This paper presents the design and realization of a digital PV simulator with a Push-Pull Forward (PPF) circuit based on the principle of modular hardware and configurable software. A PPF circuit is chosen as the main circuit to restrain the magnetic biasing of the core for a DC-DC converter and to reduce the spike of the turn-off voltage across every switch. Control and I/O interface based on a personal computer (PC) and multifunction data acquisition card, can conveniently achieve the data acquisition and configuration of the control algorithm and interface due to the abundant software resources of computers. In addition, the control program developed in Matlab/Simulink can conveniently construct and adjust both the models and parameters. It can also run in real-time under the external mode of Simulink by loading the modules of the Real-Time Windows Target. The mathematic models of the Push-Pull Forward circuit and the digital PV simulator are established in this paper by the state-space averaging method. The pole-zero cancellation technique is employed and then its controller parameters are systematically designed based on the performance analysis of the root loci of the closed current loop with k_i and R_L as variables. A fuzzy PI controller based on the Takagi-Sugeno fuzzy model is applied to regulate the controller parameters self-adaptively according to the change of R_L and the operating point of the PV simulator to match the controller parameters with R_L . The stationary and dynamic performances of the PV simulator are tested by experiments, and the experimental results show that the PV simulator has the merits of a wide effective working range, high steady-state accuracy and good dynamic performances. In recent years, due to the serious problems of energy shortages, environmental pollution and energy security throughout the world, the development and utilization of clean renewable energy is becoming an important way to improve the energy structure, reduce environment pollution and persistently ensure the energy supply all over the world [1] , [2] . The worldwide interest in renewable energy has greatly promoted the development of PV generation technology and industry [3] - [5] . In the research and development of PV generation systems, if a real PV array is used to test and verify the correctness and performance of the power electronic equipment and control systems, it would be both costly and time-consuming. Therefore, the output characteristics of a PV array under various operating conditions are always simulated by a PV simulator, which can be made by an analog circuit, a digital circuit or an analog and digital hybrid circuit. There are several methods to implement a PV simulator, including: Despite achieving adequate simulative effects, most of the PV simulators still exhibit some problems, such as a limited effective working range, bigger ripples of the output voltage and current, lack of systematic modeling and design methods for the controller parameters, lower development efficiency, and so on. Considering the above drawbacks, the digital PV simulator presented in this paper adopts modular hardware, configurable software, systematic modeling and design methods for the controller parameters so as to obtain the merits of a wide effective working range, high steady-state accuracy, good control performances, high reliability and development efficiency. A comparison of the characteristics of the different methods for PV simulators is listed in Table I . The overall configuration of the digital PV simulator is shown in Fig. 1 . It mainly consists of a DC power supply, a DC-DC converter, a digital controller, a PWM signal generation circuit, a measuring and signal conditioning circuit, a driving and isolating circuit, and so on. The main operation principle is as follows: the DC power supply provides a steady DC input voltage for the DC-DC converter. According to the output voltage measured in real-time, the digital controller calculates the reference current signal by the I-V characteristic model and then calculates the duty ratio of the converters’ PWM signals by a current closed-loop regulator. After signal conversion, the duty ratio is converted into a signal-level control voltage which is sent to the PWM signal generation circuit to generate PWM signals. The PWM signals control the states of the power switches so as to adjust the converter’s output voltage and current after signal isolation and power driving. Finally, the PV simulator can always work at a certain operational point at which the output voltage and output current of the converter can satisfy both the load characteristics and the I-V curve of the PV array. Thus, the output characteristics of the PV array can be easily simulated by the digital PV simulator. The main circuit of the DC-DC converter adopts a Push-Pull Forward (PPF) circuit in the PV simulator. A full-wave rectifier is applied in the secondary windings of the high frequency transformer so as to reduce the conduction losses of the rectifying diodes with a low-output voltage and a high-output current. An LC filter is used to provide a continuous load current and to reduce harmonics. Fig. 2 shows the topological structure of the DC-DC converter. It can be seen from the figure that the PPF circuit is different from a conventional Push-Pull converter in that a clamping capacitor is connected in series between the two primary windings of the high frequency transformer and between the two power switches. As a result, it has the advantages of both the Push-Pull converter and Forward converter, and it can restrain the magnetic biasing of the core for the DC-DC converter, magnetize the magnetic core bi-directionally and reduce the spike in the turn-off voltage across each switch. Therefore, the PPF circuit is becoming a kind of preponderant circuit topology for low voltage and high current output applications. In steady state, the PPF circuit has 8 operating modes in a switch cycle. Each power switch matches with the 4 operating modes of one cycle. In the following analysis, it is assumed that all of the power switches and diodes are ideal components and that the on-state voltage drops for all of the power electronic components are negligible [19] . The equivalent circuits of the former four operating modes are shown in Fig.3 . 1) Mode [t₀-t₁]: Prior to t₀ , the power switches S ₁ and S ₂ are off. The primary current freewheels through U_in(+) → N_p2 → C_b → N_p1 → U_in(-) and forms a circular current which is I_a = i_p1 = i_p2 . During this period, the rectifying diodes of the secondary windings turn on simultaneously, and the current through each of the rectifying diodes is i_DR1 = i_DR2 = I_o /2. At t₀ , the filtering inductor current runs down to the minimum value I_Lfmin . At t₀ , S ₁ turns on. U_in and u_cb are applied to the leakage inductances L_σ of N_p1 and N_p2 , respectively. i_p1 increases rapidly due to U_in being in the same direction as i_p1 , and i_p2 decreases rapidly due to u_cb being in the opposite direction as i_p2 . Corresponding with i_p1 and i_p2 , i_DR1 and i_DR2 increase and decreases respectively because of the affection of the magnetic circuit. During this period, the filtering inductor current increases gradually from I_Lfmin . This mode ends when i_DR1 is equal to the load current, both i_p2 and i_DR2 are decreased to 0 and the commutation process ends. The voltage across S ₁ is always 2 U_in during this period. 2) Mode [t₁-t₂]: In this mode, U_in and u_cb are applied to the magnetizing inductor L_m and the primary reduced inductance of the filtering inductances L_f of N_p1 and N_p2 , respectively. Each half of the changing rate of the magnetizing current and load current is shared by the primary winding. i_p1 continues to increase in the original direction defined as the reference direction whereas i_p2 increases gradually from 0 in the opposite direction. This mode lasts from t₁ to t₂ and S ₁ turns off at t₂ . Because the circuit works like two forward converters in parallel, the circuit is called Push-Pull Forward circuit or converter. At the instant of S ₁ turning off, i_p1 runs up to the maximum value, because both the magnetizing current and primary reduced current of the load current flow through S ₁ . The filtering inductor current also runs up to the maximum value accordingly. The voltage across S ₁ is also 2 U_in during this period. 3) Mode [t₂-t₃]: After S ₁ turns off, the body diode D_S2 of S ₂ is forced to turn on to continue the leakage inductance current because i_p1 is always larger than i_p2 before t₂ . The energy of the leakage inductance is released to charge the clamping capacitor C_b through the low impedance loop N_p1 → D_S2 → C_b . U_in and u_cb are applied to the leakage inductances of N_p2 and N_p1 , respectively. i_p1 decreases rapidly, and i_p2 reduces to 0 gradually and then changes direction to increase rapidly in the reference direction. i_DR1 and i_DR2 decrease and increase, respectively. When i_DR1 and i_DR2 reach the same value I_a at t₃ , this mode ends. During this period, the load current decreases gradually from I_Lfmax . 4) Mode [t₃-t₄]: During this period, both S ₁ and S ₂ are off, and the leakage inductance current is freewheeling through U_in(+) → N_p2 → C_b → N_p1 → U_in(-) and forms a circular current in the primary windings. Due to the two primary windings being series-opposing connections, the voltages across them are both 0 and the voltages across the power switches are both U_in . At the same time, magnetizing current also forms a circular current in the secondary side of the high frequency transformer. This mode ends when S ₂ turns on at t₄ . 5) Mode [t₄-t₅][t₅-t₆][t₆-t₇][t₇-t₈]: At t₄ , S ₂ turns on and the circuit goes into the second half of a switch cycle. The modes [ t₄-t₅ ], [ t₅-t₆ ], [ t₆-t₇ ] and [ t₇-t₈ ] in the second half of a switch cycle correspond to [ t₀-t₁ ], [ t₁-t₂ ], [ t₂-t₃ ] and [ t₃-t₄ ], respectively. The operating principle of the second half of a switch cycle works in the same way as the first half, except that the magnetizing currents of the two half cycles are opposite in direction. During the second half of a switch cycle, demagnetization of the high frequency transformer is completed. The key operating waveforms are shown in Fig.4 . Suppose the primary winding turn of the transformer is N_P1 = N_P2 = w₁ and its secondary winding turn is N_s1 = N_s2 = w₂ , then the turns ratio of the secondary to primary is n = w₂ / w₁ . The duty ratio D is defined as 2 T_on / T_s , where T_on is the on duration of each power switch (S ₁ or S ₂ ) and T_s is the switch time period which is equal to the reciprocal of the switching frequency f_s . During steady-state operation, the filtering inductor current is of the triangle waveform, and it varies periodically between I_Lfmin and I_Lmaxf . The inductor current increase Δ I_Lf(+) , during the on duration of S ₁ or S ₂ , is equal to its decrease Δ I_Lf(-) while S ₁ and S ₂ are both off. The relationship between them can be expressed as: Thus: That is: where U_in and U_o are the average input voltage and output voltage, respectively. If the power losses of the circuit are neglected, the average input current can be expressed as : where I_o is the average load current, I_o =( I_Lfmin + I_Lfmax )/2. Fig. 5 shows a control block diagram of the digital PV simulator. There are two control loops in it: the outer-reference loop and the inner-current loop. The former measures the output voltage u_o of the PV simulator and feeds it back to the I-V characteristic mode so as to generate the reference current i_ref ; while the latter controls the filtering inductor current i_Lf . R_L represents the equivalent load resistance of the PV simulator’s load characteristics; G ( s ) is the transfer function of the duty ratio to the filtering inductor current; G_c ( s ) is the transfer function of the PI controller, G_c ( s )= k_p + k_i / s ; G_fi ( s ) is the equivalent transfer function of the current measuring, conditioning and filtering, G_fi ( s )=1/( T_f1s +1); and G_fu ( s ) is the equivalent transfer function of the voltage measuring, conditioning and filtering, G_fu ( s )=1/( T_f2s +1). The I-V characteristics can be described from an engineering analytical model of the PV array. Because the changing rate of the filtering capacitance voltage u_Cf is very small in steady-state operation, the current i_Cf of the filtering capacitor branch is negligibly small and i_Lf is almost equal to i_o . Thus, i_Lf replaces i_o for use as the feed current in the actual control system. The modeling and controller parameter design of the PV simulator are carried out mainly based on the inner-current loop. According to the working principle and the input-output relationship of the PPF circuit, when S ₁ (or S ₂ ) is on, the relationship between the primary and secondary is: where i_in , i_Lf and u_F correspond to the instantaneous value of input current, the filtering inductor current and the pulse voltage rectified by the rectifying diodes, respectively. When S ₁ and S ₂ are both off, the relationship is: By the above relationship and Kirchhoff’s voltage and current laws, considering the effect of the inductance parasitic resistor R_f , the state-variable equations of the PPF circuit with S ₁ (or S ₂ ) being on are: where T_on = t_0-2 or T_on = t_4-6 , t_0-2 is the duration from t₀ to t₂ for each switch cycle, and t_4-6 is the duration from t₄ to t₆ for each switch cycle. The defining methods for the following similar variable are similar to t_0-2 and t_4-6 . Similarly, the state-variable equations of the PPF circuit when S ₁ and S ₂ are both off are: where T_off = t_2-4 or T_off = t_6-8 . Combining the above two switch statuses and according to the working mechanism of the simulator, the state space averaging equations of the PPF circuit in a switch time period are: Due to T_on + T_off = T_s /2, D=2 T_on / T_s , the above equations can be rewritten in state space expression as: Assuming that the initial conditions are zero, the transfer function of the duty ratio to the filtering inductor current by Laplace transforms of equation (10) can be obtained as: The open loop transfer function of the current loop is: There are two parameters ( k_p and k_i ) that need to be determined in equation (12). k_p and k_i are usually designed to match with the transfer function G ( s ). However, G ( s ) varies with R_L , and R_L varies with the operating point and has a wide variation range. As a result, together with the high dimension of G_ok ( s ), it is very difficult to determine the two controller parameters. If the pole of G_fi ( s ) is cancelled by the zero of the PI controller, namely, k_p / k_i = T_f1 , the design difficulty will be reduced to a certain extent. However, k_i remains difficult to determine due to the uncertainty of R_L . Under the condition of k_p / k_i = T_f1 , the performances of the root loci with k_i and R_L as variables are analyzed for designing the controller parameters. On the basis of the analytical method, several sets of PI controller parameters matched with different R_L are designed. In order to match the controller parameters with R_L , a fuzzy PI controller based on the Takagi-Sugeno fuzzy model is applied to self-adaptively regulate the controller parameters so as to satisfy the control demands as the operating point changes. The open loop transfer function of the current loop with k_i as a variable is: The root loci of the current loop with k_i as a variable for different values of R_L are a family of curves. It is found by analyzing the root loci that the root loci show three different types of motion curves with R_L =1.287Ω and R_L =1.403Ω as cut-off points. The typical root loci are shown in Fig. 6 . It can be seen from the figure that the system is always stable no matter how R_L changes. However, the locations of the zeros and poles vary with R_L . For different locations of the zeros and poles, the performances of the current loop show large differences. At a given R_L , different closed-loop poles have different values of k_i and performance indexes, such as the damping ratio ζ and the overshot. It is obvious that the value of k_i determines the location of the closed-loop poles and the performances of the system. According to the characteristic equations of the closed current loop system, its open loop transfer function with R_L as a variable is rewritten as: The root loci of the current loop with R_L as a variable for different values of k_i are also a family of curves. Although the locations of the zeros and poles change with k_i , the form of the root loci is basically the same. Typical root loci are shown in Fig. 7 . It can be seen from the figure that the system is always stable no matter how k_i changes. When k_i kept constant, different values of R_L have different performance indexes. Apparently, R_L is also a key factor to influence the system’s performances. From the analysis presented above, it can be seen that only suitable values of k_i matched with R_L can satisfy the performance requirements of the system because R_L varies with the operating point. For this purpose, by combining the time domain performance indexes of the equivalent 2-order system with the output characteristics of the PV array, typical operating points of the segmented operating ranges are selected to determine the closed-loop dominant poles and the corresponding k_i based on the root loci analysis. The closed-loop dominant poles are selected close to an imaginary axis and the absolute values of the real parts for the other close-loop poles and zeros are three times more than those of the closed-loop dominant poles. Once the closed-loop dominant poles are decided, the corresponding values of k_i can be calculated. For the given T_f1 =0.01s, the value of k_p can be obtained according to the relationship k_p / k_i = T_f1 . The PI controller parameters of the typical operating points in each of the segmented operating ranges are listed in Table I of the Appendix. It can be seen from the above analysis that the controller parameters must be regulated timely and appropriately according to R_L in order to obtain satisfactory dynamic and stationary performances. For these reasons, a fuzzy PI controller based on the Takagi-Sugeno fuzzy model is applied to regulate the controller parameters according to the change of the operating point of the PV simulator. The structure of the fuzzy PI controller is shown in Fig. 8 . In this paper, R_L is selected as the reference factor of the input variable, the input space is divided into four fuzzy subspaces based on the above design results of the controller parameter and every fuzzy subspace corresponds to the linear PI controller designed above. The models of the linear PI controllers are joined together smoothly by membership functions. With overlapping among the other inference rules, the Takagi-Sugeno fuzzy model achieves the nonlinear global mapping and makes the PI controller parameters of each rule have a different weighting factor. Therefore, with the fuzzy inference of the Takagi-Sugeno fuzzy model, the PI controller parameters can self-adaptively regulate according to the change of the operating point and R_L . The value of R ⁱ _c of the Takagi-Sugeno fuzzy model’s fuzzy implication relationship has the following form [20] : where, i is the i -th rule; m is the number of fuzzy subspaces, m =4; Aⁱ is the fuzzy subsets of the i -th rule; and kⁱ_p and kⁱ_i are the outputs of the i -th rule. The total output of the fuzzy implication relation is given by: where, μ_Ai ( R_L ) is the membership function of R_L and the application degree of the i -th rule and is decided by the membership degree for all of the input subsets in the rule. The membership function of R_L is the Gaussian type and it is shown in Fig. 9 . The design parameters of the PV simulator are shown in Table II of the Appendix. According to the above design requirements, the duty ratio of the PPF circuit and the turns ratio of the secondary to the primary are confirmed firstly. Considered the extreme case where the maximum output voltage prescribed by the design indexes can be obtained under the minimum input voltage and the effective duty ratio is increased as much as possible in order to decrease the turns ratio of the secondary to the primary, the turns ratio of the secondary to the primary can be calculated by: where D_max is the maximum duty ratio of the PPF circuit, D_max =0.9. The other parameters of the high-frequency transformer are calculated by using the Area Product method. According to the working mechanism of the PPF circuit and considered a certain safety margin, 1MBH60D-100 IGBT modules and MUR3040PT diodes are selected as the power switches and output rectifying diodes, respectively. According to the working mechanism of the PPF circuit, the averaging voltage of the clamping capacitor is equal to U_in . Due to the finite value of the clamping capacitor, there is a certain pulsating quantity of voltage Δ u_cb , and it can be expressed as: Based on the estimation of the above formula and experimental research, a 10 μF high-frequency non-inductive capacitor is selected as the clamping capacitor. The key parameters of the PPF circuit are listed in Table III of the Appendix. In the PV simulator, a SG3525 is adopted as the pulse width modulation chip to generate two push-pull PWM signals according to the input control voltage. The two PWM signals generated by the SG3525 are amplified by two EXB840 modules to drive the power switches. The output signal of the EXB840’s pin5 is sent to pin10 of the SG3525 by photocoupler so as to shut down the PWM signals for protecting the power switches when an overcurrent occurs. In order to prevent the power switches from turning on simultaneously, the two PWM signals are interlocked in the driving circuit so as to enhance the reliability of the system. A diagram of the PWM signal generation, driving and isolating circuit is shown in Fig.10 . The output voltage and inductor current are measured and converted into 0~2.5V voltage signals by Hall sensors. The signals are converted into 0~10V voltage signals by a signal conditioning circuit and then sent into a data acquisition card for A/D conversion. Taking the voltage measuring as an example, a diagram of the signal measuring and conditioning circuit is shown in Fig. 11 . The control and I/O interface of the PV simulator, which consists of a personal computer and a PCI1710 multifunction data acquisition card, can conveniently achieve the data acquisition and configuration of the control algorithm and interface by the abundant software resources of the computer. The modules of the Real-Time Windows Target are loaded to become a seamless integration of the Simulink environment and the external equipment under the external mode of Simulink, which can make Simulink become a real-time configuration development environment and a graphical control platform. In order to construct and adjust the models and parameters of the PV array, sunshine intensity, PV array’s temperature and controller conveniently and to improve the software development efficiency and reliability, the control software of the PV simulator is designed and developed based on this configuration environment and control platform. A diagram of the real-time configuration and control program for the PV simulator is shown in Fig. 12 . To increase the readability of the program, models of the I-V characteristics and the PI controller are both encapsulated into subsystems. Sampled-data control in equal intervals of time is adopted in the system and its sampling period is 1ms. In order to solve the PV model conveniently, the engineering analytical model of the PV array, which can accurately describe the output characteristics of the PV array with only several electrical parameters under the standard test conditions provided by the manufactures and is convenient for engineering applications, is adopted to build the I-V characteristics subsystem in the MATLAB/Simulink environment. The engineering analytical model of the PV array can be expressed as [21] : where: DT = T - T_ref , V_OC is the open-circuit voltage, I_SC is the short-circuit current, V_m is the optimum operating voltage and I_m is the optimum operating current, which gives the maximum power at that time. R_s is the series internal resistor, Q and Q_ref are actual sunshine intensity and the reference sunshine intensity of 1000 W/m² , respectively. T and T_ref are the actual module temperature and the reference temperature of 25 ℃ , respectively. α and β are the current and voltage temperature coefficients under the reference sunshine intensity, respectively. A diagram of the I-V characteristic subsystem is shown in Fig.13 . It can be seen from the diagram that the models and parameters of the sunshine intensity, the PV array’s temperature, etc. can be built and adjusted conveniently. A diagram of the fuzzy PI controller subsystem is shown in Fig.14 . In order to ensure real time control, the Takagi-Sugeno fuzzy model is realized with the Embedded MATLAB Function block of Simulink in the subsystem. The block can achieve complicated function flexibility with the MATLAB programming language and it can execute simulations and generate code for a Real-time Workshop target. Based on the principle of modular hardware and configurable software, the functional modules of the PV simulator’s hardware which consists of the PPF circuit, the PWM signal generation, the driving and isolating circuit, the measuring and signal conditioning circuit, the control and I/O interface, and so on, are all implemented and tested. After the module level test, all of the functional modules are connected with software to form a complete system and then tested in a whole system. In this paper, a DC programmable electronic load of IT8516C by ITECH is adopted to simulate the load characteristics. The whole system is shown in Fig. 15 , and the parameters of the PV array used to verify the performance of the PV simulator are listed in Table IV of the Appendix. In the experiment, the load mutation test is done to verify the steady-state performances of the different operating points. The typical waveforms of the output voltage and filtering inductor current are shown in Fig. 16 . In the test, R_L is changed suddenly from 20Ω to 5Ω and then changed suddenly from 5Ω to 20Ω after remaining a 5Ω for some time. The experimental waveforms indicate that the ripples of the voltage and current are both very small and that the system can work steadily at the operating point corresponding with R_L . The PV simulator has a fast dynamic response and a high steady-state accuracy. Based on the above results, the static output characteristics of the PV simulator are simulated under 25℃ and different sunshine intensities as well as 1000W/m ² sunshine intensity and different temperatures. Due to limitations on the length of this paper, only the static output characteristics under 25℃ and different sunshine intensities are given. The experimental results are shown in Fig.17 . The steady-state experimental results show that the output voltage and current of the PV simulator have a wide adjusting range, and that its current-voltage characteristics and power-voltage characteristics comply well with the theoretical output characteristics. Due to variations of the operating point of the PV simulator with a load, the test of the power variation of the PV simulator with the sunshine intensity changed under natural conditions is done to verify the dynamic characteristic of the PV simulator. In the test, the sunshine intensity change under natural conditions is simulated by a combination of the positive and negative ramp function, and the other models. A DC programmable electronic load is selected in the constant-voltage mode to maintain a constant output voltage of the PV simulator so as to measure its output power. The curves of the sunshine intensity change and the output power for the PV simulator are shown in Fig. 18 . The test result shows that the power of the PV simulator varies linearly with the sunshine intensity change and it has a good dynamic response. The PV simulator’s efficiency as a function of different output voltages under 1000W/m ² sunshine intensity is measured. The efficiency curve is shown in Fig. 19 . The efficiency curve shows that the PV simulator has a high working efficiency within a wide operating range and that it can satisfy the design requirements. In this paper, the hardware of a digital PV simulator is designed and implemented based on the principle of modular hardware. The control structure of the system is based on a PC and a multifunction data acquisition card. A PPF circuit is adopted as the main circuit of the PV simulator to restrain the magnetic biasing of the core for the DC-DC converter and to reduce the spike of the turn-off voltage across every switch. Based on an analysis of the operating mode and the input-output relationship of the PPF circuit, mathematic models of the PPF circuit and PV simulator are built by the state-space averaging method. Aiming at the problem where the load resistance R_L changing with the operating point of the PV simulator leads to the difficult controller parameter design, the inertial element of the equivalent transfer function of current measuring, conditioning and filtering is cancelled by the pole-zero cancellation technique so as to reduce the design difficulty of the controller parameters. Then the controller parameters are systematically designed based on the performance analysis of the root loci of the closed current loop with k_i and R_L as variables. In order to match the controller parameters with R_L , a fuzzy PI controller based on the Takagi-Sugeno fuzzy model is applied to self-adaptively regulate the controller parameters. The real-time configuration and the control program for the digital PV simulator are developed in the real-time environment of Matlab/Simulink which loads the modules of the Real-Time Windows Target and works under the external mode of Simulink so that the models and parameters of the system can be adjusted conveniently and the system can run in real-time. Lastly, the stationary and dynamic performances of the PV simulator are tested by experiments. The experimental results show that when the controller parameters designed in this paper according to the state-space averaging model are applied to the PV simulator, satisfactory dynamic and stationary performances are obtained. This makes it have the merits of a wide effective working range, a high steady-state accuracy and good dynamic performances. Therefore, it can be seen that the modeling and controller parameter design of the digital PV simulator based on the PPF circuit proposed in this paper is correct and reasonable. It also has a significant reference value for the modeling and controller parameter design of many other types of PV simulators and converters. Jike Zhang was born in Hohhot, China. He received his B.S. and M.S. degrees in Control Science and Engineering from the Inner Mongolia University of Technology, Hohhot, China, in 2000 and 2003, respectively. He is presently working toward his Ph.D. degree at the Inner Mongolia University of Technology. Since 2005, he has been a Lecturer in the Department of Automation, Inner Mongolia University of Technology. His current research interests include power electronics and control in renewable energy. Shengtie Wang was born in Hohhot, China. He received his B.S. and M.S. degrees in Control Science and Engineering from the Chengdu University of Science and Technology, Chengdu, China, in 1985 and 1988, respectively. He received his Ph.D. degree in Electrical Engineering from the Huazhong University of Science and Technology, Wuhan, China, in 1999. Since 2000, he has been a Professor in the Department of Automation, Inner Mongolia University of Technology, Hohhot, China. From September to December of 2003, he was a Researcher at Mie University, Tsu, Japan, where he was engaged in research on the control of small wind turbines. From 2007 to 2008, he worked as a Visiting Scholar at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway, where he was engaged in research on the control of large wind turbines. His current research interests include power electronics and control in renewable energy, intelligent control and applications. Zhihe Wang was born in Baotou, China. He received his B.S. and M.S. degrees in Control Science and Engineering from Chongqing University, Chongqing, China, in 1985 and 1988, respectively. From 1991 to 1996, he worked for the Automation Research Institute, Inner Mongolia University of Technology, Hohhot, China, where he was engaged in research on electric drives, motion control, and power quality control. Since 2003, he has been a Professor in the Department of Automation, Inner Mongolia University of Technology. His current research interests include power electronics and converter control. Lixin Tian was born in Baotou, China. He received his B.S. degree in Electronic Science and Technology from Inner Mongolia University, Hohhot, China, in 1989. From 1992 to 1996, he worked for the Automation Research Institute, Inner Mongolia University of Technology, Hohhot, China, where he was engaged in research on power electronics and power quality control. Since 2001, he has been a Assistant Professor in the Department of Electric Power, Inner Mongolia University of Technology. His current research interests include power electronics and energy conversion.