Sampling Time Modulation of a Photovoltaic Power Tracking Controller Based Upon Real-Time Monitoring of Converter Dynamics

The maximum power point tracking (MPPT) or flexible power point tracking (FPPT) of a photovoltaic (PV) array is typically performed by using a discrete-time control system. The sampling time of an MPPT/FPPT controller plays a crucial role in determining its transient performance. Performing the MPPT or FPPT control at a fixed sampling rate makes it necessary to set the respective sampling time to a large value so as to effectively deal with the worst case of converter transient. This, in turn, makes the power tracking continue for an unnecessarily longer time duration. The main objective of this article is to improve the speed of power tracking through a dynamic adjustment of the power tracker's sampling time. Although the same idea was already deployed in the literature, existing methodologies are essentially based upon tedious offline assessments of converter dynamics using the PV module information. In contrast, the proposed dynamic moment-based adaptive rate sampling (D-MARS) scheme modulates the sampling time of the power tracking controller by monitoring the actual state of converter dynamics through a moving variance calculation in real time. Detailed experimental studies are performed to verify the benefit of the proposed D-MARS scheme over the other sampling time modulation (STM) techniques.

I. Introduction

Development of efficient mechanisms for the power tracking control of a PV array has a great significance to expand the scope of deployment of PV systems for serving consumer loads at different scales and in different applications. Two different types of power tracking, namely MPPT [1], [2], [3], [4], [5], [6] and FPPT [7], [8], [9], [10], [11], [12], [13], can be performed for a PV system. The objective of the MPPT control is to operate the PV array only at the MPP of its power–voltage () curve, whereas, the FPPT control provides users with the flexibility to increase or decrease the PV power output as per the exact requirement of system. Because of the nonmonotonic nature of the curve, it is usually difficult to perform any kind of power tracking with the help of a continuous-time controller. Therefore, the practical PV power tracking is usually carried out by means of a system-interactive optimization via a discrete-time control mechanism. In the case of a two-stage PV system, the duty ratio command to the dc–dc converter is to be optimized [14], [15], whereas, optimization of a voltage reference command is required for a single-stage PV system [16], [17]. Out of several performance measures, the swiftness of power tracking is very important to reduce the loss of solar energy as well as to quickly achieve the generation-load balance for maintaining the system stability. The speed of power tracking becomes a more critical concern when the power tracking is to be performed under the PSC. This is because the power tracking under PSC should either involve an additional task of finding the GMPP zone [18], [19], [20], [21], [22], [23], [24], [25] or take the resort of a metaheuristic optimization algorithm [26], [27], [28], [29], [30], [31], [32]. This may significantly lengthen the power tracking duration. One way to improve the speed of power tracking without hampering its steady-state performance is to dynamically adjust the size of duty ratio or voltage reference perturbation from one iteration to another iteration of the concerned system-interactive optimization. A large volume of work is available in the literature in this line [2], [3], [4], [5]. It may also be possible to improve the speed of power tracking by dynamically adjusting the PTC sampling time as the power tracking iterations go along.