# A Novel Supervisory Control Algorithm to Improve the Performance of a Real-Time PV Power-Hardware-In-Loop Simulator with Non-RTDS

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Mathematical Properties of a PV System for the PHIL Simulator

#### 2.1. Characteristics of PV Cells for the PHIL Simulator

_{0}varies depending on the type of semiconductor material and temperature. In this research, however, a constant value was used under the assumption of STC.

#### 2.2. Characteristics of PV Modules for the PHIL Simulator

## 3. Advanced Operation Algorithm of a PV System for the PHIL Simulator

#### 3.1. Conventional PV Simulator Operation Algorithm Used in RTDS

_{Params}, irradiation I

_{r}(k), temperature T

_{e}(k), measured voltage V

_{m}(k), and measured current I

_{m}(k) into the PV array model. This algorithm is integrated into a Real-time Digital Simulator (RTDS), which has high performance computing units with special peripheral devices capable of real-time processing and is conducive to certain test scenarios in an RTDS simulation [14,15,16,17].

#### 3.2. Proposed Supervisory Control Algorithm for the PV PHIL Simulator with Non-RTDS

_{oc}(k) and I

_{sc}(k) are calculated from F

_{th}(I

_{r}(k) and T

_{e}(k)), as shown in Equations (11) and (12).

_{b}(k) and I

_{b}(k) are calculated from the function of F

_{Bias}(I

_{r}, T

_{e}, V

_{oc}, I

_{sc}). Finally, the PV array model results are considered along with V

_{mod}(k), and the reference value (V

_{cmd}(k), I

_{cmd}(k)) that will be transmitted to the DC power supply is determined, as shown in Figure 5. The main blocks responsible for the DC power supply control, PV model, measurement devices, communication, and operation control are operated based on the multi-rate system as S

^{1}(k), S

^{2}(k), S

^{3}(k), S

^{4}(k), and S

^{5}(k), respectively.

_{oc}and maximum current I

_{sc}values according to the current input values of the environment variables. The environmental data I

_{r}(k) and T

_{e}(k) for the current step k and the PV system parameters are input, and Equations (11) and (12) are used to calculate the values of V

_{oc}(k) and I

_{sc}(k). After the DC power supply state is verified, the measurement voltage V

_{m}(k) and current I

_{m}(k) values being currently output are acquired from the isolated measurement device. The data are used to calculate V

_{mod}(k) and I

_{mod}(k) using the mathematical model of the PV module. The measured output current value and reference current value are compared, and the DC power supply mode is verified. Depending on the mode (CV or CC), the values and $\mathrm{min}\left(A,B\right)$ functions calculated in the above step are used to output V

_{out}(k) and I

_{out}(k). The V

_{cmd}(k) and I

_{cmd}(k) values can be found through rate limitation and saturation by considering the electrical properties of the PV system and peripheral devices. Figure 6 explains the algorithm in detail.

_{s}, and the results of the operating cycle of each device (t

_{DC}, t

_{MC}, t

_{MU}, and t

_{CU}) are obtained. Finally, the above results are used to generate the operating signs of S

^{1–5}through the pulse generator function PulGen, as shown in Equation (14). The cycle of the main block that works in connection with the peripheral devices operates at multiple rates of S

^{1}(k), S

^{2}(k), S

^{3}(k), S

^{4}(k), and S

^{5}(k). The DC power supply transmits and performs the final reference values of voltage V

_{cmd}(k) and current I

_{cmd}(k).

## 4. Implementation of the PV PHIL Simulator

#### 4.1. Architecture of the Proposed PV PHIL Simulator

#### 4.2. Integration and Conversion to Real-Time Processing

**Step 1.**- PV PHIL Simulator initialization procedure: Check the system’s parameters, Universal Asynchronous Receiver Transmitter (UART) and TCP/IP communication check, Initialization to the input and output;
**Step 2.**- Pre-start up procedure: Initial stage to DC power supply and Enable the output power;
**Step 3.**- Check the state of each component: Communication check and current state of DC power supply, isolated measurement device, and other peripheral devices;
**Step 4.**- Data processing: Acquisition of the measurement data, calibration process, and visualization to the graphical user interface (GUI);
**Step 5.**- Supervisory control algorithm: Computing the mathematical model of the PV system with environmental variables and measured data;
**Step 6.**- Execution process: Send the set values to the DC power supply and check for faults;
**Step 7.**- Repeat Step 3–Step 6 until the end of the test.

## 5. Performance Evaluation of the PV System for the PHIL Simulator

#### 5.1. PV Array Simulation and Test Conditions

^{2}while the temperature varied from 0 °C to 100 °C in 25 °C increments, and the temperature was fixed to 25 °C while the irradiation was increased from 200 W/m

^{2}to 1000 W/m

^{2}in 200 W/m

^{2}increments.

^{2}and 25 °C. For the current–voltage graph considering the PV array’s irradiation and temperature, the operating point was defined with the MPPT control algorithm for the connected inverter. The PV PHIL simulator’s operating characteristics and performance were compared and analyzed. Table 3 and Table 4 present the detailed specifications of the DC power supply and inverter, respectively.

#### 5.2. Simulation Analysis of Performance Characteristics

^{2}to 1000 W/m

^{2}.

^{2}and the temperature was incrementally increased from 0 °C to 100 °C. Therefore, applying the proposed mathematical model to the PV-PHIL simulator was proven to provide the same characteristics as the operation of an actual PV system.

#### 5.3. Experiment Analysis of Performance Characteristics

^{2}and a temperature of 25 °C. No shadow is assumed to appear on any PV module. As shown in Figure 13, with the conventional algorithm of the PV PHIL simulator, the PV array maintained a maximum output voltage V

_{oc}state before being connected to the inverter system. However, after the connection of the PV inverter to the grid, the voltage and current at the maximum power follow-up point varied continuously and irregularly.

_{sc}, the I-V curve of the simulation was not followed, and the current–voltage output of the DC power supply was in the transient state. Therefore, in the inverter, the input power varied greatly over time, MPPT control became unavailable, and the efficiency and performance decreased. In conclusion, when the performance of the PV inverter and the MPPT algorithm was evaluated with the PV PHIL simulator using the conventional algorithm, the same characteristics as the actual PV could not be simulated, so a precise evaluation could not be performed.

_{oc}state as in the case of the conventional algorithm. After connection to the system, the maximum power point tracking was followed. The DC power supply’s output current for the simulated PV array increased with the same pattern as the simulation described in the previous section, as shown in Figure 14. Therefore, with the proposed algorithm, the same characteristics as the actual PV were precisely simulated. Thus, the performance could be successfully evaluated.

## 6. Conclusions

- (1)
- The conventional PV algorithm, which is used in RTDS equipment, was applied to the PV PHIL simulator proposed in this study, but the output is in a transient state. However, the proposed algorithm confirmed a stable output state with a grid-tied PV inverter. In addition, the grid-tied PV inverter was able to perform MPPT control in the PV PHIL simulator with the proposed algorithm.
- (2)
- A real-time operating program, which is applied to the proposed algorithm, operating control logic, and API functions of peripheral devices, was developed and it verified the improved performance of the PV PHILS by means of a general Computing Unit, a DC power supply, and the peripherals.
- (3)
- With the spreading use of distributed PV power, such as household PVs and modular PV containers for isolated areas, the PV PHIL simulator can be used to increase the performance, efficiency, and safety of PV inverters and thus increase their competitiveness.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Hung, D.Q.; Dong, Z.Y.; Trinh, H. Determining the size of PHEV charging stations powered by commercial grid-integrated PV systems considering reactive power support. Appl. Energy
**2016**, 183, 160–169. [Google Scholar] [CrossRef] - Ul-Haq, A.; Cecati, C.; Al-Ammar, E.A. Modeling of a Photovoltaic-Powered Electric Vehicle Charging Station with Vehicle-to-Grid Implementation. Energies
**2017**, 10, 4. [Google Scholar] [CrossRef] - Khana, O.; Xiaob, W. Review and qualitative analysis of submodule-level distributed power electronic solutions in PV power systems. Renew. Sustain. Energy Rev.
**2017**, 76, 516–528. [Google Scholar] [CrossRef] - Zhang, Q.; Tezuka, T.; Ishihara, K.N.; Mclellan, B.C. Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai Area, Japan. Renew. Energy
**2012**, 44, 99–108. [Google Scholar] [CrossRef] - Fathabadi, H. Novel solar powered electric vehicle charging station with the capability of vehicle-to-grid. Sol. Energy
**2017**, 142, 136–143. [Google Scholar] [CrossRef] - Locment, F.; Sechilariu, M.; Forgez, C. Electric Vehicle Charging System with PV Grid-connected Configuration. In Proceedings of the IEEE Vehicle Power and Propulsion Conference (VPPC), Lille, France, 1–3 September 2010. [Google Scholar]
- Birnie, D.P., III. Solar-to-Vehicle (S2V) Systems for Powering Commuters of the Future. J. Power Sources
**2009**, 186, 539–542. [Google Scholar] [CrossRef] - Erickson, L.E.; Robinson, J.; Brase, G.; Cutsor, J. Solar Powered Charging Infrastructure for Electric Vehicles: A Sustainable Development; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Xiao, B.; Hang, L.; Mei, J. Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid-Connected Applications. IEEE Trans. Ind. Appl.
**2015**, 51, 1722–1731. [Google Scholar] [CrossRef] - Moon, S.; Yoon, S.G.; Park, J.H. A New Low-Cost Centralized MPPT Controller System for Multiply Distributed Photovoltaic Power Conditioning Modules. IEEE Trans. Smart Grid
**2015**, 6, 2649–2658. [Google Scholar] [CrossRef] - Karbakhsh, F.; Amiri, M.; Zarchi, H.A. Two-switch flyback inverter employing a current sensorless MPPT and scalar control for low cost solar powered pumps. IET Renew. Power Gener.
**2017**, 11, 669–677. [Google Scholar] [CrossRef] - Wang, Y.; Yu, X. Comparison study of MPPT control strategies for double-stage PV grid-connected inverter. In Proceedings of the IECON 2013 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, 10–13 November 2013; pp. 1561–1565. [Google Scholar]
- Rout, A.; Samantara, S.; Dash, G.K. Modeling and simulation of hybrid MPPT based standalone PV system with upgraded multilevel inverter. In Proceedings of the 2014 Annual IEEE India Conference (INDICON), Pune, India, 11–13 December 2014; pp. 1–6. [Google Scholar]
- Nzimako, O.; Wierckx, R. Modeling and Simulation of a Grid-Integrated Photovoltaic System Using a Real-Time Digital Simulator. IEEE Trans. Ind. Appl.
**2016**, 53, 1326–1336. [Google Scholar] [CrossRef] - Rezkallah, M.; Hamadi, A.; Chandra, A.; Singh, B. Real-Time HIL Implementation of Sliding Mode Control for Standalone System Based on PV Array Without Using Dumpload. IEEE Trans. Sustain. Energy
**2015**, 6, 1389–1398. [Google Scholar] [CrossRef] - Zhou, Y.; Li, H.; Liu, L. Integrated Autonomous Voltage Regulation and Islanding Detection for High Penetration PV Applications. IEEE Trans. Power Electr.
**2012**, 28, 2826–2841. [Google Scholar] [CrossRef] - Pinheiro, G.G.; de Carvalho Filho, J.M.; Bonatto, B.D. Modeling, simulation and comparison analysis of an installed photovoltaic system using RTDS. In Proceedings of the 2016 12th IEEE International Conference on Industry Applications (INDUSCON), Curitiba, Brazil, 20–23 November 2016; pp. 1–8. [Google Scholar]
- Khazaei, J.; Piyasinghe, L.; Miao, Z. Real-time digital simulation modeling of single-phase PV in RT-LAB. In Proceedings of the 2014 IEEE PES General Meeting | Conference & Exposition, National Harbor, MD, USA, 27–31 July 2014; pp. 1–5. [Google Scholar]
- Mai, X.H.; Kwak, S.K.; Jung, J.H. Comprehensive Electric-Thermal Photovoltaic Modeling for Power-Hardware-in-the-Loop Simulation (PHILS) Applications. IEEE Trans. Ind. Electr.
**2017**, 64, 6255–6264. [Google Scholar] [CrossRef] - Mather, B.A.; Kromer, M.A.; Casey, L. Advanced photovoltaic inverter functionality verification using 500kw power hardware-in-loop (PHIL) complete system laboratory testing. In Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 24–27 February 2013. [Google Scholar]
- Faranda, R.; Leva, S.; Maugeri, V. MPPT techniques for PV systems: Energetic and cost comparison. In Proceedings of the 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008. [Google Scholar]
- Desoto, W.; Klein, S.; Beckman, W. Improvement and validation of a model for photovoltaic array performance. Sol. Energy
**2006**, 80, 78–88. [Google Scholar] [CrossRef] - Kim, D.J.; Kim, B.K.; Ryu, K.S.; Lee, G.S.; Jang, M.S.; Ko, H.S. Development of PV-power-hardware-in-loop simulator with realtime to improve the performance of the distributed PV inverter. J. Korean Sol. Energy Soc.
**2017**, 37, 47–59. [Google Scholar] [CrossRef] - SOLAREX MSX-60 and MSX-64 Solar Panel Datasheet. Available online: https://www.solarelectricsu pply.com/media/custom/upload/Solarex-MSX64.pdf (accessed on 10 September 2017).

**Figure 4.**Conventional PV operation algorithm with a Real-time Digital Simulator (RTDS). DC: direct current.

**Figure 7.**Architecture and data interface of the proposed PV Power Hardware-In-the-Loop (PHIL) simulator. USB: universal serial bus.

**Figure 13.**Experiment result with the conventional operation algorithm of the PV PHIL Simulator (PHILS). (

**a**) I-V Curve characteristic graph of the PV System (

**b**) DC power supply voltage output with time series (

**c**) DC power supply current output with time series.

**Figure 14.**Experiment result with proposed advanced operation algorithm of PV PHILS. (

**a**) I-V Curve characteristic graph of the PV System (

**b**) DC power supply voltage output with time series (

**c**) DC power supply current output with time series.

Function Block | Initial Stage (IS) | System Test (ST) | Normal Operation (NP) | Normal Stop (NS) |
---|---|---|---|---|

DC power supply (DC) | ${w}_{DC}^{IS}$ | ${w}_{DC}^{ST}$ | ${w}_{DC}^{NP}$ | ${w}_{DC}^{NS}$ |

Main computing unit (MC) | ${w}_{MC}^{IS}$ | ${w}_{MC}^{ST}$ | ${w}_{MC}^{NP}$ | ${w}_{MC}^{NS}$ |

Measurement unit (MU) | ${w}_{MU}^{IS}$ | ${w}_{MU}^{ST}$ | ${w}_{MU}^{NP}$ | ${w}_{MU}^{NS}$ |

Communication unit (CU) | ${w}_{CU}^{IS}$ | ${w}_{CU}^{ST}$ | ${w}_{CU}^{NP}$ | ${w}_{CU}^{NS}$ |

Category | Value | Unit |
---|---|---|

Model | MSX-60 | - |

Cell type | Polycrystalline silicon | - |

Maximum power (P_{max}) | 60 | W |

Voltage at P_{max} (V_{mp}) | 17.1 | V |

Current at P_{max} (I_{mp}) | 3.5 | A |

Open-circuit voltage (V_{oc}) | 21.1 | V |

Short-circuit current (I_{sc}) | 3.8 | A |

Diode quality factor | 1.2 | - |

PV diode band-gap energy | 1.124 | eV |

Number of series cells | 36 | - |

Number of parallel cells | 1 | - |

Number of parallel modules | 12 | - |

Number of parallel modules | 1 | - |

Category | Value | Unit |
---|---|---|

Output rating voltage | 0–315 | V |

Output rating current | 0–8.4 | A |

Output power | 2600 | W |

Programming accuracy | 0.1% + 450.0 mV | - |

Ripple and noise (20 Hz–20 MHz) | ≤25 mVrms | - |

AC input rating | Single phase 220 V ± 10% 50–60 Hz | - |

Category | Value | Unit |
---|---|---|

Manufacturer | DASSTECH | - |

Model | DSP-123K2 | - |

Max. DC power | 3300 | W |

PV voltage range MPPT | 110–450 | V |

Max. input current | 15 | A |

Nominal AC output | 3000 | W |

AC voltage output | 220–240 | V |

AC connection | Single phase | - |

Max. efficiency | 96.7 | % |

Category | Value | Unit |
---|---|---|

Performance Index of Conventional Algorithm | 75.4 | - |

Performance Index of Proposed Algorithm | 29.9 | - |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kim, D.-J.; Kim, B.; Ko, H.-S.; Jang, M.-S.; Ryu, K.-S. A Novel Supervisory Control Algorithm to Improve the Performance of a Real-Time PV Power-Hardware-In-Loop Simulator with Non-RTDS. *Energies* **2017**, *10*, 1651.
https://doi.org/10.3390/en10101651

**AMA Style**

Kim D-J, Kim B, Ko H-S, Jang M-S, Ryu K-S. A Novel Supervisory Control Algorithm to Improve the Performance of a Real-Time PV Power-Hardware-In-Loop Simulator with Non-RTDS. *Energies*. 2017; 10(10):1651.
https://doi.org/10.3390/en10101651

**Chicago/Turabian Style**

Kim, Dae-Jin, Byungki Kim, Hee-Sang Ko, Moon-Seok Jang, and Kyung-Sang Ryu. 2017. "A Novel Supervisory Control Algorithm to Improve the Performance of a Real-Time PV Power-Hardware-In-Loop Simulator with Non-RTDS" *Energies* 10, no. 10: 1651.
https://doi.org/10.3390/en10101651