## 1 Introduction  
Photovoltaic (PV) system consists of an array, a power conditioner(s), and a transmission and distribution system. With changing irradiation and the cell temperature, the power produced by PV module varies nonlinearly. To achieve maximum power at all times, the Maximum Power Point Tracking Technique (MPPT) is developed. Till now, the MPPT algorithms comprise the constant voltage tracking (CVT), voltage feedback, Perturb and Observe (P&O), power feedback, incremental conductance (INC) method, Hill Climbing, Artificial Neural Network (ANN), Fuzzy Logic, and so on [1; 2; 3; 4; 5; 6]. The simplest approach is the voltage feedback, where the output voltage at the maximum power point is essentially recognized in advance of the tracking operation. To locate the MPP, the P-V characteristic curve must be updated often, but due to the PV module declines, MPP drifts. Most of the conventional methods based on the perturbation of voltage, current or both. The popular technique is P&O method. This method compares present and past power, and then check whether the power is improved or not. If the power is improved the perturbation direction is correct otherwise the direction is reversed. This method produces more oscillation and tracking speed is less [7; 8; 9; 10; 11; 12; 13; 14; 15].  
Over recent years, a large number of studies have been published on smart MPP trackers to accurately locate the MPP and enhance the performance of tracking, during static and dynamic conditions. In general, practically partially shaded panels have multiple peaks. But, these tracking approaches fail to track the global MPP due to the multiple peak problems [16; 17; 18; 19]. In order to overcome this problem, PSO algorithm is used to follow the global MPP instead of tracking the local MPP on a multifaceted nonlinear output curve. PSO algorithm based on maximum power point tracking techniques have been proposed recently for a photovoltaic module, to increase the dynamic response of tracking [20; 21; 22; 23; 24; 25; 26; 27; 28].  
Various literature shows that the PSO based MPPT incorporates for a boost converter in PV applications [20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. The significant contribution of this work is that the PSO based MPPT is incorporated in Z-Source inverter. Also, the implementation of MPPT and PWM generation is performed in the dspace 1104 controller. This paper mentions an enhanced MPPT method based on a PSO algorithm using a Z-Source inverter. This MPPT has a comparable structure to the conventional P&O method; therefore, a direct duty cycle control can be utilized. The main advantages of the proposed method are the reduction of steady-state oscillation and faster tracking of MPP even during the extreme environmental condition, e.g., partial shading condition and large fluctuations of irradiance. By comparing it to conventional MPPT techniques, it has been found that the PSO has a faster tracking speed. Moreover, due to the simplicity of the algorithm, it can be computed very quickly; so, it is also possible to implement this algorithm using a simple low-priced controller. The tracking performance of this method is established by experimental results. In [30], the algorithm computes the value of initial particles' dMPP (duty cycle at MPP) based on the voltage at maximum power. Therefore, the algorithm can start the optimization process with an initial value that is already close to the MPP.  
This paper is prepared as Section 1 presents introduction and necessity of MPPT, and Section 2 gives the modeling of 60 W PV panel using mathematical equations [31; 32; 33]. Sections 3 and 4 discuss conventional perturb and observe method and particle swarm optimization method, respectively. To integrate the output of PV panel to load through Z-Source inverter is discussed in Section 5 [34; 35; 36; 37; 38]. Finally, the results are conferred in Section 6.