Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman Online dynamic conductance estimation based maximum power point tracking of photovoltaic generators Moshe Sitbon a,d , Simon Lineykin b , Shmuel Schacham d , Teuvo Suntio a , Alon Kuperman c,d, ⁎ a Dept. of Electrical Engineering, Tampere University of Technology, Tampere FI-33101, Finland b Dept. of Mechanical Engineering and Mechatronics, Ariel University, Ariel 40700, Israel c Dept. of Electrical and Computer Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel d Dept. of Electrical Engineering and Electronics, Ariel University, Ariel 40700, Israel ARTICLE INFO Keywords: Renewable energy generators Maximum power point tracking Dynamic conductance ABSTRACT In this paper, a novel method of maximum power point tracking of renewable energy generators is proposed, utilizing the sum of dynamic and static conductance as maximum power point tracking loop variable. This allows to formulate the maximum power point tracking problem as a typical closed-loop stabilization task of non-linear static plant with zero reference. Consequently, a simple integrative controller is shown to be sufficient to ensure zero steady-state maximum power point tracking error with easily determinable nominal dynamics. A recently revealed method of online photovoltaic generator dynamic conductance estimation allowing robust terminal voltage control is utilized. Moreover, it is revealed that the resulting maximum power point tracking loop plant is piecewise linear around the maximum power point, i.e. for given environmental conditions two different con- vergence rates are expected, depending on the relative value of operating voltage to maximum power point voltage. Presented analytical outcomes are verified by application of the proposed maximum power point tracking structure to a grid-connected photovoltaic generator system under robust voltage control. 1. Introduction Over the last decade, a notable increase in energy consumption both in emergent and well-established countries has been witnessed [1]. The climb in energy prices and fossil fuel defiance, in addition to harmful pollution as a side effect of conventional generation, increased the in- vestment and encouraged the development of renewable energy gen- erators (REG) [2]. Nowadays, > 14% of the worldwide energy pro- duction is based on energy from renewable sources [3]. Photovoltaic and wind generators represent the most significantly growing renewable electricity generating technologies, due to ever rising efficiency, good infrastructure and relative cost competitiveness [4]. It is well known that REG electrical characteristics possess (at single unit level) a single maximum power point (MPP) for specific environmental conditions set (energy generating variable ξ and tem- perature T), as shown in Fig. 1 [5]. Therefore, the amount of harvested energy (and hence overall efficiency) decreases if REG operating point does not coincide with the MPP [6]. As a result, maximum power point tracking (MPPT) operation is extremely desirable for any combination of environmental conditions and load, achieved by suitable control of interfacing power converter (IPC), decoupling the REG from the load [7]. It should be emphasized that non-MPPT operation is sometimes required as well in case of isolated microgrids, there the ability of loads and storage elements to absorb power is limited [8]. MPPT algorithms has evolved significantly over the years, conse- quently requiring high computational power and advanced technology [9]. Established MPPT techniques are typically categorized into three main groups [10]: • Artificial intelligent methods [11]. • Direct methods [12]. • Indirect methods [13]. The artificial intelligent method uses fuzzy logics, genetic algo- rithms etc. [14] for MPP determination. In direct methods, the MPP is searched by perturbing the operating point (typically using hill climbing or perturb and observe related algorithms) and spotting the resulting power gradient [15]. Indirect methods (also known as quasi- seeking) is based on mathematical rules that apply data providing by REG manufacturers or acquired from experiments for tracking the MPP https://doi.org/10.1016/j.enconman.2018.04.053 Received 15 December 2017; Received in revised form 25 March 2018; Accepted 13 April 2018 ⁎ Corresponding author at: Dept. of Electrical and Computer Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel. E-mail address: alonk@bgu.ac.il (A. Kuperman). Abbreviations: REG, renewable energy generator; MPPT, maximum power point tracking; MPP, maximum power point; PVG, photovoltaic generator; IPC, interfacing power converter; IVC, input voltage controller; CC, current controller; INC, incremental conductance; DCE, Dynamic Conductance Estimator; PWM, pulse width modulation Energy Conversion and Management 166 (2018) 687–696 0196-8904/ © 2018 Elsevier Ltd. All rights reserved. T