Power optimization for photovoltaic micro-converters using multivariable gradient-based extremum-seeking

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Abstract
It is well-known that distributed architectures such as micro-converters and micro-inverters for photovoltaic (PV) systems can recover between 10%-30% of annual performance loss or more that is caused by partial shading and/or module mismatch. In this work, we present a novel multivariable gradient-based extremum-seeking (ES) design to extract maximum power from an arbitrary micro-converter configuration of PV modules, that includes cascade and parallel connections. Conventional maximum power point tracking (MPPT) schemes for micro-converters (where each PV module is coupled to its own DC-DC converter) employ a decentralized control, with one peak seeking scheme per each PV module, thereby requiring one control loop and two sensors per module (one each for current and voltage). By contrast, the scheme that we present employs a single control loop with just two sensors, one for the overall array output current and the other one for the DC bus voltage. This centralized design provides more flexibility in tuning the parameters of the controller, and also takes into account interactions between PV modules. The computational effort of our design is not higher than that of the conventional scheme, and simulation results using Simulink's SimPowerSystems toolbox show that our proposed design outperforms the conventional one. Thus, our proposed design offers two benefits: (i) the balance-of-system (BOS) cost reduction as a result of the significantly lower number of sensors, and (ii) improved performance, both contributing towards reduced average cost/watt, and enhancing the economic viability of solar.

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