CHAPTER 2 LITERATURE SURVEY

Transcription

1 13 CHAPTER 2 LITERATURE SURVEY 2.1 INTRODUCTION Investment in solar photovoltaic (PV) energy is rapidly increasing worldwide due to its long term economic prospects and more crucially, concerns over the environment. The solar PV system not only consists of PV panels, but also has a few power electronic converters for connecting its output to the grid. The power electronics converters normally used are, the DC-DC converter to boost the PV output DC, and the DC-AC inverter for AC conversion. Generally, the Maximum Power Point Tracking (MPPT) algorithm is incorporated with the DC-DC converter to boost the level of the solar PV array output voltage, and to attain the maximum energy extraction. The use of power electronic converters introduces new problems like power loss, the electromagnetic interference (EMI), thus reducing efficiency, and power quality. In view of the above, it is essential to develop effective algorithms for the MPPT, and for the control of DC-DC as well as DC-AC converters. This chapter reviews various MPPT techniques and control algorithms proposed in the literature for the DC-DC and DC-AC converters used in the PV system. 2.2 MPPT TECHNIQUES The photovoltaic (PV) cell directly converts solar energy into electricity. At a unique point on the I-V or P-V curve of a PV cell, called the Maximum Power Point (MPP), the PV system operates with the maximum efficiency and produces the maximum output power. Hence, it is essential to

2 14 include a MPPT module in the PV system so that the PV arrays are able to deliver the maximum available power. This section reviews various MPPT techniques proposed in the literature Conventional Techniques One of the most successful and simplest methods for MPPT is the Perturb and Observe (P&O) method (Femia et al., 2004). In this approach, the controller works by perturbing the PV array output voltage and observing the effect on the output PV power. There are a number of variants of P&O algorithm which are reported in the literature. In Ying-Tung & China-Hong, (2002) the authors propose a three-point weight comparison P&O method. Here, the slope of the perturbation is decided, based on the comparison of the actual operating power with the two preceding ones. The work in Al-Amoudi & Zhang, (1998) suggests an adaptive P&O scheme, for a grid connected three-phase inverter. Initially, the perturbation is set at 10% of the open circuit voltage (V OC ). Each successive perturbation is set to 50% of the preceding one, until the value of perturbation is 0.5% of the V OC. Although the method exhibits better performance, it is still not fully adaptive due to the predetermined perturbation steps. The authors in Fortunato et al, (2008) exploit the capability of the multi objective optimization technique to design the P&O based method for a single-stage inverter. Similar to the P&O method, Hill climbing (HC) (Weidong & Dunford, 2004) works by perturbing the PV power converter s duty cycle, and observing its impact on the PV array output power, and then deciding the new direction of the duty cycle to extract the maximum power. Even though both techniques use the same concept for optimum operating point searching, they use different ways to achieve the same method; the former uses the PV array output voltage and the latter uses the power converter duty cycle, to

3 15 extract the maximum power. In the HC MPPT method, the duty cycle is changed directly without a PI controller; hence, this method is known as the direct duty cycle technique. This scheme offers a number of advantages: (1) it simplifies the tracking structure, (2) it reduces the computation time, and (3) no tuning effort is needed for the PI gains. In short, it replaces the sophisticated MPPT control with a more simplified structure while maintaining similar optimal results. The major drawbacks of the P&O/HC MPPT method are, oscillations in the vicinity of the MPP, power loss, and degraded solar energy conversion efficiency. Also, the P&O approach tracks in the wrong direction under rapidly varying irradiance (Femia et al., 2004). In (Salas V et al 2005), the P&O method has been improved by using the PV panel current (I PV ) as the variable for the calculation of the duty cycle (D). To overcome the disadvantages of slow convergence and oscillation around the MPP, the use of a variable perturbation size approach was proposed in (Liu and Lopes, 2004). In this approach large perturbations are applied, when the output power is far from the MPP, whereas smaller steps are adopted as the output power oscillates around the MPP. The magnitude of the variable perturbation is determined, based on the slope of the power current curve. The determination of this slope, however, increases the complexity and cost associated with this approach. The method which makes use of the ripple in the power, to perform the tracking of the MPP is known as the ripple correction control (RCC) (Calais Martina et al., 1998). This algorithm is based on the fact that in singlephase systems the instantaneous power oscillates at twice the line frequency. The oscillation of the AC power also causes a ripple of twice the line frequency on the DC voltage and DC power of the photovoltaic array (Kimball, Krein 2008). The described algorithm requires knowledge of the

4 16 phase relationship between the DC voltage and DC power ripple, to determine the maximum power point. The method of extremum seeking (ES) control is closely related to the ripple correlation control (RCC). Leyva et al. (2011) and Bratcu et al. (2008) implemented ES control by injecting an external perturbation signal. In both the ESC and RCC methods, the attainment of the MPP is guaranteed but they have difficulties in hardware implementation. The idea behind the Incremental conductance method (Hohm and Ropp 2000) is to increase or decrease the reference voltage (V ref ) value (which is in the vicinity of the MPP voltage), based on the comparison of the instantaneous conductance with the incremental conductance. The increment size determines how fast the MPP is tracked. Fast tracking can be achieved with bigger increments, but the system might not operate exactly at the MPP and oscillate about it instead; so there is a tradeoff. In (Hussein et al., 1995 & Bruendlinger et al 2006), a method is proposed that brings the operating point of the PV array close to the MPP in the first stage, and then uses the IC to exactly track the MPP in the second stage. In (Koizumi et al 2005), a linear function is used to divide the I V plane into two areas, one containing all the possible MPPs under changing atmospheric conditions. The operating point is brought into this area, and then IC is used to reach the MPP. The main drawback associated with the IC method, is that it requires complex control circuitry Artificial Intelligent Techniques Artificial intelligence (AI) techniques, like artificial neural networks (ANN) (Mellita et al 2007 and Veerachary et al 2003)and fuzzy logic (FL) (Kottas et al., 2006 and Esram et al. 2007), have been employed as

5 17 alternative approaches to the conventional MPPT techniques. Gounden et al. (2009) have presented a Fuzzy logic based MPPT controller for a gridconnected PV generation system, and proved that the fuzzy logic control is an effective tool to extract the maximum power from the PV system. Fuzzy logic based system has the advantages of utilizing the human expert knowledge instead of mathematical models to address the real world problems. But the main problem with the fuzzy logic based approach is that as the number of variables increases, the number of membership function and the fuzzy if-then rules also increases exponentially. The artificial neural network (ANN) based methods can track the MPPs quickly and accurately, in response to quick changes in the environmental conditions (Ahmet Afsin Kulaksız, 2012). However, the disadvantage of this method is that, the ANN model should be trained periodically in order to ensure convergence to the accurate MPP. In general, artificial intelligence based MPPT techniques have good performance under various operating conditions; but, these algorithms are complex to apply on the commercial PV system, because many variables which determine the performance of the algorithm, need to be set by the engineer MPPT Algorithms for Partial Shading Conditions Partial Shading of the PV array may occur due to clouds, trees, buildings etc. Because of the partial shading effect in the PV array, multiple peaks will be exhibited in the PV characteristic curve. The presence of multiple peaks reduces the effectiveness of the above mentioned MPP tracking algorithms. Some researchers have worked on global MPP tracking under partial shading condition. In (Kobayashi et al 2003) a two-stage MPPT combined with the instant online measurement of V oc and I sc was proposed. This MPPT technique is relatively simple to apply. However, an additional circuit for the instant online measurement of V oc and I sc is required.

6 18 Evolutionary algorithms, like the Genetic Algorithm (Ahmet Afsin Kulaksiz, 2012), Partial Swarm Optimization (Miyatake et al., 2007) and Differential Evolution (Taheri et al., 2010), have been applied to obtain the maximum point in the P-V characteristics. But, these algorithms take more time for convergence which prevents them from being applied to on-line applications. In (Patel et al., 2008), the Global Peak (GP) MPPT, based on the P&O MPPT is proposed. Here an additional subroutine named the GP track subroutine, was used. Kobayashi et al., (2003) implemented a two-stage IC method. In the first stage, the PV array is forced to operate in the neighborhood of the GP using the values of the maximum voltage and current. Subsequently, in the second stage, an adjustment is made to move the operating point towards the GP, based on the IC method MPPT Algorithms for Rapidly Varying Atmospheric Conditions If the dynamics of the developed MPPT algorithm is slower than the speed of irradiation changes due to changing atmospheric conditions such as clouds, the overall maximum power point (MPP) tracking efficiency will become lower, since the MPP cannot be tracked accurately at every instant. Hence, a fast and accurate MPPT method is required. Many MPPT techniques suitable for rapidly changing environments have been proposed. In Jain and Agarwal (2007), a new method is proposed based on the analysis and derivation of the I V characteristics of a PV module, by defining a natural logarithmic index. This method can provide faster tracking speed than the conventional hill-climbing method, but the utilized index is too complicated for real-time applications. The Extreme seeking (ES) controller is proposed in Brunton et al. (2010), which offers fast convergence and good steady-state performance, with guaranteed stability for a range of parameters. This method

7 19 requires voltage and current ripple information, which needs a high resolution analog to digital converter (ADC) that increases the cost of the system. Fast tracking of the MPP without oscillations can be achieved with the prior knowledge of V ref value under partial shading conditions. Generally, the value of V ref is obtained from a linear equation (Ji et al., 2011) which related to the physical values of the PV panels, such as an open circuit voltage or short circuit current or both. In this thesis, a modified form of incremental conductance MPPT method is proposed, to track MPP under partial shading condition, as well as rapidly varying atmospheric conditions. 2.3 DC-DC CONVERTER CONTROL The general technique to control the DC-DC converter is Pulsewidth Modulation (PWM) (Raviraj and Sen, 1997). The conventional PWM controlled power electronics circuits are modeled, based on the averaging technique, and the system being controlled operates optimally only for a specific condition (Forsyth and Mollow, 1998). Linear controllers like P, PI, and PID, do not offer a good result under the transient state (Seo and Choi, 2012). Therefore, research is being conducted for investigating non-linear controllers. Recently, several researchers have developed efficient control strategies to improve the dynamic behavior of DC DC converters by using the fuzzy logic controller (FLC). The FLC could be useful in situations where there is no acceptable mathematical model for the plant and there are experienced human operators who can satisfactorily control the plant. The design of fuzzy logic controllers poses some difficulties in the selection of optimized membership functions and fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. In order to solve this problem,

8 20 adaptive fuzzy logic controllers have been introduced. In (Wong, Leung & Tam, 1997), an adaptive fuzzy logic controller is used to control the DC DC converters and the performance of the controller is verified by the simulation results. The application of the Adaptive Fuzzy Controller (Huh & Park, 1999) for the DC-DC Converter results in faster operation with less oscillation under varying operating conditions. In this thesis, an adaptive fuzzy controller is developed to control the DC-DC converter in the PV system. The hardware realization of the proposed algorithms is carried out through the FPGA. The hardware realization of the developed algorithms using FPGA, results in a reduction of the development time and cost. 2.4 INVERTER CONTROL The DC-AC inverter is used to convert the generated DC in the Solar PV module into AC power, at the desired voltage and frequency. In Grandpicrre et al. 1996, proportional (P) control was attempted for such inverters, but such a control has an inherent steady-state error. This error was eliminated by adding an integral component to the transfer function (Hajizadeh Amin, et al, 2007). The average value of the current error can be reduced to zero at a specified rate with such an integral component. Nevertheless, the transient response of such a proportional integral (PI) controller is limited by the proportional gain. The gain must be set at a value such that the slope of the error is less than the slope of the carrier saw tooth waveform, required for generating the firing pulses of the inverter. This results in an error under transient conditions. An average current mode control (ACMC) was found to be better than the PI control technique because of an additional derivative component,

9 21 which improves the gain of the regulator at the switching frequency (Dixon et al, 1990). The ACMC has a relatively fast transient response, with a relatively fast removal of the steady-state error. But this method has the problem of high frequency sub-harmonic oscillations with the current mode control (Tangn et al, 1993). Also, instability is reported under certain conditions (Pressman AI, 1998). Alongside the development of the PID controller, the hysteresis current controllers were also developed. In hysteresis control, the actual value of the output current is controlled, in order to remain in a defined area (Brod, Novotny, 1985). Such hysteresis controllers were generally used for their robustness and simplicity, and for their very good transient response. In addition, they do not need a triangular carrier signal for the generation of the triggering pulses. Moreover, these controllers ensure current control without the knowledge of the load model and its parameters (Anushuman Shukla et al, 2007). However, their disadvantage is that, the converter s switching frequency varies with the error amplitude, and thus, the current waveform contains numerous harmonics (Eun-Chul Shin et al, 2004). Hysteresis bands are generally set in analog circuitry, which can cause steady-state fidelity errors (McMurray, 1985). A solution proposed to this problem, is to employ variable limits for the hysteresis controller (Bose, 1990). This solution needs to know the system parameters, and hence, is difficult to implement. In order to solve the variable switching frequency problem without affecting the good dynamic performance of the hysteresis current control, an adaptive hysteresis band can be used to maintain a constant switching frequency. Rathika et al. (2010) has proposed an adaptive fuzzy hysteresis current controller for filter applications. Xunjiang Dai et al. (2008) proposed an adaptive hysteresis controller for the grid connected PV inverter under normal operating condition which was verified by simulation.

10 22 The amount of harmonics and the switching frequency are the two major issues to be addressed while developing the control strategy for DC-AC converter used in solar PV system. In this thesis, a modified form of incremental conductance MPPT method is proposed to track MPP under rapidly varying atmospheric as well as partial shading conditions. Also, an adaptive hysteresis-band current control technique is proposed to control the DC-AC inverter in the PV system. The hysteresis band of the proposed current controller is to achieve constant switching frequency under any operating conditions of the PV system. The hardware realization of the proposed algorithms is carried out through FPGA. Hardware realization of the developed algorithms using FPGA results in reduction in development time and cost. 2.5 CONCLUSION This chapter has reviewed various algorithms for tracking the maximum power point (MPP) in solar PV systems. The limitations of the conventional MPPT methods and its variations and adaptive forms have been discussed. In addition, the more recent MPPT approaches, using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms are also discussed. Also, control techniques to improve the dynamic behavior of DC-DC converters, are also highlighted. In addition various control techniques, such as current control methods used to control the DC-AC inverter, are also reviewed in this chapter. Finally, the technical issues in the efficient operation of solar PV system are also outlined.