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Soft computing techniques for maximum power point tracking (MPPT) of photovoltaic (PV) systems have been already used to achieve better performance of the systems (Amit et al., 2019; Hanane & Elhassan, 2018). These techniques include fuzzy logic control, genetic algorithms, probabilistic computing, etc (Amit et al., 2019; Mohmed et al., 2020). These techniques have also been used in conjunction with another effective tool for implementing real time control and modeling, which is the Finite State Machine (FSM). FSMs have been used as Fuzzy-FSMs(Hamzaoui et al., 2020a; Hamzaoui et al., 2020b; Mohmed et al., 2020; Reyneri, 1997; Speranskii, 2015), Genetic-FSM synthesis(Ali et al., 2004; Bereza et al., 2013), probabilistic-FSM(Li & Tan, 2019), etc. Hence, FSMs have hence established a significant hand-shake with soft computing techniques in various applications.
However, FSMs have certain limitations due to lack of abstraction in representing a complex system with many states which results in explosion of states(Harel, 1987; Lahari et al., 2019; Mierlo & Vangheluwe, 2019). This could pose a problem in efficiently integrating the FSM with a soft-computing technique. To overcome this limitation of FSM, in this work, an extension of FSM, ‘statechart’ is presented for MPPT control of a PV system. The statechart is developed for an FPGA target and is modeled with abstraction in this work.
Statecharts, that can represent, model and also implement control of complex systems using states, transitions and actions codes (Harel, 1987; Mierlo & Vangheluwe, 2019), have unique features as explained further. Statecharts allow for ‘abstraction’ in their models, which is to have number of layers of states instead of a flat set of states as in FSMs. This abstraction or layering of states provides compact, simpler and flexible modeling possible. Statecharts also have feature of history, which is, their abstract or layered states can be modeled to have ‘memory’ of the last inner state that it was active in, before the higher-layered state is left. Also, statecharts method of communication is ‘broadcast-communication’ through which every state, layered or non-layered, can be communicated of an event occurring at any other state of the system at the same time as the event occurred and an apt control action can thus be achieved. These traits of abstraction, history, ‘sensing’ of an event by all states at same time (broadcast-communication)(Harel, 1987) make statecharts highly flexible, reliable and apt for systems that require ‘softness’ instead of ‘crisp’ methodology in their implementation and modeling. Hence, statecharts can be integrated seamlessly with other soft-computing techniques, as an alternative to FSMs being integrated in fuzzy-FSMs, genetic-FSM synthesis, probabilistic-FSM, etc.
For this reason, abstract-statecharts are proposed in this work, developed for achieving MPPT control of a photovoltaic system, with fast tracking speeds. The statechart is developed for an FPGA as a target and performance of the PV system is verified with the abstract-statechart MPPT(ASM) controller so-developed.