PV System Power Research Articles

Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2.

Improving Performance of Solar Array Fed BLDC Drive Using Firefly Algorithm Based High Gain Converter

The improved transient response of PID controller for improved Luo converter supplied Brushless DC (BLDC) motor using solar energy source is extensively investigated. The BLDC drive uses a hybrid PV system and battery-based power generation as a power source to assure continuous power supply to pump water regardless of solar insolation. The PV system serves as the main energy source, and the battery providing as a backup source that discharged its power only during inclement weather or at night if the photovoltaic panel is insufficient to operate the centrifugal pump. The solar panel feeds power into the battery, whenever water to the field is not required, thus there is no need for an extra power source to charge the batteries. The high output voltage gain DC-DC converter does not make any discomfit to the system performance in regards to irradiance variation, switching loss, or power loss by converter or motor side respectively. Moreover, current sensor, voltage sensor, and control circuits are completed excluded which enhance performance and the cost of the system get reduced effectively. A bidirectional charging control allows a bi-directional converter to alter the battery operation mode automatically. The overall system performance is validated using simulation and experimental setup.

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

A Sensorless MPPT Approach For PV Pumb System Used BLDC Motor

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

Control approach for photovoltaic inverters enhancing the primary grid using the virtual synchronous generator concept

This paper presents a control scheme for virtual synchronous generators (VSGs) in PV inverters, designed to enhance grid frequency and voltage. Through the skillful management of active and reactive power, this control scheme enables PV inverters to interact seamlessly with the main grid in response to grid events, including voltage and frequency fluctuations. The VSG controller is implemented using Matlab Simulink, and its effectiveness is rigorously assessed under various scenarios in a case study involves a 50 kVA rated PV inverter, a 50 kW rated PV system, and a 220 V grid phase voltage. In conditions of low power generation from the PV system (solar radiation of 200 W/m²) and high load power (120 kW and 37.5 kVAr), the load voltage drops to 202.4 V. The VSG controller successfully raises the grid voltage by 17.6 V, stabilizing it at 220 V. Conversely, in scenarios of high PV power generation (solar radiation of 1000 W/m²) and low load power (20 kW and 7.5 kVAr), the grid voltage surges by 10 V, reaching 230 V. The proposed control strategy adeptly fine-tunes the voltage, ultimately stabilizing at 220 V. Additionally, when the frequency deviates within ±0.4 Hz from the nominal frequency of 50 Hz, the proposed control effectively restores the frequency to its nominal value.

Analysis of PV power plant performance considering combination of different MPPT algorithms, shading patterns and connection types

The effeteness of MPPT algorithms is deteriorated by having more Maximum Power Points (MPPs) on the characteristics of the PV array. The number of MPPs can increase with the different connection types of PV panels and the pattern of partial shading, and it also changes the P–V (Power–Voltage) characteristics of the panel. Therefore, it is important to consider different combinations of algorithms, connection types and partial shading patterns for an effective PV plant design. This study assesses the performance of a PV power plant including a 5 × 5 PV array by considering eight different connection types, six partial shading conditions (PSCs), and three well-known MPPT algorithms, all together, on the TMS320F28335 DSP platform. The obtained results and analyses show that the TCT (Total Cross Tied) connection type has higher performance than the other connection types under all PSCs with an average of 25.98% Mismatched Loss (ML), 54.3% Fill Factor (FF) and 13.45% efficiency (η). As for the shading patterns, the Diagonal (DG) pattern has less effect on the system’s ML and FF values, and the Long Wide (LW) pattern is less effective in reducing system η. Regarding the algorithms, it has been proven that the performance of a PV system using the simplest form of the basic algorithms can reach the performance of the system using complex algorithms when the connection type is well-adjusted according to the shading patterns. Overall, the given analyses and results prove the significance of taking into account the combination of different MPPT algorithms, types of panel connections, and partial shading patterns to enhance PV system power efficiency and reduce the complexity of PV power plant installations.

DEVELOPMENT AND PERFORMANCE EVALUATION OF ROOF TOP SMALL SCALE SOLAR PV SYSTEM

Many renewable energy conversion systems have been built in order to reduce the use of fossil fuel and minimize global warming. Solar power plant, one of many renewable energy systems, has promising potential in green energy sector. However, there are only a few solar PV system installed in roof top of a building. Thus, the present work aims to development small scale roof top solar PV system and to investigate its performance. The 300 WP solar PV system is installed on a roof top of the office building of campus III-IST. AKPRIND. The performance of the system in terms of output current, output voltage, output power, and conversion efficiency. The results shows that maximum output current of 3.91 ampere and maximum output voltage of 58.29 volt were obtained at day 3 and day 5, respectively. The output current give predominant effect on output power of the solar PV system. The trend of output power is similar to that of output current. Maximum output power of 173.25 W was generated at day 3 where maximum output current was measured. Thus, it can be figure out that the solar PV system has an conversion efficiency of almost 58%.

Techno-economic feasibility study for the use of photovoltaic energy on the south-east algerian farms

This works aims to study the technological feasibility and economic viability of standalone Photovoltaic system for the electrification of farms in the south-east of Algeria. The PV system power plant is sized to supply potato farm in Akfadou-El Oued. The standalone PV system was designed and simulated using the Hybrid Optimization Model for Electric Renewable (HOMER). Simulation results showed that a high capital cost is needed to install the PV system with a high cost of energy reaches 30 DA/kWh.

Transition towards a sustainable power system: MA-DA&DC framework based voltage control in high PV penetration networks

Grid control systems must deal with voltage fluctuation and power losses because of the rising saturation of Renewable Energy Resources (RERs) in the Power Distribution Network (PDN). There have been several methods used to mitigate these inevitable voltage instabilities, but PV inverters are regarded as the most efficient ones. On the other hand, achieving optimum management and real-time coordination of several PV inverters in a PDN remains essential while preventing large power losses. The suggested work utilizes Decentralized Actors and Distributed Critics (DA&DC) architecture for multi-agents to obtain the optimal control and coordination of the agents (PV-inverters) incorporated into the distribution network. The critics employ a distributed control system, while the actors operate in a decentralized method. The proposed technology reduces computing complexity through multi-agent distributed training and decentralized execution. The suggested model is tested on IEEE-33, and results are obtained for the winter and summer seasons to analyze the influence of seasonal fluctuations on the outcomes of voltage variation, Power Losses (PL), Active Power (AP), and Reactive Power (ReP) of buses and PV systems. By reducing the voltage fluctuation ratio to 0.01327, the suggested approach achieves 85% voltage control. Moreover, average voltages of 0.9854 p.u. and 0.9950 p.u. were recorded throughout the summer and winter seasons, respectively. The PL are also maintained to an absolute minimum i.e., 0.0476 MW in the winter and 0.0535 MW in the summer. The results conclusively demonstrate that the proposed methodology outclasses its counterpart in both the summer and winter seasons.

A New Intrinsic Power Balancing Control Method for Boosting Grid-Connected PV System Power Stability

Photovoltaic sources have recently become economical electrical generation resources with high potential power generation systems. The increasing rate of PV system penetration and grid integration has raised serious concerns about the stability and reliability of energy networks. The grid-connected inverter and its operations are taken into consideration in the architecture to provide grid failure robustness. As a result, the capacity of the Low Voltage Ride-Through photovoltaic grid-connected power production system to inject more reactive power into the grid when the voltage disturbance is large has become the most critical issue correlated to the grid. The proposed IPBC controller circuit automatically adjusts the duty cycle value, a desired constant output voltage value, and the load output. To achieve this objective, a suitable feedback controller is necessary to regulate the output voltage to a reference value by automatically controlling the system’s duty cycle, quick response and output load, minimum overshoot zero steady-state error. The effectiveness and reliability of the proposed IPBC controller-based grid-connected solar power generation are developed and verified MATLAB 2017 b software environment. Several simulation programs are based on the operation and performance under different operating conditions evaluated for each of different parameters like steady-state error, THD, and system efficiency.

Fault Detection System of Photovoltaic Based on Artificial Neural Network

Using PV systems, solar energy may be used to create electricity. Every year, the proportion of solar energy in the electric system increases significantly. On the other hand, photovoltaic cells are susceptible to malfunctions that diminish their efficiency and profitability. Due to the severity of the defects, fault detection and diagnosis (FDD) in the PV system have become difficult. Thus, the primary objective of the proposed study is to detect and diagnose particular types of PV system problems using an artificial neural network (ANN). This early operation is more effective for avoiding errors during PV installation and minimizing PV system power losses. A new solar cell model is created in the MATLAB/SIMULINK environment and is used to identify the fault dataset. The solar cell consists of three parallel strings and three series modules, with each module containing 20 series-connected photovoltaic cells. This model determines four parameters (V-load in Volts, I-load in Amps, Irradiance in W/m2, and Temperature in Celsius) under varying situations (five temperature values, three irradiance values, and three-time events), which are repeated for all faults evaluated. In the training and testing phases of the proposed ANN, these four parameters are utilized as effective features. Additionally, this network possesses eight outputs, one for each defect. Using implementations with three different numbers of hidden layers and an identical fault dataset, the performance of the proposed network is tested. The findings of the simulation indicate a considerable proportion of fault detection and diagnosis accuracy, ranging between 95% and 98%. The computed RMSE reveals that the training period yields 0.193% improvement, whereas the testing phase yields 0.365%.

FPGA Based Integrated Control of Brushless DC Motor for Renewable Energy Storage System

To reduce air pollution and global warming, renewable energy technologies may generate power. Wind, solar PV, and fuel cell energy are the primary sources. Solar PV system-powered brushless direct current motor (BLDC) drives are used in the automobile industry due to their importance. In this study, Sheppard–Taylor (S-T) converter and Pulse Width Modulated (PWM) Inverter-fed BLDC provide steady voltage across the BLDC motor drive independent of solar PV system power output. When renewable energy is scarce, the proposed battery-supercapacitor hybrid energy storage system (BS-HESS) provides electricity. S-T converters may be used for load matching and power processing to create energy-efficient systems and stabilize PV panel output voltage. The variable step size open circuit voltage-Maximum Power Point Tracking (VSSOCV-MPPT) technique in S-T converter switching pulses extracts maximum power from the solar PV system. This study considers the PVSWPS control function as a Port-Controlled Hamiltonian (PCH) system to continue rural growth and reduce the greatest demand and load. MATLAB/Simulink software simulates the system’s performance, and FPGA controllers validate the controller’s real-time performance.

Energy management strategy with smart building control system to reduction electrical load using ANN

Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.

The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies.

Local and Remote Cooperative Control of Hybrid Distribution Transformers Integrating Photovoltaics in Active Distribution Networks

This paper proposes a hybrid distribution transformer integrating photovoltaics (HDT-PV) with local and remote cooperative control in an active distribution network (ADN). HDT-PVs, regarded as promising devices with high reliability and easy engineering in ADNs, can not only realize the on-site maximum utilization of distributed solar energy but also solve the various power quality problems that follow. Moreover, the reactive power flow in the ADN can be optimized through remote cooperative control of multiple HDT-PVs. In this situation, the two objectives of voltage regulation and network power loss decrease are achieved in the ADN. If the power electronic device in the HDT-PV fails, the HDT-PV is able to work as a conventional distribution transformer by cutting off all power electronic devices and PV systems. Even when a small number of HDT-PVs malfunction, the system can still run stably. Finally, the simulation and experimental results demonstrate the effectiveness of the proposed scheme.

Solar radiation performance adjusting to PV system

The first section of this paper presents the conditions of solar radiation orientation in Kosovo. The sheer existence of the sunlight is indeed an inexhaustible source of renewable energy having ample potential to meet all humankind’s needs for it when innovative technology is used in compliance with modern standards appropriate to economic and social environment and to the nature itself, too. The research conducted for the purpose of the present paper reveals that the greatest amount of radiant energy is focused on the absorber of the collector sensor which transmits the entire moving space at right angles to the sunlight. It is important to note that the collector angle in relation to the horizontal plane cannot be less than 20°, because there is a possibility that the collector, due to the small angle, is covered in dirt and aerosol pollution. These data ensure that best performance in high generation efficiency is reached by improving harnessing patterns in solar cell response. The objective of the Kosovo Plan in 10 years’ period has stimulated the support policy for renewable energy sources, set to be at least 10% at the national level. This paper examines radiation efficiency assessments under sensor monitoring over the absorption space where all time, high absorption power PV system panels are located. Experimental study shows that Kosovo has radiation potential due to its Geographical position equal to 1400kWh, with the optimal sensor orientation angle of 25° in the Gjakova Region. The solar radiation efficiency for one-year period has resulted in increased performance under sensor monitoring during the months of March - September, from 0.89 kWh/m2/y to 0.92 kWh/m2/y, when the equinox provides the longest sunlight intervals.

Optimal PV system capacity ratio and power limit value selection based on a novel fast calculation method of IGBT junction temperature and IGBT annual damage analysis

In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the XGBoost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The XGBoost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value.

Marmara Bölgesindeki Bir Konteyner Limanı İçin Fotovoltaik Enerji Sistem Kurulumunun Tekno-Ekonomik ve Çevresel Analizi

Dünyada 1960’lı yıllardan itibaren gelişen konteyner gemileri ve konteyner limanlarının, ülkemizin dış ticaret, kabotaj ve transit yüklerinin taşınmasında önemli bir rolü bulunmaktadır. Özellikle Marmara Bölgesinde yer alan konteyner limanlarının ülkemizin toplam konteyner elleçleme kapasitesinde önemli bir payı vardır. Diğer taraftan, güneş enerjisinden faydalanarak elektrik üretimi, dünya genelinde olduğu gibi ülkemizde de yaygınlaşmakta olup konteyner limanlarında da bu teknolojiden azami ölçüde faydalanılması gerekmektedir. Bu çalışmanın amacı; Marmara (Kocaeli) Bölgesinde bulunan büyük ölçekli bir konteyner limanında fotovoltaik yenilenebilir enerji sistemi kurulumunun tekno-ekonomik ve çevresel analizini gerçekleştirmektir. Bu amaçla, büyük ölçekli bir konteyner limanı incelenmiş, yıllık elektrik ihtiyacı tespit edilmiş ve Fotovoltaik Coğrafi Bilgi Sistemi yazılımı yardımıyla uygun PV sistem gücü belirlenmiştir. Ayrıca, İndirgenmiş Geri Ödeme Süresi Yöntemi kullanılarak PV sistemin ilk yatırım maliyeti ve geri ödeme süresi hesaplanmış ve RETScreen yazılımı yardımıyla PV sisteminin zararlı sera gazı salımını azaltmadaki rolü ve çevresel etkileri analiz edilmiştir. Çalışmada, konteyner limanındaki kapalı binaların çatılarına 962 k Wp gücünde PV sistem kurulabileceği ve PV sistemin bu güç ile yılda 1.061.034 kWh elektrik üretimi gerçekleşebileceği sonucuna ulaşılmıştır. Limana yapılacak PV sistem yatırımının geri ödeme süresi 8,33 yıl olarak belirlenmiştir. Söz konusu PV sistemin yılda 37,7 tCO2/yıl sera gazı emisyonuna engel olacağı da tespit edilmiştir. PV sistemden üretilecek elektrik ile limanının yıllık elektrik ihtiyacının %9,47’lik bir kısmı sağlanabilmektedir. Bu çalışmada, bir limana kurulacak PV sistemden en yüksek seviyede yararlanılmasına katkı sağlamak üzere bazı öneriler de sunulmuştur.

A modified power management algorithm with energy efficiency and GHG emissions limitation for hybrid power ship system

With the integration of energy storage system (ESS), photovoltaic cell (PV) and generator, hybrid power ship system (HPSS), as one of promising technology, is regarded as an advanced method to improve energy efficiency and marine environment quality. However, the computational complexity and non-convexity of energy scheduling in hybrid power ship system make it challenging to obtain the feasible solution. To address this crucial issue, a heuristic optimization algorithm named multi-populations particle swarm optimization (MPPSO) is proposed for economic and feasible energy scheduling. Firstly, a hybrid power ship system, comprising generator, ESS, PV, service loads and propulsion system, is formulated. On this basis, a load shedding coefficient is given for the secure and stable operation of hybrid power ship system under fault model. Then, to achieve energy scheduling, several improvements are proposed to enhance PSO. Considering the problem of premature, a nonlinear adaptive inertial weight strategy is proposed to improve the searching ability. With the fitness value of population, learning coefficients are adjusted in nonlinear so that particle can accurately learn from individual or population position. Further, a modified velocity update formula with the information of historical experience and center particle is proposed to employ the particle information fully. Finally, the effectiveness of MPPSO is illustrated on simulation experiment by three cases.

PV Solar Energy Supply in Smart Cities

The application of solar energy and other renewable energy sources (wind, hydro, etc.) enables the connection of low-power electrical energy generation units to the grid. This new concept called distributed energy generation, provides opportunities to separate parts of existing distribution networks into smaller units, i.e., microgrids, which can be self-sufficient and sustainable for shorter or longer periods. In a smart grid environment, efficient usage of energy and better consumer energy supply is possible, especially in cases of failures in the main grid. This paper presents a method for the assessment of possible rooftop PV system power capacity in a smart city. Special attention will be paid to the generation units coming from renewable energy (solar) and the possibility of supplying existing loads. As a case study, the results of a hypothetical project on the possibility of PV solar energy application for the electricity supply in some cities in Serbia are presented.