A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization

Author:

Dimara Asimina12ORCID,Papaioannou Alexios13ORCID,Grigoropoulos Konstantinos1,Triantafyllidis Dimitris1,Tzitzios Ioannis1ORCID,Anagnostopoulos Christos-Nikolaos2,Krinidis Stelios13,Ioannidis Dimosthenis1ORCID,Tzovaras Dimitrios1

Affiliation:

1. Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

2. Department of Cultural Technology and Communication, Intelligent Systems Lab, University of the Aegean, 81100 Mytilene, Greece

3. Management Science and Technology Department, International Hellenic University (IHU), 65404 Kavala, Greece

Abstract

The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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