Abstract
Modern-day world is facing problems such as, electricity generation deficiency, mounting energy demand, GHG (Greenhouse Gas) emissions, reliability and soaring prices. To resolve these issues, sustainable and renewable energy resources like SPV (Solar Photovoltaic) would be quite helpful. In this regard, the extraction of maximum power from SPV array in PSC (Partial Shading Weather Conditions) remains a challenge. Creation of multiple power peaks in the P-V (Power-Voltage) curve of a PV array due to partial shading, makes it difficult to track GMPP (Global Maximum Power Point) out of multiple power peaks known as LMPP (Local Maximum Power Points). Conventional algorithms are not able to perform in any condition other than UWC (Uniform Weather Condition). Nature inspired SC (Soft Computing) algorithms efficiently track the GMPP in PSC. The top performing SC algorithm named, FPA (Flower Pollination Algorithm) presents an efficient solution for GMPP tracking in PSCs. In this paper, the efficiency, accuracy and tracking speed of FPA algorithm is optimized. Comparison of the proposed OFPA (Optimized Flower Pollination Algorithm) and the existing FPAs is performed for zero shading condition, weak PSC, strong PSC, and changing weather conditions. In zero shading conditions, improvement of 0.7% in efficiency and 33% in tracking speed is achieved. In weak shading conditions, improvement of 0.97% in efficiency and 32.2% in tracking speed is achieved. In strong shading conditions, improvement of 0.24% in efficiency and 30.6% in tracking speed is achieved. OFPA is also tested for changing weather conditions (entering from Case-1 to Cae-3) and it retains its outstanding performance in the changing weather conditions. Simulations are performed in MATLAB/Simulink.
Highlights
Modern energy generation technologies created huge problems for the world such as, pollution, respiratory diseases, GHG emissions, depletion of fuel resources etc
Some area of PV array is shadowed due to any reason such as, birds dropping, buildings, trees, dust etc. In this condition the P-V curve of the PV array exhibits multiple power peaks known as LMPP (Local Maximum Power Points)
In the proposed OFPA algorithm, the role of Global Pollination is efficiently utilized by introducing the concept of LCP (Local Cross Pollination)
Summary
Modern energy generation technologies created huge problems for the world such as, pollution, respiratory diseases, GHG emissions, depletion of fuel resources etc. Conventional MPPT algorithms include: P&O (Perturb and Observe) [5], InC (Incremental Conductance) [6], FSC (Fractional Short Circuit) [7] and FOC (Fractional Open Circuit) [8] Switching probability has been fixed at 0.8, it does not ensure the fine balance between two types of searches Such a drawback presents difficulty in GMPP tracking [22]. Some area of PV array is shadowed due to any reason such as, birds dropping, buildings, trees, dust etc In this condition the P-V curve of the PV array exhibits multiple power peaks known as LMPP (Local Maximum Power Points). GMPP mode is activated and afterwards, switches to the other mode of conventional P&O method Both improved methods are ineffective in PSC. Section-2 describes the modeling of SPV cell, section-3 explains the partial shading effects and FPA technique, section presents the problem formulation, section-5 describes the proposed optimized FPA algorithm”, section-6 presents the simulation and results, section presents the conceptual comparison, section-8 performs the comparison, section-9 conclusion
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