International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol.2, Issue.6, Nov-Dec. 2012 pp-4491-4496 ISSN: 2249-6645 www.ijmer.com 4491 | Page Divya Teja Reddy Challa 1 , I. Raghavendar 2 1 (PG student Department of EEE, Teegala Krishna Reddy Engineering college, JNTU- Hyd, AP, INDIA) 2 (Associate Professor Head of the Department of EEE, Tee gala Krishna Reddy engineering college, JNTU-Hyd, AP, INDIA) ABSTRACT: This paper presents simulation of incremental conductance (IncCond) maximum power point tracking (MPPT) used in solar array power systems with direct control method. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional–integral control loop and investigation of the effect of simplifying the control circuit. The resultant system is capable of tracking MPPs accurately and rapidly without steady-state oscillation, and also, its dynamic performance is satisfactory. The IncCond algorithm is used to track MPPs because it performs precise control under rapidly changing atmospheric conditions. MATLAB and SIMULINK were employed for simulation studies. Simulation results indicate the feasibility and improved functionality of the system. IndexTerms: incremental conductance (IncCond), maximum power point tracking (MPPT), photovoltaic (PV) system. I. INTRODUCTION RECENTLY, energy generated from clean, efficient, and environmentally friendly sources has become one of the major challenges for engineers and scientists [1]. Among all renewable energy sources, solar power systems attract more attention because they provide excellent opportunity to generate electricity while greenhouse emissions are reduced [1]–[3]. It is also gratifying to lose reliance on conventional electricity generated by burning coal and natural gas. Regarding the endless aspect of solar energy, it is worth saying that solar energy is a unique prospective solution for energy crisis. However, despite all the aforementioned advantages of solar power systems, they do not present desirable efficiency [4], [5]. The efficiency of solar cells depends on many factors such as temperature, insolation, spectral characteristics of sunlight, dirt, shadow, and so on. Changes in insolation on panels due to fast climatic changes such as cloudy weather and increase in ambient temperature can reduce the photovoltaic (PV) array output power. In other words, each PV cell produces energy pertaining to its operational and environmental conditions [6], [7]. In addressing the poor efficiency of PV systems, some methods are proposed, among which is a new concept called ―maximum power point tracking‖ (MPPT).All MPPT methods follow the same Goal which is maximizing the PV array output power by tracking the maximum power on every operating condition. A. MPPT Methods There is a large number of algorithms that are able to track MPPs. Some of them are simple, such as those based on voltage and current feedback, and some are more complicated, such as perturbation and observation (P&O) or the incremental conductance (IncCond) method. They also vary in complexity, sensor requirement, speed of convergence, cost, range of operation, popularity, ability to detect multiple local maxima, and their applications [8] – [10]. Having a curious look at the recommended methods, hill climbing and P&O [11]–[16] are the algorithms that were in the center of consideration because of their simplicity and ease of implementation. Hill climbing [14], [17] is perturbation in the duty ratio of the power converter, and the P&O method [15], [18] is perturbation in the operating voltage of the PV array. However, the P&O algorithm cannot compare the array terminal voltage with the actual MPP voltage, since the change in power is only considered to be a result of the array terminal voltage perturbation. As a result, they are not accurate enough because they perform steady-state oscillations, which consequently waste the energy [8]. By minimizing the perturbation step size, oscillation can be reduced, but a smaller perturbation size slows down the speed of tracking MPPs. Thus, there are some disadvantages with these methods, where they fail under rapidly changing atmospheric conditions [19]. On the other hand, some MPPTs are more rapid and accurate and, thus, more impressive, which need special design and familiarity with specific subjects such as fuzzy logic [20] or neural network [21] methods. MPPT fuzzy logic controllers have good performance under varying atmospheric conditions and exhibit better performance than the P&O control method [8]; however, the main disadvantage of this method is that its effectiveness is highly dependent on the technical knowledge of the engineer in computing the error and coming up with the rule-based table. It is greatly dependent on how a designer arranges the system that requires skill and experience. A similar disadvantage of the neural network method comes with its reliance on the characteristics of the PV array that change with time, implying that the neural network has to be periodically trained to guarantee accurate MPPs. Fig. 1 Basic idea of the IncCond method on a P–V curve of a solar module Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter