Photovoltaic (PV) modules subjected to partial shading conditions (PSC) can drastically decrease their power output. Hence, there have been various maximum power point tracking (MPPT) control algorithms developed to reduce or counteract the shading effects. Recently, a new meta-heuristic algorithm known as firefly algorithm (FA) was developed, which, under PSC, has been shown to successfully track the GMP. Nevertheless, the FA still has some inherent problems, which may hinder the performance of the MPPT. This paper modifies the existing FA to counteract these problems. As will be demonstrated in the paper, the proposed modified FA (MFA) method can reduce the number of computation operations and the time for converging to the GMP that the existing FA requires.Experimental results show that the proposed method can track the global point under various PSC, has a faster convergence time, compared to the FA, and can effectively suppress the power and voltage fluctuations. Index TermsMaximum power point tracking (MPPT), photovoltaic (PV) array, partial shading, global optimization, firefly algorithm

The performance of a non-intrusive load monitoring (NILM) system heavily depends on the uniqueness of the preferred load signature (LS) extracted from each appliance. Some electrical characteristics such as instantaneous current waveform (CW), instantaneous power waveform (IPW), current harmonic (CH) and voltage-current (V-I) trajectory have been proposed as appliance features in the literature. However, in some situations, these LSs cannot effectively distinguish different loads apart. In this paper, a time-domain based advanced power theory is used to decompose the load current into active and non-active orthogonal components. Then, two new LSs have been established based on the non-active component of the load current, namely the non-active current waveform (i f ) and the voltage-non-active current (V-I f ) characteristics curve. Simulation and experimental tests show that both of these features can distinguish different appliances. Hence, the proposed LSs can significantly enhance the existing NILM systems.

A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a proposed modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

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