Mismatch Based Diagnosis of PV Fields Relying on Monitored String Currents

Abstract

This paper presents a DC side oriented diagnostic method for photovoltaic fields which operates on string currents previously supplied by an appropriate monitoring system. The relevance of the work relies on the definition of an effective and reliable day-by-day target for the power that every string of the field should have produced. The procedure is carried out by comparing the instantaneous power produced by all solar strings having the same orientation and by attributing, as producible power for all of them, the maximum value. As figure of merit, the difference between the maximum allowed energy production (evaluated as the integral of the power during a defined time interval) and the energy actually produced by the strings is defined. Such a definition accounts for both weather and irradiance conditions, without needing additional sensors. The reliability of the approach was experimentally verified by analyzing the performance of two medium size solar fields that were monitored over a period of four years. Results allowed quantifying energy losses attributable to underperforming solar strings and precisely locating their position in the field.

On the Link between the Operating Point and the Temperature Distribution in PV Arrays Working under Mismatching Conditions

“…Active bypass switches are an improvement over bypass diodes but do not resolve the problem [7]. There are two further methods to prevent hot spots: method 1-regulation of the operating point (make the PV string work in open circuit in extreme cases) [26,37,38] and method 2-ensure that the cell can fully dissipate the worst-case power scenario without damages [7]. For example, cells with low reverse-breakdown voltages limit the power dissipated during hot spots and may be an effective prevention method.…”

Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants

“…In such conditions, it is difficult to induce realistic noise sources such as mixture of several fault types and diversity of operating conditions. Moreover, synthetic data can be generated at an arbitrary granularity level: except for very few research works (Guerriero, Piegari, Rizzo, & Daliento, 2017;Skomedal, Øgaard, Selj, Haug, & Marstein, 2019), all models assume data availability from single PV panels, despite the fact that real operational data is usually available at the level of PV-strings or even inverters, potentially gathering information from hundreds or thousands of panels. Thus, algorithms that are developed under synthetic conditions are not directly usable for commercial deployment.…”

Using EMPHASIS for the Thermography-Based Fault Detection in Photovoltaic Plants

“…For the aforementioned reasons, diagnosis represents a valuable strategy to detect and analyze the reduction in PV plant reliability [19]. The literature is populated by many fault-detection approaches [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], a first classification of which can be based on their granularity. While in [23] the investigation involves the whole PV plant, methods aimed at monitoring PV strings are shown in [24,25].…”

“…The literature is populated by many fault-detection approaches [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], a first classification of which can be based on their granularity. While in [23] the investigation involves the whole PV plant, methods aimed at monitoring PV strings are shown in [24,25]. The granularity is even higher in the approach presented in [26,27], in which sensing circuits to be mounted on each PV panel are exploited.…”