q}is q-axis current, V

_{Ld}and V

_{Lq}are dq-axis grid voltages (Hiti et al. 1994; Tuomas et al. 2015). Based on the above equations, small signal model of three-phase inverter has been derived and shown in Fig. 14.

## Simulation results

Parameters of EPT structure used in simulation study

Parameters | Value |
---|---|

Grid voltage | 800 V |

DC voltage of input stage | 2000 V |

DC voltage of isolation stage | 400 V |

Power frequency | 50 Hz |

Grid resistance and inductance | 0.1 Ω, 5 mH |

HF transformer | 5:1, 1000 Hz, 30 kVA |

Capacitors | 3.3 mF, 4.7 mF |

LC filter | 2 mH, 200 µF |

## Conclusion

EPT structure which has many superior features such as high power factor, voltage sag/swell compensation, multi-functionality, excellent power quality compared with conventional transformer is proposed in this study. EPT structure in this study is composed of input, isolation and output stages. Three-phase PWM rectifier at the input stage is not only used in order to convert AC to the constant DC voltage, but also has reactive power compensation ability. PI controllers are generally used in PWM rectifiers due to their simple structures. However, PI controller needs mathematical model of the system to be controlled and has undesirable characteristics such as slow response, large overshoots and oscillation. To cope with these problems, neuro-fuzzy controller that has nonlinear, robust structure and which does not require the mathematical model of the system to be controlled is preferred in this study. Dual active bridge converter at isolation stage is used for DC–DC conversion and is controlled by neuro-fuzzy controller in order to obtain constant DC bus voltage. Three-phase inverter that provides the desired power and voltage to load is located at the output stage. After designing of all stages, a number of simulation studies have been carried out in order to verify performance of EPT structure with the proposed controller under voltage harmonics, voltage flicker and voltage sag/swell conditions. The simulation results illustrate that EPT structure with the neuro-fuzzy controller provides more superior performance than PI controller with respect to rise time, settling time, overshoot, and power factor in all test conditions and is not sensitive these conditions and is capable of regulating output voltages and compensating disturbances in grid voltages. Moreover, proposed EPT provides fast and controllable AC/DC responses because of strong structure of the neuro fuzzy controller and thus, improves the stability of power system.

## Declarations

### Authors’ contributions

MS provided the basic idea of the research and supervise. HA researched the background literature, mathematical model of electronic power transformer and neuro fuzzy controller. HA, CY and FK modelled small signal models of three-phase PWM rectifier, DAB converter and three-phase inverter. FK, AG and HA developed the Simulink/MATLAB model of the power electronic transformer based on neuro-fuzzy controller, organized and drafting of the manuscript. All authors read and approved the final manuscript.

### Competing interests

The authors declare that they have no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

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