The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.



This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.


The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.


1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)):           MPPT_IncCond=Simulink.Variant('MPPT_MODE==1')           MPPT_PandO=Simulink.Variant('MPPT_MODE==2')           MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3')           MPPT_MODE=1           Without_FAULT=Simulink.Variant('FAULT_MODE==1')           Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2')           Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3')           Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4')           Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5')           Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6')           FAULT_MODE=1 3. For more details about the Variant Subsystems see the Matlab Documentation Center: or


The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.


Conventianlly, state estimation is run on reduced bus-branch (BB) network models. On the other hand, when detailed node-breaker (NB) models are used, no network reduction is necessary. This allows joint estimation of system states and network topology. For testing SE applications using NB models, an expanded version of the IEEE 300-bus system is created. The following changes are made to the original BB model to generate the NB model:


It contains all the optimal operating condition data from case 1 to case 8 used to verify the optimal linear model. The file name is “DataSource.xlsx”.


It contains all the optimal operating condition data from case 1 to case 8 used to verify the optimal linear model. The file name is “DataSource.xls”.


This data set encompasses the optimal power flow and maximum loadability results related to the paper entitled “Sequential Convex Programming for Tightening Second-Order Cone AC Power Flow Relaxations”.


The Excel file has three sheets:

  1. OPF Typical: The optimal power flow results of 34 test cases in typical operating conditions
  2. OPF Congested: The optimal power flow results of 34 cases in congested operating conditions
  3. ML: The maximum loadability of 19 test cases (typical operating conditions)

The solution results of each problem instance are provided in 44 consecutive rows, each corresponding to a specified model. Computation times, objective values, and optimality gaps are reported for both second-order cone programming (SOCP) and sequential SOCP (SSOCP).


The datasets consist of operational data and detailed information of three inverter transformers in a 3.275 MW PV plant in the outskirt of Brisbane, Australia. The data includes load current, top-oil temperature, moisture in top oil, ambient temperature, solar irradiance and individual current harmonics (up to 31st order). The time interval of the data is either 1 minute or 3 seconds (dependent on the data type). The data can be used to study the ageing of inverter transformers in this PV plant. 


This animation is a supplement to the article 'A new approach to the PWM modulation for the double, square–type, conventional matrix converters supplying loads with open-end winding.' This is an animation of the four basic modulation schemes of PWM modulation for the Conventional Matrix Converter using the rotated vector concept based on the Hilbert transform, which can be performed by DSOGI filter.


Copy three files: arrow3.m, IeeeDataPort_1_AuthorInfo.m, and IeeeDataPort_2_CMC5x5x2_animation_R2020b.m into Your workspace. Next run the IeeeDataPort_2_CMC5x5x2_animation_R2020b.m. It is recommended to read the original paper when available.


Files and complement the publication under the title "A Direct Modulation for Matrix Converters based on the One–cycle Atomic operation developed in Verilog HDL", which is under review process.


Unzip files. There are two sets: for Matlab and Modelsim. Both are presenting the idea of a modulator based on the analytic signal concept. The file "DAV_PWM.m" is an animation of the conventional matrix converter, while the content of the second zip allows for HDL simulation of the matrix converter in Modelsim from Intel FPGA company.


Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.


Contains acoustic measurement data from 180 multilayer ceramic capacitors (2220 case size, 22 uF, 24V), soldered onto two test circuit boards. The measurements were performed by placing a piezoelectric point contact sensor on top of each capacitor, and subjecting the MLCC to a voltage frequency sweep from 100 Hz to 2 MHz over a duration of 100 ms. The resulting acoustic waveforms have been denoised, bandpass filtered, and downsampled. Furthermore, instantaneous phase response was calculated for each MLCC.

The dataset contains measurements from both intact and mechanically damaged components for quality assurance purposes (classification task). The acoustic signature of each MLCC is represented by an eight-dimensional feature vectror in the file inputs.mat:

  1. Acoustic emission amplitude at the highest resonace peak
  2. Frequency of the highest resonance peak
  3. Amplitude of the second-highest resonance peak
  4. Frequency of the second-highest resonance peak
  5. Total phase shift during frequency sweep
  6. Median amplitude of 10 of the highest resonance peaks
  7. Median frequency of 10 of the highest resonance peaks
  8. Mean group delay ripple calculated from the phase response of each component

The labels (0=no damage; 1=damage) for each component are found in targets.mat. Note that the labelling process was done by cross-sectioning each component and inspecting the sample visually under a microscope. Therefore, the labels may not be completely accurate, as the signs of damage can be difficult to observe.