A Cyber-Physical Photovoltaic Array Monitoring and Control System (original) (raw)

Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System

Energies

The major problem associated with the grid-connected solar photovoltaic (PV) system is the integration of the generated DC power into the AC grid and maintaining the stability of the system. With advancements in research on these PV inverters, artificial intelligence (AI)-based control models are replacing the existing linear methods. These smart PV systems are prone to a variety of attacks, ranging from physical attacks on the PV plants to data integrity attacks and communication-based attacks. This paper provides an overview of the cybersecurity issues with smart PV inverters, their impacts on the grids, and control methods that exist to detect and identify cyber-attacks on a smart PV grid system. An extensive bibliography is provided on grid-forming and grid-following inverters with a variety of control techniques like Proportional–Integral–Derivative (PID) control, fuzzy-based control, and their performances under different fault situations. Multi-level inverter design approache...

Intelligent Cloud-Based Monitoring and Control Digital Twin for Photovoltaic Power Plants

2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), 2022

A main challenge in the scope of integrating higher shares of photovoltaic (PV) systems is to ensure optimal operations. This can be achieved through next-generation monitoring with automatic data-driven functionalities. This work aims to address this fundamental challenge by presenting the stage of implementation of an advanced cloud-based monitoring platform and a control digital twin for PV power plants (MW scale). The platform is fully equipped with a multitude of artificial intelligent (AI) algorithms for health-state diagnostics and analytics. The performance of the digital twin to act as a health state monitor was validated against field and synthetic data from PV systems at different locations and demonstrated high accuracies for PV performance modelling and fault diagnosis.

AN OVERVIEW OF REMOTE MONITORING PV SYSTEMS: ACQUISITION, STORAGES, PROCESSING AND PUBLICATION OF REAL- TIME DATA BASED ON CLOUD COMPUTING

Renewable energy sources (RES) are regard as an important alternative for significantly contributing to the sustainable energy supply in the world. Photovoltaic (PV) systems generate electricity from solar radiation. They are one of the most emerging RES technologies due to their continuous technological progress and their constant cost reduction. Monitoring PV systems is necessary to provide information that allows their holders to maintain, operate and control these systems, reducing installations costs and avoiding unwanted electric power disruptions. This paper presents an overview of remote monitoring solutions for PV installations along with the main proposals, commercial products and international experiences proposed in the literature. The paper is focused on three main features: i) consumption of data measuring and recording devices, ii) high storage requirements for certain conditions of data logging frequency or historical data series register, and iii) proprietary software provided by solar manufacturers. Furthermore, a detailed review of cloud-based platform for monitoring of PV systems is present. This review focuses on real experiences based on following features: i) low consumption wireless sensors to gather real-time information, ii) extreme scalability of the system, iii) huge amount of information (real-time data and periodic reports) to process, store and publish, and iV) a set of open source e-services and web applications. Finally, a proposal of new architecture for isolated PV monitoring systems in Ecuador is described.

Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance

Energies

A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance iss...

Design and Development of an Online Smart Monitoring and Diagnosis System for Photovoltaic Distributed Generation

Energies

In photovoltaic power plants, fault diagnosis tools are essential for ensuring a high energy yield. These tools should be capable of accurately identifying and quantifying the factors behind the various fault mechanisms commonly found in photovoltaic plants. Considering the aforementioned factors, this article proposes an online smart PV monitoring solution, which is capable of detecting malfunctions that arise from accidental and/or technical causes through the analysis of I-V curves, however, without the necessity to interrupt the operation of the system, thus reducing the maintenance cost. Accidental causes can lead to the reduction of energy productivity due to the excessive accumulation of dirt on the photovoltaic modules, partial shading and eventual errors that occur during its installation. On the other hand, technical causes can be attributed to faults found on the photovoltaic modules, which lead to gradual losses in their electric and material characteristics. Therefore, ...

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Sensors

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based meth...

Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution

Journal of Sensor and Actuator Networks, 2020

The large spread of Distributed Energy Resources (DERs) and the related cyber-security issues introduce the need for monitoring. The proposed work focuses on an anomaly detection strategy based on the physical behavior of the industrial process. The algorithm extracts some measures of the physical parameters of the system and processes them with a neural network architecture called autoencoder in order to build a classifier making decisions about the behavior of the system and detecting possible cyber-attacks or faults. The results are quite promising for a practical application in real systems.

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

International Journal of Photoenergy, 2022

Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimati...

PVInGrid: A Distributed Infrastructure for evaluating the integration of Photovoltaic systems in Smart Grid

Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator.

Machine learning for monitoring and classification in inverters from solar photovoltaic energy plants

The efficiency of solar energy farms requires detailed analytics and information on each inverter regarding voltage, current, temperature, and power. Monitoring inverters from a solar energy farm was shown to minimize the cost of maintenance, increase production and help optimize the performance of the inverters under various conditions. Machine learning algorithms are techniques to analyze data, classify and predict variables according to historic values and combination of different variables. The 140 kWp photovoltaic plant contains 300 modules of 255 W and 294 modules of 250 W with smart monitoring devices. In total the inverters are of type SMA Tripower of 25 kW and 10 kW. The 590 kWp photovoltaic plant contains 1312 Trina solar 450 W modules. In total the four inverters are SMA Sunny Tripower type of 110-60 CORE 2 with rated power of 440 kW were analyzed and several supervised learning algorithms were applied, and the accuracy was determined. The facility enables networked data and a machine learning algorithm for fault classification and monitoring was developed, energy efficiency was calculated and solutions to increase energy production and monitoring were developed for better reliability of components according to the monitorization and optimization of inverters.