Photovoltaic Radiation Research Articles

Fractional order pole fixed second order generalized integrator based control for grid connected solar photovoltaic system

AbstractIn this article, the development of multi‐functional grid connected solar photovoltaic (PV) system using improved Fractional order Control theory‐based Pole fixed Second Order Generalized Integrator (FO‐PFSOGI) is proposed. The fractional order (FO) control technique offers an advantage to adjust the fixed structure of the integer order and provide additional degree of freedom to achieve accurate response during the system operation. The FO‐PFSOGI control technique is used to extricate fundamental constituent of the non‐sinusoidal load current. The PV system is competent of feeding the local load requisite and may also inject surplus power into the grid. The voltage source converter (VSC) utilized in the grid‐connected system can also be operated as power quality compensator, taking care of harmonics, unbalancing and excess reactive power demand of the local load. The feasibility of multifunctional operations of the converter is established in this article, thus achieving maximum utilization of the power electronics used in the system and reducing the overall cost. A performance comparison of the developed control technique is presented with the conventional techniques in terms of harmonic compensation, weight convergence and computational complexity. The implementation of the controller is modest and extracts fundamental quantities without any phase delay under different loading conditions. The developed system is validated using both simulation and experimental study. A scaled down experimental setup of grid‐connected PV system is implemented in the laboratory and real time performance of the FO‐PFSOGI is demonstrated using variations in system load and PV irradiance.

A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays

To improve the operational efficiency and reliability of photovoltaic power stations, this paper introduces a novel approach to detect outliers in photovoltaic arrays using a Vine-Copula method. The procedure is divided into two distinct phases. Initially, it identifies deviations in the direct current (DC) component of the photovoltaic (PV) system. The following phase extends this by pinpointing irregularities in the DC voltage of the array. To model the interconnection between the PV current, irradiance, and temperature, the Vine-Copula is employed in this process. The optimisation of this function is based on the Akaike information criterion. Subsequently, a conditional probability model for the PV current is developed along with a formula to determine the quantile of this probability. This interval is then employed as the primary metric for detecting and eliminating current deviations. After refining the current data, a similar approach is taken to address voltage irregularities. The results of the simulation tests indicate that this proposed method is more effective, showing lower error rates and higher accuracy in detecting outliers, compared to other methods.

A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%.

Optimal Evaluation of Photovoltaic Cells Parameters Using Euclidean Distance Calculations

The relationship between current and voltage describes the features and characteristics of the photovoltaic (PV) cells. This relationship mainly depends on the equivalent circuit parameters of the PV cell model. Accurate estimation of these parameters is crucial for analyzing the performance of PV systems. This paper proposes a simple and efficient method to estimate the equivalent circuit parameters of the PV cells/modules. The main concept of the proposed method is to optimize the PV series resistance value using Euclidean distance calculations in such a way to get the corresponding best maximum power conditions. Various assessments have been employed in this paper to confirm the validity of the presented approach. Those include analyzing different commercial PV modules, experimental data, irradiance and temperature variations, and comparison with other reported algorithms. When compared with experimental data at standard test conditions, the mean absolute current and power differences are 0.0329 A and 0.6339 W, respectively. Furthermore, the mean absolute differences at normal operating cell temperature are 0.0120 A and 0.1412 W. The results have shown that the proposed method has confirmed its effectiveness in predicting the PV cell equivalent circuit characteristics for any PV cells/modules using only data available from the manufacturer’s datasheet.

Performance analysis of photovoltaic panel using machine learning method

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.

Multiple voltage source converters based microgrid with solar photovoltaic array and battery storage

In this paper, a standalone photovoltaic (PV)-battery storage (BS) based microgrid (MG) is presented with a 415V-AC bus. The PV array is linked to the main voltage source converter (VSC1) DC-link to maximize power production in an efficient and cost-effective single-stage structure. This is achieved through an incremental conductance (INC) maximum power point (MPP) tracking approach. Moreover, a secondary converter (VSC2) is connected to BS for power flow management. MG performance is evaluated under various operating conditions. Steady-state and dynamic performances of MG are studied for variations such as changes in solar PV irradiance and load unbalances.

Improved vµ-LMS-Controlled Single-Phase Grid Tied PV Powered Electric Vehicle Battery System with High Gain DC-DC Converter and Power Management

The increasing solar photovoltaic (PV) and EV charging applications create power quality issues at utility grid. It requires efficient control thus, in this paper, single-phase grid-tied PV powered electric vehicle battery (EVB) system is proposed with reliable control and power management. The presented work offers efficient EVB charging either from PV power or utility to handle PV impacts on the utility. During peak power demand, the proposed power management offers less burden on utility and allows EVB to feed increased load along with changing PV power. In work, a recent high-gain DC-DC boost converter topology is utilized thereby making smaller household PV arrays in grid-tied mode with DC-AC voltage source converter (VSC). Moreover, a new improved variable step-parameter ‘µ’ least mean square (IvµLMS) adaptive control is proposed and compared for efficient control of VSC with various nonlinear load and PV irradiance variations. The IvµLMS offers good convergence, less burden on controller and improved dynamic responses under various conditions. As per IEEE-519 standard, the PV power is fetched to grid at unity power factor (UPF) with low total harmonic distortion (THD). The proposed grid tied EVB system, and its controls are modelled in MATLAB/Simulink environment and test results are analyzed and validated on a laboratory prototype.

Genetic algorithm optimization for parametrization, digital twinning, and now-casting of unknown small- and medium-scale PV systems based only on on-site measured data

Accurately predicting and balancing energy generation and consumption are crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, these predictions should ideally be performed based only on on-site measured data and data available within the monitoring platforms, data which are scarce for small- and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a genetic algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of −1.14 W/kWp and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.

Modeling, Simulation, and Experimental Validation of a Novel MPPT for Hybrid Renewable Sources Integrated with UPQC: An Application of Jellyfish Search Optimizer

Changes in climatic circumstances, as well as intermittency, which has a significant impact on the overall energy system output from renewable energy sources (RESs), require the development of control strategies for extracting the maximum power available from RESs. To accomplish this task, several techniques have been developed. An efficient maximum power point tracking (MPPT) technique should be utilized to guarantee that both wind-generation and PV-generation systems provide their full advantages. In this paper, a new MPPT approach with jellyfish search optimization (JSO) is developed; in addition, a unified power-quality conditioner (UPQC) is utilized to enhance the performance of the microgrid (MG) and to solve the power-quality issues for the sensitive load. The MG, which includes a photovoltaic (PV), a wind turbine, and a fuel cell battery, is examined and modeled for uniform and nonuniform wind speed and solar irradiance. A comparison between the developed algorithm and different maximum power tracking algorithms is presented. Additionally, four case studies are carried out to verify the effectiveness of the introduced UPQC in enhancing power-quality problems. The research outcome shows high performance from the developed algorithm when assessed with additional algorithms. MATLAB/Simulink software is utilized for the simulation of the wind, PV, and FC control systems. However, experiment validation tests are given under the same condition of PV irradiation to validate the simulation results. The experimental validation is executed by utilizing the PV module simulation model, threefold, 23 V/2A CO3208-1A with solar altitude emulator CO3208-1B board, and the results are compared to the simulation results.

Data Driven Based Maximum Power Prediction for Solar Photovoltaic System

This manuscript proposes a data-driven machine learning algorithm to track maximum power for PV (photovoltaic) panel systems. Data from the PV panel system connected to a boost converter has been collected. PV Voltage, current, temperature, irradiance, PI and power value have been collected for the supervised machine learning-based modeling. Where PV Voltage, PV current, temperature, and irradiance are the predictors, and PI (proportional integral) is the response of the machine learning-based model. The proposed system becomes more efficient with time while existing MPPT (maximum power point tracking) work on a specific logic for whole life. The model efficacy has been analyzed based on accuracy, scattering plot, and ROC (receiver operating characteristics) curve.

Study on Integration of Retired Lithium-Ion Battery With Photovoltaic for Net-Zero Electricity Residential Homes

Abstract The behavior of a retired lithium-ion battery (LIB) from its first-life in an electric aircraft (EA) to its second-life in a solar photovoltaic (PV) system for a net-zero electricity residential home is studied. The first part of this study presents the design and sizing of a battery energy storage system (BESS), made from retired LIBs, to store a portion of the PV generation for a typical home in Ohio. The home is connected to the grid, but the net electricity usage from the grid in one year is zero. The purpose of the BESS is to peak shaving, power arbitrage, reduce the home dependency on the grid, and increase the economic benefits. The sizing is determined based on the hourly data of the PV system generation, ambient temperature, irradiation, and home demand electricity. In the second part of this study, the retired LIB degradation rate and its remaining useful life in the BESS are estimated using an adopted empirical LIB model. The model includes the capacity-fade for both first-life and second-life of the LIB under various duty cycles. It is shown that the retired LIB from its first-life is still suitable to be used in the PV grid-tied battery (PVGB) system for another 10 years. The results of this study can potentially reduce the LIB cost for electric vehicles (EVs) and EAs because the retired LIBs from these applications still have value to serve for other applications such as PVGB systems for residential homes.

Hyperparameter optimization strategies for machine learning-based stochastic energy efficient scheduling in cyber-physical production systems

One of the major challenges faced by system operators in an integrated energy system (IES) is to come up with an optimal schedule of the energy generation units in the presence of uncertainties at both the demand and supply sides. Employing efficient forecasts of the uncertain parameters while ensuring the least possible prediction error is crucial to the overall scheduling of IESs. Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) based architecture is widely used in natural language processing and time series forecasting and has reported to be a viable and powerful forecasting technique. The aim of this work is to enhance the autonomy of energy scheduling in cyber-physical production systems (CPPS) through the development of data-driven LSTM based forecast models for the uncertain parameters. However, one of the demanding tasks in time series forecasting methods such as LSTM and the subject that received little attention is the judicious choice/fine-tuning of the hyperparameter values, which significantly impacts the performance of the developed forecasting models. This paper mainly focuses on the performance analysis of various hyperparameter tuning techniques and algorithms used by LSTM networks in forecasting uncertain parameters for stochastic energy scheduling in the context of smart manufacturing. The plant under consideration is the highly energy intensive Model Factory (MF) at the Singapore Institute of Manufacturing Technology (SIMTech), that features an actual production environment supporting both experimentation and learning of digitalisation technologies for Industry 4.0. Three main energy sources installed in an IES framework consisting of solar photo voltaic (PV) system, waste-to-energy (WTE) unit and the main utility grid serves as the energy supply module to meet the requested energy by the industrial facility. Various strategies to tune the LSTM hyperparameters in forecasting the uncertain parameters are examined. Among the four strategies, the first one deals with the traditional manual tuning approach and the second one focuses on automating the traditional manual approach using a FOR loop. While the third one focuses on a tuning strategy using Optuna with a grid search algorithm, the fourth strategy concentrates on adopting Optuna with a Bayesian optimization framework. The overall objective entails generating an optimal day-ahead forecast of the uncertain parameters (in this case solar PV irradiance, temperature, main utility grid energy price and the energy demand of the MF) using LSTM based on the best performing hyperparameter tuning techniques. These forecasts are then utilized to solve a day-ahead stochastic energy scheduling problem that generates an optimal energy dispatch schedule with the overall objectives to minimize the total operational cost and carbon emission.

Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network

The implementation of solar photovoltaic (PV) based Direct Current (DC) microgrid is limited due to lack of well-defined protection standards. These low voltage DC distribution networks are facing implementation challenges especially under multi-distributed generations subjected to various events such as PV arc faults, load switching faults, PV partial shading, change in PV irradiance and DC cable faults, etc. Thus a new deep learning neural network model is proposed for the accurate fault classification and distance calculation for an effective monitoring and protection coordination of photovoltaic based DC microgrid. The proposed model is the combination of an Adaptive variational mode decomposition (AVMD) and deep minimum variance random vector functional link network (DRVFLN). The optimal parameters of AVMD are selected using chaotic sine–cosine firefly algorithm (CSCFA) and efficient weighted kurtosis index (EWKU). Further, a novel non-iterative DRVFLN network is applied for accurate fault classification and location. In the DRVFLN direct connections are present from preceding layers to the forward layers of the network like random vector functional link network. These connections help to reduce the model complexity and also regularize the randomization. Further, the denoising criterion is also introduced in this network where the uncorrupted input can be recovered from the corrupted versions by using the autoencoder to obtained better results than the traditional networks. The performances of various models are evaluated using some performance index such as overall accuracy and sensitivity. Results conclude that the proposed AVMD-DRVFLN model classified the faults with an accuracy and sensitivity of 100% for all the events. The model performance is also tested and validated against the unwanted noise by considering different signal-to-noise ratio to ensure the robustness of the proposed model. Results conclude that the proposed model correctly classified the faults against such incorporation of noise. Similarly, the performances of various models for the estimation of fault distance are validated by computing the relative error. Results conclude that the proposed AVMD-DRVFLN model produces promising result in terms of relative error(which is less than 5%) for all the events. Comparisons with some existing models like extreme learning machine, support vector machine, random vector functional link network and deep extreme learning machine are included to validate the efficacy of the proposed AVMD-DRVFLN model. Results conclude that proposed method outperforms all the other methods. The effectiveness of the proposed AVMD-DRVFLN model for fault classification and distance estimation is established through rigorous case studies in MATLAB augmented by some real time test results.

A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System

Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks.

Energy Management of an Isolated Wind/Photovoltaic Microgrid Using Cuckoo Search Algorithm

This paper introduces a renewable-energy-based microgrid that includes Photovoltaic (PV) energy and wind energy generation units. Also, an energy storage system is present. The proposed microgrid is loaded with a constant load impedance. To improve the performance of the proposed microgrid, an optimal control algorithm utilizing Cuckoo Search Algorithm (CSA) is adapted. It has many merits such as fast convergence, simple tunning, and high efficiency. Commonly, the PV and wind energies are suitable for supplying loads under normal conditions. However, the energy storage system recovers the excess load demand. The load frequency and voltage are regulated using the CSA optimal controller. The microgrid responses with the introduced optimal controller are measured under step changes in load demand, wind power, and the PV irradiation level. Matlab simulations are carried out to test the proposed system performance. The simulation results showed that the proposed microgrid fed the load with Alternating Current (AC) power of constant amplitude and frequency for all disturbances. Moreover, the required load demand has been perfectly compensated. Moreover, the performance of the storage system is excellent with the unstable wind speed and variable solar irradiation. Also, the results with the optimal CSA controller are compared to those with the Particle Swarm Optimization (PSO) algorithm at the same conditions. It is also found that the optimal CSA controller provides better responses.

Integration of PV system with SMES based on model predictive control for utility grid reliability improvement

This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy.

A CVaR-Robust-Based Multi-Objective Optimization Model for Energy Hub Considering Uncertainty and E-Fuel Energy Storage in Energy and Reserve Markets

The increasing demand for energy carriers has expanded the use of energy hubs that employ distributed demand response programs to improve power system reliability and efficiency. Moreover, the unstable behavior of renewable resources, as well as the indeterminate electrical and thermal demands, create major problems for energy hub operation. Inspired by this, this paper presents a day-ahead scheduling framework for energy hubs (EH) in energy and reserve markets considering two main objectives of economy and pollution emission. The studied energy hub consists of a novel hybrid energy storage facility based on a fuel cell, wind power, photovoltaic energy, and a particular fuel cell unit in the presence of elastic demand. This multi-component system participates in energy and reserve markets as a single entity to optimize energy hub operation. The proposed method also models the uncertainty of wind speed, photovoltaic irradiance, and load using the Mont-Carlo method. The energy hub risk level is analyzed using the conditional value at risk (CVaR) approach to increase the EH operation and efficiency. The proposed multi-objective energy hub model is solved using the MINLP method in General Algebraic Modeling System (GAMS) to minimize operation cost and pollution emission. Finally, to prove the effectiveness of adding a new E-fuel energy storage system and considering uncertainties on energy hub operation, the proposed method is compared with other reported models.

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

A simplified method for fault detection and identification of mismatch modules and strings in a grid-tied solar photovoltaic system

AbstractThe foremost problem facing by the photovoltaic (PV) system is to identify the faults and partial shade conditions. Further, the power loss can be avoided by knowing the number of faulty modules and strings. Hence, to attend these problems, a new method is proposed to differentiate the faults and partially shaded conditions along with the number of mismatch modules and strings for a dynamic change in irradiation. The proposed method has developed in two main steps based on a simple observation from the Current versus Voltage (I-V) characteristic curve of PV array at Line-Line (LL) fault. First, the type of fault is detected using defined variables, which are continuously updated from PV array voltage, current, and irradiation. Second, it gives the number of mismatch modules (or short-circuited bypass diodes) and mismatch strings (or open-circuited blocking diodes) by comparing with the theoretical predictions from the I-V characteristic curve of PV array. The proposed algorithm has been validated both on experimentation using small scale grid-connected PV array developed in the laboratory as well as MATLAB/Simulink simulations. Further, the comparative assessment with existing methods is presented with various performance indices to show the effectiveness of the proposed algorithm.

Selection of optimal wavelengths for optical soiling modelling and detection in photovoltaic modules

Soiling impacts the photovoltaic (PV) module performance by reducing the amount of light reaching the photovoltaic cells and by changing their external spectral response. Currently, the soiling monitoring market is moving toward optical sensors that measure transmittance or reflectance, rather than directly measuring the impact of soiling on the performance of photovoltaic modules. These sensors, which use a single optical measurement, are not able to correct the soiling losses that depend on the solar irradiance spectra and on the spectral response of the monitored PV material. This work investigates methods that can improve the optical detection of soiling by extracting the full soiling spectrum profiles using only two or three monochromatic measurements. Spectral transmittance data, measured with a spectrophotometer and collected during a 46-week experimental soiling study carried out in Jaén, Spain, was analysed in this work. The use of a spectral profile for the hemispherical transmittance of soiled PV glass is found to significantly improve the soiling detection, returning the lowest errors independently of the PV materials and irradiance conditions. In addition, this work shows that it is also possible to select the measurement wavelengths to minimize the soiling loss detection error depending on the monitored PV semiconductor material (silicon, CdTe, a-Si, CIGS and a representative perovskite). The approaches discussed in this work are also found to be more robust to potential measurement errors compared to single wavelength measurement techniques.