Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions  
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1 Department of Electrical Engineering, Indian Institute of Technology, Jodhpur 342 037, India; [EMAIL_ADDRESS]  
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2 Power System Planning Division, Rajasthan Rajya Vidhyut Prasaran Nigam Ltd., Jaipur 302005, India; [EMAIL_ADDRESS]  
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3 Department of Computer Science and Engineering, Oakland University, Rochester, MI 48084, USA; [EMAIL_ADDRESS]  
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However, the performance of these converters depends on the implemented control algorithms [5]. These algorithms are helpful for reactive power management, smooth grid synchronization [6] and power quality mitigation at the point of common coupling (PCC) [7]. Among all these RE resources, Solar Photo-Voltaic (SPV) has been found most prominent RE source less clean power generation with high efficiency and low maintenance [8]. It has been recognized that by the end of 2050, the SPV would fulfil 25% of total electrical power needs worldwide, and it will be the second-largest renewable power generation source behind wind energy. This will lead the way for the transformation of the world renewable power generation zones [9]. Also, the conversion efficiency of Solar PV cells is limited, and the output power entirely depends on the irradiation and temperature of nonuniform operating conditions (NUOCs) [10]. Therefore, accurate tracking of global power peaks to achieve maximum power point tracking is a significant challenge for efficient utilization of the SPV array under grid-connected mode or isolated mode of operation [11]. Hence, it is of foremost importance that, SPV array and hybrid wind-PV sources should have maximum efficiency to generate maximum power [12]. An accurate tracking is essential for maximum power point tracking (MPPT), specially in variable atmosphere conditions [13]. In literature, various MPPT algorithms are proposed, such as perturbation and observation (P&O) algorithm is utilized because of its attracting features like simplicity and easy implementation [14]. However, in this algorithm, large fluctuations are generated during the process of tracking maximum power (MPP) tracking under NUOCs. An incremental conductance (I&C) algorithm has been proposed to overcome these problems [15]. This method uses constant measurement steps, which make it possible to track the MPP by measuring the ratio between instantaneous conductance and INC values of the SPV output power. Also, to increase the tracking accuracy of the MPP, a modified version of this algorithm or hybrid method based on gravitational search algorithm-PSO is proposed in [16]. A modified version of I&C algorithm using the initial value incremental conductance strategy for global MPPT is presented in [17]. Adaptive neuro-fuzzy inference system-PSO based hybrid algorithm for accurate and fast-tracking of MPP with zero oscillation tracking [18], Marginal power-based MPP [19], capacitor selection method [20], combined PSO and perturb and observe method [21], modified genetic algorithm [22] for tracking the global MPPT in Solar-PV system are also reported in literature. It has been perceived that under nonuniform operating conditions of solar-PV conventional PSO may take high time to reach the steady-state conditions due to arising of large step size, selection of parameters and process involved in the algorithm. Voltage variations also persist during the process of MPP tracking. The computed value of velocity can be responsible for significant overshoot due to multiple global peaks in the Solar-PV power and voltage characteristics, leading to uncertain MPPT [23]. New overall distribution (OD) based MPPT algorithm for tracking of maximum global power has been presented in [11]. However, PSO is integrated into the OD based MPPT algorithm to enhance the accuracy of MPPT. Accuracy is not only criteria for designing of hybrid MPPT algorithm.