Photovoltaic Array Output Research Articles

A novel Magic-Square puzzle based one-time PV reconfiguration technique to mitigate mismatch power loss under various partial shading conditions

Partial Shading Condition (PSC) significantly reduce the maximum power output of Photovoltaic (PV) array. There are different techniques to mitigate partial shading problem in PV arrays. One of the best technique to mitigate power losses due to PSC is “Reconfiguration of PV modules” without changing electrical connection. This paper proposes a Magic-Square technique as a one-time solution for any PSC, to increase the power output of the PV array. In this technique, the physical location of PV modules are rearranged based on Magic-Square puzzle without changing electrical connections. In the end, the performance of the proposed Magic-Square has been investigated with the existing SuDoKu and Optimal-SuDoKu patterns with all possible shading conditions. The performance of the proposed technique is studied with Global maximum power point (GMPP), Mismatch power loss (MPL), Fill-Factor (FF) and Efficiency(η) indices.

A Cuckoo MPPT based SMC Control for Grid tied PV System

Maximum power point techniques (MPPT) are used in photovoltaic system to make full utilization of PV array output power. The output power of PV array is always changing with weather conditions i.e., solar irradiation and atmospheric temperature. PV cell generates power by converting sunlight into electricity. The electric power generated is proportional to solar radiation. PV cell can generate around 0.5 to 0.8 volts. During cloudy weather due to varying insolation levels the output of PV array varies. The MPPT is a process which tracks the maximum power from array and by increasing the duty cycle of the DC-DC boost converter, the output voltage of the system is increased. This paper presents the cuckoo mppt technique for PV system along with SMC controller methods in grid connected photovoltaic (PV) systems for optimizing the solar energy efficiency

Evaluation of Mathematical Model to Characterize the Performance of Conventional and Hybrid PV Array Topologies under Static and Dynamic Shading Patterns

The analysis and the assessment of interconnected photovoltaic (PV) modules under different shading conditions and various shading patterns are presented in this paper. The partial shading conditions (PSCs) due to the various factors reduce the power output of PV arrays, and its characteristics have multiple peaks due to the mismatching losses between PV panels. The principal objective of this paper is to model, analyze, simulate and evaluate the performance of PV array topologies such as series-parallel (SP), honey-comb (HC), total-cross-tied (TCT), ladder (LD) and bridge-linked (BL) under different shading patterns to produce the maximum power by reducing the mismatching losses (MLs). Along with the conventional PV array topologies, this paper also discusses the hybrid PV array topologies such as bridge-linked honey-comb (BLHC), bridge-linked total-cross-tied (BLTCT) and series-parallel total-cross-tied (SPTCT). The performance analysis of the traditional PV array topologies along with the hybrid topologies is carried out during static and dynamic shading patterns by comparing the various parameters such as the global peak (GP), local peaks (LPs), corresponding voltage and current at GP and LPs, fill factor (FF) and ML. In addition, the voltage and current equations of the HC configuration under two shading conditions are derived, which represents one of the novelties of this paper. The various parameters of the SPR-200-BLK-U PV module are used for PV modeling and simulation in MATLAB/Simulink software. Thus, the obtained results provide useful information to the researchers for healthy operation and power maximization of PV systems.

Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm

Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.

PV-tied three-port DC–DC converter-operated four-wheel-drive hybrid electric vehicle (HEV)

This paper presents a photovoltaic (PV)-interfaced dual-drive hybrid electric vehicle (PV-DDHEV) based on dual boost three-port DC–DC converter to increase the fuel efficiency of the electric vehicle by increasing the conversion efficiency of the converters. HEV designed with the photovoltaic (PV) power system requires a DC–DC converter to enhance the PV array output voltage to the level demanded by the drive inverters. Normally, the high gain of DC conversion is obtained by connecting two or more DC–DC converters in series with each other. The series connection of the converters reduces the conversion efficiency and increases the cost of the unit. In this paper, a three-port DC–DC converter designed with a two-winding high-frequency transformer is proposed to offer high voltage gain with reduced number of active and passive elements. In addition to this, the power management algorithm incorporated with the proposed PV-DDHEV reduces the power consumption of the drive motor by estimating the drive torque requirement. The MATLAB simulink and experimental prototype results are presented to validate the proposed PV-DDHEV.

Improved strategy of an MPPT based on the sliding mode control for a PV system

The energy produced using a photovoltaic (PV) is mainly dependent on weather factors such as temperature and solar radiation. Given the high cost and low yield of a PV system, it must operate at maximum power point (MPP), which varies according to changes in load and weather conditions. This contribution presents an improved maximum power point tracking (MPPT) controllers of a PV system in various climatic conditions. The first is a sliding mode MPPT that designed to be applied to a buck converter in order to achieve an optimal PV array output voltage. The second MPPT is based on the incremental conductance algorithm or Perturb-and-Observe algorithm. It provides the output reference PV voltage to the sliding mode controller acting on the duty cycle of the DC-DC converter. Simulation is carried out in SimPower toolbox of Matlab/Simulink. Simulation results confirm the effectiveness of the sliding mode control MPPT under the parameter variation environments and shown that the controllers meet its objectives.

A dynamic alarm threshold setting method for photovoltaic array and its application

Abstract The setting of the alarm threshold is critical for photovoltaic (PV) fault diagnosis systems. In practice the prior knowledge is generally used to set the alarm threshold with a fixed value. However, the output of a PV array varies with the external environment, and the distribution of the array electrical parameters has obvious volatility and nonlinearity. Therefore, it is difficult to judge the abnormal state of the PV system under all working conditions by setting the alarm threshold to a fixed value. To solve this problem, this paper proposes a new method for setting the alarm threshold of a PV array output. In this paper, the dynamic variation of the PV array output under different environmental conditions is analyzed, quantile method is adopted to set the threshold of the PV array output, and the dynamic threshold is used to realize the abnormal state judgment of the PV array. Finally, the effectiveness of the proposed method is demonstrated by experimental verification in the actual PV system. The proposed method is simple in calculation and can adapt to dynamic changes in the PV output, which provides a new method for the threshold setting of PV system fault diagnosis systems.

New Feature Extraction Method for Photovoltaic Array Output Time Series and Its Application in Fault Diagnosis

Photovoltaic array produces massive running data, and such data are time series of strong coupling features with each other. In addition, photovoltaic output data has a strong fluctuating and nonlinear feature; it brings extra difficulty to photovoltaic array fault feature extraction and its fault diagnosis. To solve these problems, this article proposes a fault diagnosis method using the time series features for photovoltaic array. The features of the photovoltaic array output are described in this article. From the perspective of distance analysis and similarity analysis, this article proposes a feature extraction method for photovoltaic array output time series, and features of output time series under different fault conditions are analyzed. Taking similarity index and distance index as input, the fuzzy system is built for identifying faults for photovoltaic array. The operational data analysis shows that the time series feature indexes proposed can successfully characterize different fault types, and this method can effectively diagnose typical faults of photovoltaic array.

Gene Evaluation Algorithm for Reconfiguration of Medium and Large Size Photovoltaic Arrays Exhibiting Non-Uniform Aging

Aging is known to exert various non-uniform effects on photovoltaic (PV) modules within a PV array that consequently can result in non-uniform operational parameters affecting the individual PV modules, leading to a variable power output of the overall PV array. This study presents an algorithm for optimising the configuration of a PV array within which different PV modules are subject to non-uniform aging processes. The PV array reconfiguration approach suggests maximising power generation across non-uniformly aged PV arrays by merely repositioning, rather than replacing, the PV modules, thereby keeping maintenance costs to a minimum. Such a reconfiguration strategy demands data input on the PV module electrical parameters so that optimal reconfiguration arrangements can be selected. The algorithm repetitively sorts the PV modules according to a hierarchical pattern to minimise the impact of module mismatch arising due to non-uniform aging of panels across the array. Computer modelling and analysis have been performed to assess the efficacy of the suggested approach for a variety of dimensions of randomly non-uniformly aged PV arrays (e.g., 5 × 5 and 7 × 20 PV arrays) using MATLAB. The results demonstrate that enhanced power output is possible from a non-uniformly aged PV array and that this can be applied to a PV array of any size.

A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.

Modelling and Analysis of PV-Based Shunt Active Filter with BESS and Conservative Power Theory for Enhancing Power Quality

Abstract Power Quality has always been a major concern ever since the increase in the usage of many power electronic loads such as Personal Computers (PCs), TVs, variable frequency drives in nearly every household. These loads are a major source of harmonics into the distribution system. Unbalanced Three-phase loads or different single-phase loads result in unbalance in the three-phase voltages and currents in the grid. Equipment damage, Loss of data, nuisance tripping, overheating of wires, relay tripping are few of the major issues caused due to power quality. Also, there is an increase in the awareness about the various power quality issues as well as its ill-effects amongst the consumers. Hence, it is essential to improve the quality of the power supplied. Active and Passive filters play a huge role in the mitigation of power quality issues. In recent years, usage of active filters has increased because tuning is possible for various harmonic elimination and active filters when combined with a storage system can provide active as well as reactive power compensation. The fast depletion of non-renewable sources of energy and its effect on the environment has shifted the focus on the usage of non-renewable energy sources such as solar energy, wind energy, etc for the production of electricity. In this research work, solar energy is combined with shunt active filter for the mitigation of various power quality issues occurring in a grid-connected non-linear and unbalanced three-phase load. The active power filter will provide reactive power compensation to the non-linear load and active power delivery for unbalanced loads such the grid current and voltage will remain balanced and the stress on the grid is reduced while meeting the load demand. A battery system is also integrated to store the excess energy that may be generated by the Photo-Voltaic(PV) array and acts as a source of energy when PV array output is low or nil. Conservative Power Theory is used for controlling the power injection into the system by the Voltage Source Converter(VSC). The proposed system is verified using MATLAB/SIMULINK.

Optimal SuDoKu Reconfiguration Technique for Total-Cross-Tied PV Array to Increase Power Output Under Non-Uniform Irradiance

Partial shading condition drastically reduces the maximum power output of photovoltaic array. Partial shading occurs due to several factors, such as flying birds, trees, and passing clouds. Many ways can be mitigated partial shading problems in photovoltaic (PV) array. One among the way is reconfiguration techniques, namely reconfigure the location of PV modules in PV array based on irradiance levels in order to distribute shading effects and increasing maximum power. This paper proposed an optimal SuDoKu reconfiguration pattern for $9\times 9$ total-cross-tied (TCT) PV array to improve maximum power under partial shading conditions. In this approach, the physical location of PV modules in TCT array are rearranged based on optimal SuDoKu style without altering the electrical connections, so that the shading effects can distribute over the array. Further, the performance of proposed pattern investigated with existing SuDoKu pattern under different shading conditions by comparing the global maximum power point, mismatch losses, fill-factor, and efficiency using MATLAB-Simulink. Based on the results of this paper, it concluded that the proposed optimal SuDoKu reconfiguration arrangement is reducing the wiring arrangement and increasing the shading distribution over the array as compared to SuDoKu arrangement.

A Novel Photovoltaic Array Outlier Cleaning Algorithm Based on Sliding Standard Deviation Mutation

There is a large number of outliers in the operation data of photovoltaic (PV) array, which is caused by array abnormalities and faults, communication issues, sensor failure, and array shutdown during PV power plant operation. The outlier will reduce the accuracy of PV system performance analysis and modeling, and make it difficult for fault diagnosis of PV power plant. The conventional data cleaning method is affected by the outlier data distribution. In order to solve the above problems, this paper presents a method for identifying PV array outliers based on sliding standard deviation mutation. Considering the PV array output characteristics under actual environmental conditions, the distribution of array outliers is analyzed. Then, an outlier identification method is established based on sliding standard deviation calculation. This method can identify outliers by analyzing the degree of dispersion of the operational data. The verification part is illustrated by case study and algorithm comparison. In the case study, multiple sets of actual operating data of different inverters are cleaned, which is selected from a large grid-connected power station. The cleaning results illustrate the availability of the algorithm. Then, the comparison against the quantile-algorithm-based outlier identification method explains the effectiveness of the proposed algorithm.

Characterization of electric faults in photovoltaic array systems

Electric faults in photovoltaic (PV) systems cause negative economic and safety impacts, reducing their performance and causing unwanted electric connections that can be dangerous for the user. Line to line, ground and open circuit faults, are three of the main faults that happen in a photovoltaic array system. This work proposes a characterization of the equivalent circuits and the voltage-power (VP) curves at the output of multiple PV arrays under different topological configurations and fault conditions to evaluate the effects of these three main faults on the performance of a photovoltaic array system, taking into account the temperature and solar radiation influence. This work presents a validation of the characterization by measuring the output VP curves of a low-power photovoltaic array system under real outdoors conditions. This method can be useful in future works to develop low cost systems capable of detecting and classifying electric faults in photovoltaic array systems.

Probability modeling for PV array output interval and its application in fault diagnosis

It is crucial to detect fault arrays timely to ensure the safe and economic operation of large-scale photovoltaic (PV) power plants. The inconsistencies among different arrays and the random fluctuations of PV output result in uncertainties of the PV array output, and the deterministic fault diagnosis methods decrease the accuracy of PV fault diagnosis methods. In this paper, a probability modeling approach for a PV array electrical parameter distribution, which can effectively solve the problems of the nonlinearity and uncertainty of the PV array output interval, is proposed. Actual PV plant data are utilized to calculate fault indicators and analyze the uncertainty of fault indicator distributions. The t-location scale distribution function is used to fit the fault indicator distributions and to establish a probability model of PV fault indicators in different irradiance ranges. Finally, fault indicator thresholds with different degrees of confidence are obtained to complete the PV array fault diagnosis. The setting of the indicator threshold for PV arrays is based on measured data rather than experience, and it can effectively detect different types of faults. The effectiveness of the proposed method is verified in a real PV plant.

Shaping photovoltaic array output to align with changing wholesale electricity price profiles

Large-scale deployment of solar photovoltaics (PV) contributes to the occurrence of depressed—and sometimes negative—electricity prices during daylight hours as PV displaces higher-cost generators in the merit-order dispatch stack. These changes in electricity price provide an opportunity to increase the wholesale energy revenue of PV generators through temporal shaping of PV output. Here, we explore the impact of three output-shaping strategies on PV wholesale energy revenue and capacity factor: utilization of 1-axis tracking, curtailment during negative-price hours, and modification of fixed-tilt array orientation. Utility-scale PV arrays are modeled at more than 10,000 pricing nodes across six United States electricity markets over the 2010–2017 time period. Large changes in revenue-optimized output profiles are observed for the California system, where solar capacity penetration has increased from ∼2% of peak load in 2010 to ∼28% of peak load in 2017, and the wholesale revenue benefits of temporal output shaping are increasing with time. On the California real-time market in 2017, compared to capacity-factor-optimized fixed-tilt arrays with must-run operation, curtailment increases revenues by 9%, curtailment in conjunction with fixed-tilt orientation optimization increases revenues by 20%, 1-axis tracking without curtailment increases revenues by 32%, and 1-axis tracking with curtailment increases revenues by 42% for the median node. Median optimal fixed-tilt azimuths for PV on the real-time market in California have increased from 192° in 2010 to 235° (i.e. 55° west of south) in 2017. Among the markets and years studied, the California market in 2017 demonstrates the largest potential benefit from temporal output shaping. These results highlight mechanisms for mitigating some of the decline in PV wholesale value at high solar penetrations, and illustrate the importance of adapting PV installation and dispatch strategies to changing power system conditions.

Diagnosis and mitigation of voltage and current sensors malfunctioning in a grid connected PV system

Accuracy of sensors measuring Photovoltaic (PV) array output voltage, current and the ac currents flowing between VSC and grid plays an indispensable role in efficient operation of a grid connected PV system. Erroneous measurements due to malfunctioning of aforementioned sensors can cause significant disruptions in the operation of a PV system, as the impact of erroneous measurements propagate through the controllers in a PV system. In this paper, malfunctioning of PV system sensors are regarded as sensor faults. This paper presents an approach for diagnosis and mitigation of sensor faults in a PV system. The fault diagnosis approach is based on the sliding mode observer (SMO)-based fault detection and identification theory, which is capable of accurately estimating faults in sensor measurements. Estimated faults are used by the fault mitigation technique in the proposed approach to rectify the sensor measurements. The rectified sensor measurements are used by the controllers in PV system, instead of possibly erroneous sensor measurements, which ensure fault resilient operation of the PV system. The efficacy of the proposed approach has been validated through rigorous simulation and experimental studies.

Experimental Investigation on a New Hybrid System Employing Wind-Driven DFIG and Solar PV Panels

This paper investigates the operation of wind-driven doubly fed induction generator (DFIG) with solar photovoltaic (PV) system for supplying isolated loads. A simple control strategy has been developed to maintain a set voltage and frequency at the DFIG stator terminals which is envisaged as a virtual grid for supplying loads. This is achieved by effective closed-loop control of a PV-fed sinusoidal pulse width modulation (SPWM) inverter connected at the rotor terminals of DFIG. The DC link of the SPWM inverter is made as a stiff one by connecting a battery bank, which acts as an energy storage device as well as a contingency source when output of PV array is less. A boost converter is interfaced between solar PV panels and battery to enable maximum power extraction from PV panels. A single sensor-based maximum power point tracking (MPPT) algorithm has been developed for solar PV system. The MPPT algorithm monitors only the output current of the boost converter, as the output voltage of boost converter is held at a constant value by the battery bank. In order to demonstrate the successful working of the proposed hybrid system, a prototype has been fabricated in the laboratory and tested under different operating conditions.

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

Improvements in Performance Analysis of Photovoltaic Systems: Array Power Monitoring in Pulse Width Modulation Charge Controllers

Various challenges should be considered when measuring photovoltaic array power and energy in pulse width modulation (PWM) charge controllers. These controllers are frequently used not only in stand-alone photovoltaic (SAPV) systems, but may also be found in photovoltaic (PV) self-consumption systems with battery storage connected to the electricity grid. An acceptable solution may be reached using expensive data acquisition systems (DASs), although this could be generally disproportionate to the relatively low cost of SAPV systems. Therefore, the aim of this paper is to develop new and effective monitoring techniques which will provide the PV array direct current (DC), output power (PA,dc), and PV array DC output energy (EA), thus avoiding the use of sophisticated DASs and providing high accuracy for the calculated parameters. Only transducers and electronic circuits that provide the average and true rms values of the PWM signals are needed. The estimation of these parameters through the aforementioned techniques showed high accuracy for both series and shunt PWM battery charge controllers. Normalized root mean square error (NRMSE) was lower than 2.4%, normalized mean bias error (NMBE) was between −1.5% and 1.1%, and mean absolute percentage error (MAPE) was within 1.6%.