# A Review of Control Techniques in Photovoltaic Systems

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## Abstract

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## 1. Introduction

## 2. First Level Controllers

#### 2.1. Current and Voltage Control

#### 2.2. Maximum Power Point Tracking Methods

#### 2.2.1. Improving the Performance of Classical Techniques

- A new control algorithm with a multi-variable P&O is presented in [25]. It is a hybrid multivariable control that combines central and distributed MPPT to extend the MPPT range.
- To eliminate steady state oscillations in P&O and incremental conductance algorithms, ref. [26] introduces improved system operation.
- The maximum power trapezium (MPT) method is considered as a classic MPPT [27]. In this paper, a modified MPT is proposed. The new algorithm introduces a variable current range lower bound to substitute fixed voltage range upper bound of the traditional MPT method.

#### 2.2.2. Partial Shading Condition

- Ref. [28] presents a novel maximum point searching design utilizing a maximum power point scanning technique is proposed. This technique is developed into an online or off-line tester and finds out the maximum power point automatically.
- A smart technique is presented in [31] to systematically schedule the search for the global peak, by using the maximum power triangle method.
- Ref. [32] shows a scheme is focusing on the disturbances of random variables by using a flower pollination algorithm.
- An optimization method in a MPPT algorithm is proposed in [33]. The technique named chaotic flower pollination algorithm integrates the chaos maps for an adaptive adjustment of the basic algorithm parameters.
- An improved MPPT control by using a fusion firefly algorithm is presented in [34]. Additionally a novel simplified propagation process is considered.
- A fuzzy logic MPPT optimized by a combination of PSO and GA is presented in [35]. The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem solved by using PSO-GA.
- Ref. [36] poses an improved gray wolf optimizer. In this nature-inspired algorithm a convergence factor is integrated to improve the dynamic performance.

#### 2.3. Synchronization

- A PLL based on a decoupled double synchronous reference frame is presented in [37]. This structure is suitable for unbalanced grid and variable frequency conditions.
- Ref. [38] proposes a self adaptive controller to operate in both grid connected and islanding condition, with sure transfer between modes without reconfiguring control structure. The controller is designed on the basis of a PLL and two cascaded control loops.
- A SOGI based PLL technique that uses two interdependent loops one for frequency and the other one during the synchronization process is presented in [39].
- Ref. [40] proposes a PLL with second order approximation valid for steady state and transients. Compared with other PLLs, it is more accurate during large phase perturbation by cause of grid faults.
- A PLL based on a dual second order generalized integrator (SOGI) enhanced is presented in [42]. The algorithm realizes a harmonics cancellation before performing sequence calculations. Its application is weak grids.
- A novel PLL with an improved dual adaptive notch and multivariable filter is presented in [43] for unideal grid conditions.
- Ref. [44] proposes an extra function on the basis of direct phase-angle detection method to support asymmetrical grids.
- A novel grid synchronization technique with bumpless start is proposed in [45]. The method reduces the computational effort and can operate in an unbalanced and distorted weak grid.

## 3. Second Level Controllers

#### 3.1. Power Quality

#### 3.1.1. Harmonic Detection in the Load

- In [46] harmonic components are obtained in synchronous rotating DQ frame, as a subtraction between instantaneous current and fundamental components.
- The unbalanced output power problem in single-phase cascaded H-bridge PV inverter is studied in [48,49]. This condition results in a higher harmonic content of the grid current. In [48], a novel harmonic compensation technique is proposed. In this strategy, harmonic components are obtained from DC-link average voltage calculated by means of a notch filter. In [49], multiples harmonics are injected in overmodulation and non-overmodulation regions, to extend the linear modulation range and compensate grid current harmonics.

#### 3.1.2. Selective Harmonic Compensation

- Methods based on the traditional DFT are used to detect the load current harmonic content [50,51]. A sliding DFT is applied in a dynamic current saturation algorithm. Sliding DFT provides high computational efficiency in comparison with traditional algorithm [50]. An enhanced DFT is proposed. The controller provides a feedback for each harmonic being able to compensate different harmonics [51].
- A flexible method of selective compensation based on instantaneous power theory is presented in [52]. Compensation current is calculated according to THD index and power factor, injecting to grid active component or reactive component or both.
- A new technique to compensate second order harmonic component is proposed in [53]. This technique is based on cascaded LPFs and synchronous rotating DQ frame.
- Ref. [54] considers the current saturation problem and the compensation of the extra harmonics generated in this process. Two saturation techniques are proposed. Harmonic current components are detected applying SOGI based method.

#### 3.2. Anti-Islanding Protection

#### 3.2.1. Active Techniques

- A method based on reactive power perturbation is presented in [60]. This islanding detection method poses a reactive power P&O anti-islanding method for indirect current control. The proposed algorithm introduces a small reactive power disturbance in the inverter output and detects the islanding by observing reactive power mismatch during the islanding condition.
- An approach based on the periodical injection of a second order harmonic current component and evaluates grid response through a new cross-correlation anti-islanding detection is proposed in [61]. This approach is focused in module integrated converters with pseudo dc-link.
- Ref. [62] proposes a hybrid method for islanding detection. The proposed scheme injects a low frequency sinusoidal perturbation signal into the d-axis current control loop.
- A comparative analysis of active anti-islanding techniques based on the frequency drift is presented in [63]. These techniques are the classic active frequency drift (AFD), AFD with pulsating chopping factor and AFD with positive feedback.
- An hybrid islanding detection strategy that exploits Gibbs phenomenon on the interpolation of two voltage sinusoidal functions is described in [64]. The proposed technique combines active and passive methods of frequency rate of change at a given moment while the voltage THD is monitored.

#### 3.2.2. Passive Techniques

- A detection scheme based on support vector machine is presented in [57]. This method exploits powerful classification capability. Algorithm collects measures of current, voltage, power, frequency and THD.
- Ref. [58] proposes a scheme based on the detection of voltages and frequencies higher and lower than the admissible values. This method reduces the non-detection zone of passive islanding techniques.
- A passive method with an adaptive algorithm is presented in [59]. This paper proposes a new islanding detection strategy based on the combination of an adaptive neuro-fuzzy inference system (ANFIS) approach and passive monitoring techniques of system variables. The method exploits the pattern recognition of ANFIS approach to detect the islanding condition.

#### 3.3. Grid Support

#### 3.3.1. Frequency Support

#### 3.3.2. Voltage Support

## 4. Third Level Controllers

#### 4.1. Active Power Limiting

#### 4.2. Energy Storage Systems

#### 4.3. Photovoltaic Monitoring

#### 4.4. Power Forecasting

- Two artificial intelligence techniques are proposed in [87]: auto-regressive integrated moving average model with an ANN model considering weighing factors computed periodically by means of least squares method.
- Ref. [88] analyzes the performance of different machine learning models that predict the PV power generation. The forecasting models are developed by using historic data of PV power and weather predictions.
- A model uses historic PV generation and weather data is presented in [90]. A Bayesian network performs data inference. The approach also incorporates spatial similarity and temporal correlation to support the power prediction.
- A novel solar generation forecasting proposal based on exploring weather factors from PV model is presented in [91]. The method is performed at three stages: PV systems modeling, machine learning methods for mapping weather features with solar power and forecast adjustment.
- In [92] PV generation estimation is achieved by using numerical weather prediction (NWP). Historical data is processing in NWP products.
- A PV output forecast based on weather prediction is presented in [93]. K-means clustering algorithm is employed to classify historical generation data and the correlation analysis method reduces the dimension of the inputs. Prediction model is solved by considering the long-short memory neural network combined with attention mechanism.
- In [94] a forecasting method based on the ANFIS approach is presented to optimize peak load reduction. The forecasted results are used to calculate the BESS capacity and a FLC considering BESS capacity and PV power determines optimal BESS usage for the sake of power peak curtailment.

## 5. Discussion

#### 5.1. Identified Findings

#### 5.2. Other Review Papers

## 6. Potential Challenges

- Control techniques with a trade-off between simplicity and effectiveness.
- Optimal integration of controllers.
- Control algorithms with the potential to perform functions in more than one control level (multi-function and multilevel controllers).
- Specialized software of reasonable cost with self-learning ability.
- Secure and reliable communications.
- Processing of high data volumes.
- Hardware with greater computing power and fast time response.
- Adaptive and smart protection systems.
- Control and communication architectures.
- Longer component life spans and lower costs.
- Optimal energy management.

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AFD | Active Frequency Drift |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

ANN | Artificial Neural Network |

DFT | Discrete Fourier Transform |

ESS | Energy Storage System |

GA | Genetic Algorithm |

LPF | Low Pass Filter |

ML | Machine Learning |

MPPT | Maximum Power Point Tracking |

MPT | Maximum Power Trapezium |

NWP | Numerical Weather Prediction |

PCC | Point of Common Coupling |

PI | Proportional Integral |

PLL | Phase Locked Loop |

PSO | Particle Swarm Optimization |

PV | Photovoltaic |

P&O | Perturb & Observe |

SVM | Support Vector Machine |

SOGI | Second Order Generalized Integrator |

THD | Total Harmonic Distortion |

## References

- Ullah, S.; Branquinho, R.; Mateus, T.; Martins, R.; Fortunato, E.; Rasheed, T.; Sher, F. Solution Combustion Synthesis of Transparent Conducting Thin Films for Sustainable Photovoltaic Applications. Sustainability
**2020**, 12, 10423. [Google Scholar] [CrossRef] - Almutairi, A.; Abo-Khalil, A.; Sayed, K.; Albagami, N. MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions. Sustainability
**2020**, 12, 10310. [Google Scholar] [CrossRef] - Ammar, R.B.; Ammar, M.B.; Oualha, A. Fuzzy Intelligent Management of Inter-Exchanged Energy between Standalone Photovoltaic Systems. In Proceedings of the 2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia, 24–26 March 2019; pp. 292–297. [Google Scholar]
- Schrittwieser, L.; Leibl, M.; Kolar, J.W. 99% Efficient Isolated Three-Phase Matrix-Type DAB Buck–Boost PFC Rectifier. IEEE Trans. Power Electron.
**2019**, 35, 138–157. [Google Scholar] [CrossRef] - Wang, H.; Wu, W.; Li, Y.; Blaabjerg, F. A Coupled-Inductor-Based Buck–Boost AC–DC Converter with Balanced DC Output Voltages. IEEE Trans. Power Electron.
**2019**, 34, 151–159. [Google Scholar] [CrossRef] - Bukar, A.L.; Tan, C.W. A Review on Stand-Alone Photovoltaic-Wind Energy System with Fuel Cell: System Optimization and Energy Management Strategy. J. Clean. Prod.
**2019**, 221, 73–88. [Google Scholar] [CrossRef] - Ashtiani, M.N.; Toopshekan, A.; Yousefi, H.; Maleki, A. Techno-Economic Analysis of a Grid-Connected PV/Battery System Using the Teaching-Learning-Based Optimization Algorithm. Sol. Energy
**2020**, 203, 69–82. [Google Scholar] [CrossRef] - Al-Shetwi, A.Q.; Hannan, M.; Jern, K.P.; Mansur, M.; Mahlia, T. Grid-Connected Renewable Energy Sources: Review of the Recent Integration Requirements and Control Methods. J. Clean. Prod.
**2020**, 253, 119831. [Google Scholar] [CrossRef] - Ali, Z.; Christofides, N.; Saleem, K.; Polycarpou, A.; Mehran, K. Performance Evaluation and Benchmarking of PLL Algorithms for Grid-Connected RES Applications. IET Renew. Power Gener.
**2019**, 14, 52–62. [Google Scholar] [CrossRef] - Elkholy, A. Harmonics Assessment and Mathematical Modeling of Power Quality Parameters for Low Voltage Grid Connected Photovoltaic Systems. Sol. Energy
**2019**, 183, 315–326. [Google Scholar] [CrossRef] - Sarkar, M.N.I.; Meegahapola, L.G.; Datta, M. Reactive Power Management in Renewable Rich Power Grids: A Review of Grid-Codes, Renewable Generators, Support Devices, Control Strategies and Optimization Algorithms. IEEE Access
**2018**, 6, 41458–41489. [Google Scholar] [CrossRef] - Azghandi, M.A.; Barakati, S.M. A Temporary Overvoltages Mitigation Strategy for Grid-Connected Photovoltaic Systems Based on Current-Source Inverters. Iran. J. Sci. Technol. Trans. Electr. Eng.
**2020**, 44, 1253–1262. [Google Scholar] [CrossRef] - Livera, A.; Theristis, M.; Makrides, G.; Georghiou, G.E. Recent Advances in Failure Diagnosis Techniques Based on Performance Data Analysis for Grid-Connected Photovoltaic Systems. Renew. Energy
**2019**, 133, 126–143. [Google Scholar] [CrossRef] - Chuang, M.; Hong, L. Research on Photovoltaic Grid-connected Control of Z Source Inverter Based on Active Disturbance Rejection Technology. In Proceedings of the 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, 20–22 December 2019; Volume 1, pp. 2648–2652. [Google Scholar]
- Bhagiya, R.D.; Patel, D.R.M. PWM based Double loop PI Control of a Bidirectional DC-DC Converter in a Standalone PV/Battery DC Power System. In Proceedings of the 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, 13–15 December 2019; pp. 1–4. [Google Scholar]
- Yadav, A.; Chandra, S. Single stage high boost Quasi-Z-Source inverter for off-grid photovoltaic application. In Proceedings of the 2020 International Conference on Power Electronics IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 28–29 February 2020; pp. 257–262. [Google Scholar]
- Ravada, B.R.; Tummuru, N.R. Control of a Supercapacitor/Battery/PV based Stand-Alone DC-Microgrid. IEEE Trans. Energy Convers.
**2020**, 35, 1268–1277. [Google Scholar] [CrossRef] - Shan, Y.; Hu, J.; Guerrero, J.M. A Model Predictive Power Control Method for PV and Energy Storage Systems with Voltage Support Capability. IEEE Trans. Smart Grid
**2020**, 11, 1018–1029. [Google Scholar] [CrossRef] - Rahman Habib, H.U.; Wang, S.; Elmorshedy, M.F.; Waqar, A.; Imran, R.M.; Kotb, K.M. Performance Enhancement of Power Converters for PV-Based Microgrid using Model Predictive Control. In Proceedings of the 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Kuala Lumpur, Malaysia, 24–25 July 2019; pp. 1–6. [Google Scholar]
- Arteaga, M.U.; Ruiz, A.G.; Rivera, M. Control of Energy Storage and Photovoltaic Systems using Model Predictive Control. In Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portuga, 9–11 September 2019; pp. 1–6. [Google Scholar]
- Nasir, M.; Khan, H.A.; Niazi, K.A.K.; Jin, Z.; Guerrero, J.M. Dual-loop control strategy applied to PV/battery-based islanded DC microgrids for swarm electrification of developing regions. J. Eng.
**2019**, 2019, 5298–5302. [Google Scholar] [CrossRef] - Bellinaso, L.V.; Figueira, H.H.; Basquera, M.F.; Vieira, R.P.; Gründling, H.A.; Michels, L. Cascade Control with Adaptive Voltage Controller Applied to Photovoltaic Boost Converters. IEEE Trans. Ind. Appl.
**2019**, 55, 1903–1912. [Google Scholar] [CrossRef] - Zurbriggen, I.G.; Ordonez, M. PV Energy Harvesting Under Extremely Fast Changing Irradiance: State-Plane Direct MPPT. IEEE Trans. Ind. Electron.
**2019**, 66, 1852–1861. [Google Scholar] [CrossRef] - Triki, Y.; Bechouche, A.; Seddiki, H.; Abdeslam, D.O. ADALINE Based MPPT with Indirect Control Mode for Photovoltaic Systems. In Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada, 12–14 June 2019; pp. 2183–2188. [Google Scholar]
- Erauskin, R.L.; Gonzalez, A.; Petrone, G.; Spagnuolo, G.; Gyselinck, J. Multi-Variable Perturb & Observe Algorithm for Grid-tied PV Systems with Joint Central and Distributed MPPT Configuration. IEEE Trans. Sustain. Energy
**2020**, 12, 360–367. [Google Scholar] - Bhattacharyya, S.; Patnam, D.S.K.; Samanta, S.; Mishra, S. Steady Output and Fast Tracking MPPT (SOFT MPPT) for P&O and InC Algorithms. IEEE Trans. Sustain. Energy
**2020**, 12, 293–302. [Google Scholar] - Xu, S.; Gao, Y.; Zhou, G.; Mao, G. A Global Maximum Power Point Tracking Algorithm for Photovoltaic Systems Under Partially Shaded Conditions Using Modified Maximum Power Trapezium Method. IEEE Trans. Ind. Electron.
**2020**, 68, 370–380. [Google Scholar] [CrossRef] - Lin, B.; Wang, L.; Wu, Q.H. Maximum Power Point Scanning for PV Systems Under Various Partial Shading Conditions. IEEE Trans. Sustain. Energy
**2020**, 11, 2556–2566. [Google Scholar] [CrossRef] - Mendez, E.; Ortiz, A.; Ponce, P.; Macias, I.; Balderas, D.; Molina, A. Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm. Energies
**2020**, 13, 3047. [Google Scholar] [CrossRef] - Zhang, W.; Zhou, G.; Ni, H.; Sun, Y. A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition. IEEE Access
**2019**, 7, 160091–160100. [Google Scholar] [CrossRef] - Kermadi, M.; Salam, Z.; Ahmed, J.; Berkouk, E.M. A High-Performance Global Maximum Power Point Tracker of PV System for Rapidly Changing Partial Shading Condition. IEEE Trans. Ind. Electron.
**2020**, 68, 2236–2245. [Google Scholar] [CrossRef] - Elbehairy, N.M.; Swief, R.A.; Abdin, A.M.; Abdelsalam, T.S. Maximum Power Point Tracking For a Stand Alone PV System Under Shading Conditions Using Flower Pollination Algorithm. In Proceedings of the 2019 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 17–19 December 2019; pp. 840–845. [Google Scholar]
- Yousri, D.; Babu, T.S.; Allam, D.; Ramachandaramurthy, V.K.; Etiba, M.B. A Novel Chaotic Flower Pollination Algorithm for Global Maximum Power Point Tracking for Photovoltaic System Under Partial Shading Conditions. IEEE Access
**2019**, 7, 121432–121445. [Google Scholar] [CrossRef] - Huang, Y.; Huang, M.; Ye, C. A Fusion Firefly Algorithm with Simplified Propagation for Photovoltaic MPPT under Partial Shading Conditions. IEEE Trans. Sustain. Energy
**2020**, 11, 2641–2652. [Google Scholar] [CrossRef] - Dehghani, M.; Taghipour, M.; Gharehpetian, G.B.; Abedi, M. Optimized Fuzzy Controller for MPPT of Grid-Connected PV Systems in Rapidly Changing Atmospheric Conditions. J. Mod. Power Syst. Clean Energy
**2020**, 1–8. [Google Scholar] [CrossRef] - Guo, K.; Cui, L.; Mao, M.; Zhou, L.; Zhang, Q. An Improved Gray Wolf Optimizer MPPT Algorithm for PV system with BFBIC Converter under Partial Shading. IEEE Access
**2020**, 8, 103476–103490. [Google Scholar] [CrossRef] - Kalaivani, C.; Rajambal, K. Grid Integration of Three-phase Inverter using Decoupled Double Synchronus Reference Frame PLL. In Proceedings of the 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Melmaruvathur, Chennai, India, 27–28 March 2019; pp. 221–226. [Google Scholar]
- Meng, X.; Liu, X.; He, M.; Liu, Z.; Liu, J. A Self-Adaptive Controller for Inverter with Seamless Transfer and Automatic Presynchronization Capability. IEEE Access
**2020**, 8, 105936–105949. [Google Scholar] [CrossRef] - Sahoo, A.; Mahmud, K.; Ciobotaru, M.; Ravishankar, J. Adaptive Grid Synchronization Technique for Single-phase Inverters in AC Microgrid. In Proceedings of the 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA, 29 September–3 October 2019; pp. 4441–4446. [Google Scholar]
- Zou, Z.; Rosso, R.; Liserre, M. Modeling of the Phase Detector of a Synchronous-Reference-Frame Phase-Locked Loop based on Second-Order Approximation. IEEE J. Emerg. Sel. Top. Power Electron.
**2019**, 8, 2534–2545. [Google Scholar] [CrossRef] - de Carvalho, M.M.; Medeiros, R.L.P.; Bessa, I.V.; Junior, F.A.C.; Lucas, K.E.; Vaca, D.A. Comparison of the PLL Control techniques applied in Photovoltaic System. In Proceedings of the 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC), Santos, Brazil, 1–4 December 2019; pp. 1–6. [Google Scholar]
- Li, S.; Xiao, Y.; Liang, J.N.; Meng, L. Research on phase-locked loop of photo voltaic grid-connected inverter in weak grid. In Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 19–21 June 2019; pp. 1476–1479. [Google Scholar]
- Pan, H.; Li, Z.; Wei, T. A Novel Phase-Locked Loop with Improved-Dual Adaptive Notch Filter and Multi-Variable Filter. IEEE Access
**2019**, 7, 176578–176586. [Google Scholar] [CrossRef] - Sadeque, F.; Benzaquen, J.; Adib, A.; Mirafzal, B. Direct Phase-Angle Detection for Three-Phase Inverters in Asymmetrical Power Grids. IEEE J. Emerg. Sel. Top. Power Electron.
**2020**, 1. [Google Scholar] [CrossRef] - Pérez-Estévez, D.; Doval-Gandoy, J. Grid-Tied Inverter with AC Voltage Sensorless Synchronization and Soft Start. IEEE Trans. Ind. Appl.
**2019**, 55, 4920–4933. [Google Scholar] [CrossRef] - Batool, Z.; Biricik, S.; Komurcugil, H.; Ngo, T.; Vu, T.V. Photovoltaic Supplied T-Type Three- Phase Inverter with Harmonic Current Compensation Capability. In Proceedings of the 2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE), Singapore, 25–28 November 2019; pp. 1–5. [Google Scholar]
- El Kadi, Y.A.; Lakhal, Y.; Baghli, F.Z. Compensation of the harmonic pollution by photovoltaic systems under variable solar radiation. In Proceedings of the 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco, 22–24 July 2019; pp. 1–4. [Google Scholar]
- Zhao, T.; Zhang, X.; Mao, W.; Wang, M.; Wang, F.; Wang, X.; Xu, J. Harmonic Compensation Strategy for Extending the Operating Range of Cascaded H-Bridge PV Inverter. IEEE J. Emerg. Sel. Top. Power Electron.
**2020**, 8, 1341–1350. [Google Scholar] [CrossRef] - Wang, M.; Zhang, X.; Zhao, T.; Ma, M.; Hu, Y.; Wang, F.; Wang, X. Harmonic Compensation Strategy for Single-Phase Cascaded H-Bridge PV Inverter under Unbalanced Power Conditions. IEEE Trans. Ind. Electron.
**2020**, 67, 10474–10484. [Google Scholar] [CrossRef] - De Oliveira, A.L.P.; Xavier, L.S.; Callegari, J.M.S.; Cupertino, A.F.; Mendes, V.F.; Pereira, H.A. Partial Harmonic Current Compensation Applied to Multiple Photovoltaic Inverters in a Radial Distribution Line. In Proceedings of the 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC), Santos, Brazil, 1–4 December 2019; pp. 1–6. [Google Scholar]
- Chen, H.; Liu, H.; Xing, Y.; Hu, H.; Sun, K. Analysis and design of enhanced DFT-based controller for selective harmonic compensation in active power filters. In Proceedings of the 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, NC, USA, 4–8 March 2018; pp. 1305–1309. [Google Scholar]
- Rajeev, M.; Divya, S. Harmonic Compensation by Transformer-less Grid-tied PV inverter using Conservative Power Theory. In Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, 29–31 March 2019; pp. 1–5. [Google Scholar]
- Shawky, A.; Sayed, M.A.; Takeshita, T. Selective Harmonic Compensation of Three Phase Grid tied SEPIC based Differential inverter. In Proceedings of the 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), Anaheim, CA, USA, 17–21 March 2019; pp. 396–403. [Google Scholar]
- Xavier, L.S.; Cupertino, A.F.; Pereira, H.A.; Mendes, V.F. Partial Harmonic Current Compensation for Multifunctional Photovoltaic Inverters. IEEE Trans. Power Electron.
**2019**, 34, 11868–11879. [Google Scholar] [CrossRef] - Agrawal, S.; Vaishnav, S.K.; Ajit; Somani, R.K. Active Power Filter for Harmonic Mitigation of Power Quality Issues in Grid Integrated Photovoltaic Generation System. In Proceedings of the 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 27–28 February 2020; pp. 317–321. [Google Scholar]
- Smadi, A.A.; Lei, H.; Johnson, B.K. Distribution System Harmonic Mitigation using a PV System with Hybrid Active Filter Features. In Proceedings of the 2019 North American Power Symposium (NAPS), Wichita, Kansas, 13–15 October 2019; pp. 1–6. [Google Scholar]
- Baghaee, H.R.; Mlakić, D.; Nikolovski, S.; Dragicčvić, T. Anti-Islanding Protection of PV-Based Microgrids Consisting of PHEVs Using SVMs. IEEE Trans. Smart Grid
**2020**, 11, 483–500. [Google Scholar] [CrossRef] - Zhang, X.; Gamage, D.; Rashid, Y.; Manglani, V.; Ukil, A. PQ Control-based Novel Passive Islanding Detection Method for Renewable Energy Application. In Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand, 22–25 January 2019; pp. 1–4. [Google Scholar]
- Mlakić, D.; Baghaee, H.R.; Nikolovski, S. A Novel ANFIS-Based Islanding Detection for Inverter-Interfaced Microgrids. IEEE Trans. Smart Grid
**2019**, 10, 4411–4424. [Google Scholar] [CrossRef] - Park, S.; Kwon, M.; Choi, S. Reactive Power P O Anti-Islanding Method for a Grid-Connected Inverter with Critical Load. IEEE Trans. Power Electron.
**2019**, 34, 204–212. [Google Scholar] [CrossRef] - Voglitsis, D.; Papanikolaou, N.P.; Kyritsis, A.C. Active Cross-Correlation Anti-Islanding Scheme for PV Module-Integrated Converters in the Prospect of High Penetration Levels and Weak Grid Conditions. IEEE Trans. Power Electron.
**2019**, 34, 2258–2274. [Google Scholar] [CrossRef] - Pal, D.; Panigrahi, B.K.; Kewat, S. A Hybrid Method for Islanding Detection of Inverter Interfaced Distributed Generators Utilizing Superimposed Component of d-axis Voltage. In Proceedings of the 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA, 29 September–3 October 2019; pp. 1020–1025. [Google Scholar]
- Resende, Ê.C.; Carvalho, H.T.M.; Melo, F.C.; Coelho, E.A.A.; de Lima, G.B.; de Freitas, L.C.G. A Performance Analysis of Active Anti-Islanding Methods Based on Frequency Drift. In Proceedings of the 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC), Santos, Brazil, 1–4 December 2019; pp. 1–6. [Google Scholar]
- Mlakić, D.; Baghaee, H.R.; Nikolovski, S. Gibbs Phenomenon-Based Hybrid Islanding Detection Strategy for VSC-Based Microgrids Using Frequency Shift, THD
_{U}, and RMS_{U}. IEEE Trans. Smart Grid**2019**, 10, 5479–5491. [Google Scholar] [CrossRef] - Silwal, S.; Karimi-Ghartemani, M. On transient responses of a class of PV inverters. IEEE Trans. Sustain. Energy
**2018**, 10, 311–314. [Google Scholar] [CrossRef] - Xu, H.; Su, J.; Liu, N.; Shi, Y. A grid-supporting photovoltaic system implemented by a VSG with energy storage. Energies
**2018**, 11, 3152. [Google Scholar] [CrossRef][Green Version] - Yang, L.; Hu, Z. Implementation of Dynamic Virtual Inertia Control of Supercapacitors for Multi-Area PV-Based Microgrid Clusters. Sustainability
**2020**, 12, 3299. [Google Scholar] [CrossRef][Green Version] - Hosseinipour, A.; Hojabri, H. Virtual inertia control of PV systems for dynamic performance and damping enhancement of DC microgrids with constant power loads. IET Renew. Power Gener.
**2017**, 12, 430–438. [Google Scholar] [CrossRef] - Yap, K.Y.; Sarimuthu, C.R.; Lim, J.M.Y. Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter. IEEE Access
**2020**, 8, 49961–49976. [Google Scholar] [CrossRef] - Zarina, P.; Mishra, S.; Sekhar, P. Exploring frequency control capability of a PV system in a hybrid PV-rotating machine-without storage system. Int. J. Electr. Power Energy Syst.
**2014**, 60, 258–267. [Google Scholar] [CrossRef] - Hernández, J.; Sanchez-Sutil, F.; Muñoz-Rodríguez, F.; Baier, C. Optimal sizing and management strategy for PV household-prosumers with self-consumption/sufficiency enhancement and provision of frequency containment reserve. Appl. Energy
**2020**, 277, 115529. [Google Scholar] [CrossRef] - Karimi, M.; Mokhlis, H.; Naidu, K.; Uddin, S.; Bakar, A. Photovoltaic penetration issues and impacts in distribution network–A review. Renew. Sustain. Energy Rev.
**2016**, 53, 594–605. [Google Scholar] [CrossRef] - Vargas, M.C.; Altoé Mendes, M.; Tonini, L.G.R.; Elias Batista, O. Grid Support of Small-scale PV Generators with Reactive Power Injection in Distribution Systems. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Conference—Latin America (ISGT Latin America), Gramado, Brazil, 15–18 September 2019; pp. 1–6. [Google Scholar]
- Shamseh, M.B.; Inzunza, R.; Fukasawa, I.; Tanaka, T.; Ambo, T. Grid Support During Asymmetrical Faults using Negative Sequence Current Injection. In Proceedings of the 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), Singapore, 25–28 November 2019; pp. 1–6. [Google Scholar]
- Shuvra, M.A.; Chowdhury, B. Distributed dynamic grid support using smart PV inverters during unbalanced grid faults. IET Renew. Power Gener.
**2019**, 13, 598–608. [Google Scholar] [CrossRef] - Islam, M.; Mithulananthan, N.; Hossain, M.J.; Bhumkittipich, K. A New Grid-support Strategy with PV Units to Enhance Short-term Voltage Stability. In Proceedings of the 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), Bangkok, Thailand, 20–23 March 2019; pp. 142–147. [Google Scholar]
- Islam, M.; Nadarajah, M.; Hossain, M.J. A Grid-Support Strategy with PV Units to Boost Short-Term Voltage Stability Under Asymmetrical Faults. IEEE Trans. Power Syst.
**2020**, 35, 1120–1131. [Google Scholar] [CrossRef] - Kuncoro, M.; Darussalam, R.; Sukmono, C.B.; Garniwa, I. Dynamic Power Injection for Solar PV Constant Power Generation. In Proceedings of the 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, 24–25 September 2019; pp. 1–5. [Google Scholar]
- Cabrera-Tobar, A.; Zanatta, N.; Aragüés-Peñalba, M.; Salles, M.; Pozo, M.; Bellmunt, O.G. Active power control of a PV generator for large scale photovoltaic power plant without energy storage. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Conference—Latin America (ISGT Latin America), Gramado, Brazil, 15–18 September 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Zhu, Y.; Wen, H.; Chu, G.; Li, X. An Adaptive Constant Power Generation Control Scheme with Simple MPP Estimation for Photovoltaic Systems. In Proceedings of the 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019—ECCE Asia), Busan, Korea, 27–31 May 2019; pp. 1–6. [Google Scholar]
- Li, Y.; Wu, J. Optimum Integration of Solar Energy with Battery Energy Storage Systems. IEEE Trans. Eng. Manag.
**2020**, 1–11. [Google Scholar] [CrossRef] - Ranamuka, D.; Muttaqi, K.M.; Sutanto, D. Flexible AC Power Flow Control in Distribution Systems by Coordinated Control of Distributed Solar-PV and Battery Energy Storage Units. IEEE Trans. Sustain. Energy
**2020**, 11, 2054–2062. [Google Scholar] [CrossRef] - Samara, S.; Natsheh, E. Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks. IEEE Access
**2019**, 7, 50287–50299. [Google Scholar] [CrossRef] - Garaj, M.; Hong, K.Y.; Shu-Hung Chung, H.; Zhou, J.; Lo, A.W. Photovoltaic Panel Health Diagnostic System for Solar Power Plants. In Proceedings of the 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), Anaheim, CA, USA, 17–21 March 2019; pp. 1078–1083. [Google Scholar]
- Haba, C. Monitoring Solar Panels using Machine Learning Techniques. In Proceedings of the 2019 8th International Conference on Modern Power Systems (MPS), Cluj-Napoca, Romania, 21–23 May 2019; pp. 1–6. [Google Scholar]
- Massaoudi, M.; Chihi, I.; Sidhom, L.; Trabelsi, M.; Refaat, S.S.; Oueslati, F.S. Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting. In Proceedings of the 2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE), Singapore, 25–28 November 2019; pp. 1–6. [Google Scholar]
- Vrettos, E.; Gehbauer, C. A Hybrid Approach for Short-Term PV Power Forecasting in Predictive Control Applications. In Proceedings of the 2019 IEEE Milan PowerTech, Milano, Italy, 23–27 June 2019; pp. 1–6. [Google Scholar]
- Visser, L.; AlSkaif, T.; van Sark, W. Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions. In Proceedings of the 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC), Chicago, IL, USA, 16–21 June 2019; pp. 2111–2116. [Google Scholar]
- Ueshima, M.; Babasaki, T.; Yuasa, K.; Omura, I. Examination of Correction Method of Long-term Solar Radiation Forecasts of Numerical Weather Prediction. In Proceedings of the 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), Brasov, Romania, 3–6 November 2019; pp. 113–117. [Google Scholar]
- Zhang, R.; Ma, H.; Hua, W.; Saha, T.K.; Zhou, X. Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network with Spatial—Temporal Correlation Analysis. IEEE Trans. Ind. Inf.
**2020**, 16, 1635–1644. [Google Scholar] [CrossRef] - Wang, J.; Zhong, H.; Lai, X.; Xia, Q.; Wang, Y.; Kang, C. Exploring Key Weather Factors From Analytical Modeling Toward Improved Solar Power Forecasting. IEEE Trans. Smart Grid
**2019**, 10, 1417–1427. [Google Scholar] [CrossRef] - Atencio Espejo, F.E.; Grillo, S.; Luini, L. Photovoltaic Power Production Estimation Based on Numerical Weather Predictions. In Proceedings of the 2019 IEEE Milan PowerTech, Milano, Italy, 23–27 June 2019; pp. 1–6. [Google Scholar]
- Huang, W.; Zhang, C.; Zhang, X.; Meng, J.; Liu, X.; Yuan, B. Photovoltaic Power Prediction Model Based on Weather Forecast. In Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Brasov, Romania, 20–24 November 2019; pp. 1596–1600. [Google Scholar]
- Nikolovski, S.; Reza Baghaee, H.; Mlakić, D. ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs. Energies
**2018**, 11, 2953. [Google Scholar] [CrossRef][Green Version] - Eltawil, M.A.; Zhao, Z. Grid-connected photovoltaic power systems: Technical and potential problems—A review. Renew. Sustain. Energy Rev.
**2010**, 14, 112–129. [Google Scholar] [CrossRef] - Haque, M.M.; Wolfs, P. A review of high PV penetrations in LV distribution networks: Present status, impacts and mitigation measures. Renew. Sustain. Energy Rev.
**2016**, 62, 1195–1208. [Google Scholar] [CrossRef] - Yazdani, S.; Ferdowsi, M.; Davari, M.; Shamsi, P. Advanced current-limiting and power-sharing control in a PV-based grid-forming inverter under unbalanced grid conditions. IEEE J. Emerg. Sel. Top. Power Electron.
**2019**, 8, 1084–1096. [Google Scholar] [CrossRef] - Tafti, H.D.; Konstantinou, G.; Townsend, C.D.; Farivar, G.G.; Sangwongwanich, A.; Yang, Y.; Pou, J.; Blaabjerg, F. A Comparative Study of Flexible Power Point Tracking Algorithms in Photovoltaic Systems. In Proceedings of the 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), Singapore, 25–28 November 2019; pp. 1–6. [Google Scholar]
- Ejgar, M.; Momin, B. Solar plant monitoring system: A review. In Proceedings of the 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 18–19 July 2017; pp. 1142–1144. [Google Scholar]
- Seme, S.; Štumberger, B.; Hadžiselimović, M.; Sredenšek, K. Solar Photovoltaic Tracking Systems for Electricity Generation: A Review. Energies
**2020**, 13, 4224. [Google Scholar] [CrossRef] - Ali, A.; Almutairi, K.; Malik, M.Z.; Irshad, K.; Tirth, V.; Algarni, S.; Zahir, M.; Islam, S.; Shafiullah, M.; Shukla, N.K. Review of online and soft computing maximum power point tracking techniques under non-uniform solar irradiation conditions. Energies
**2020**, 13, 3256. [Google Scholar] [CrossRef] - Mohamed Hariri, M.H.; Mat Desa, M.K.; Masri, S.; Mohd Zainuri, M.A.A. Grid-Connected PV Generation System—Components and Challenges: A Review. Energies
**2020**, 13, 4279. [Google Scholar] [CrossRef] - Kavya Santhoshi, B.; Mohana Sundaram, K.; Padmanaban, S.; Holm-Nielsen, J.B.; KK, P. Critical review of PV grid-tied inverters. Energies
**2019**, 12, 1921. [Google Scholar] [CrossRef][Green Version] - Meegahapola, L.; Sguarezi, A.; Bryant, J.S.; Gu, M.; Conde D, E.R.; Cunha, R. Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends. Energies
**2020**, 13, 3441. [Google Scholar] [CrossRef] - Ezhiljenekkha, G.; MarsalineBeno, M. Review of Power Quality Issues in Solar and Wind Energy. Mater. Today Proc.
**2020**, 24, 2137–2143. [Google Scholar] [CrossRef] - Li, W.; Ren, H.; Chen, P.; Wang, Y.; Qi, H. Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review. Energies
**2020**, 13, 5951. [Google Scholar] [CrossRef] - Hossain, E.; Faruque, H.M.R.; Sunny, M.; Haque, S.; Mohammad, N.; Nawar, N. A Comprehensive Review on Energy Storage Systems: Types, Comparison, Current Scenario, Applications, Barriers, and Potential Solutions, Policies, and Future Prospects. Energies
**2020**, 13, 3651. [Google Scholar] [CrossRef] - Bosman, L.B.; Leon-Salas, W.D.; Hutzel, W.; Soto, E.A. PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities. Energies
**2020**, 13, 1398. [Google Scholar] [CrossRef][Green Version] - Berrizbeitia, S.E.; Jadraque Gago, E.; Muneer, T. Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis. Energies
**2020**, 13, 701. [Google Scholar] [CrossRef][Green Version] - Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R. State of the art of machine learning models in energy systems, a systematic review. Energies
**2019**, 12, 1301. [Google Scholar] [CrossRef][Green Version]

PV System | Requirements |
---|---|

Islanded | Current/voltage/power |

MPPT | |

Energy storage | |

Power quality | |

Grid-connected | Current/voltage/power |

MPPT | |

Synchronization | |

Anti-islanded protection | |

Power quality | |

Active power control | |

Grid support | |

Energy storage | |

Monitoring | |

Power prediction |

Level | Control Objective | Strategy | References |
---|---|---|---|

1 | Current/voltage | PI controllers | [14,15,16] |

Predictive control | [17,18,19,20] | ||

Passivity based control | [17] | ||

Sliding | [19] | ||

Droop control | [21] | ||

Adaptive controllers | [22] | ||

Active disturbance rejection | [14] | ||

MPPT | Improved classical | [23,24,25,26,27,28,29] | |

Intelligent algorithms | [30,31,32,33,34,35,36] | ||

Synchronization | Synchronus frame | [37,38] | |

Generalized integrator | [39,40] | ||

PLL in quadrature | [41] | ||

Enhanced PLL | [42,43,44] | ||

Novel synchronization algorithm | [45] | ||

2 | Power quality | Active filters | [46,47,48,49,50,51,52,53,54,55] |

Hybrid filters | [56] | ||

Anti-islanded protection | Passive techniques | [57,58,59] | |

Active techniques | [60,61,62,63,64] | ||

Grid support | Frequency | [65,66,67,68,69,70,71,72] | |

Voltage | [73,74,75,76,77] | ||

3 | Active power limiting | Direct power control | [78] |

Current limiting | [78] | ||

Modified MPPTs | [79,80] | ||

Energy storage | Power control | [17,81,82] | |

PV monitoring | Neural network | [83] | |

Genetic algorithms | [84] | ||

Machine learning | [85] | ||

Power forecasting | Artificial intelligence | [86,87,88] | |

Statistical methods | [89,90] | ||

Physical methods | [91] | ||

Hybrid algorithms | [91,92,93,94] |

Reference | Inner Loop | Outer Loop |
---|---|---|

[14] | PI algorithm for inner current loop | Active perturbation rejection in the outer voltage control |

[15] | PI current control | Outer loop controller is used to voltage control |

[16] | Inner current and voltage loops | Outer current loop |

[17] | Finite control set-model predictive control as inner current loop | Interconnection damping assessment- Passivity based controller as outer controller |

[20] | Vector oriented control as current controller | Model predictive control with multiple steps as voltage and power control |

[21] | Inner loop current droop control | Outer loop voltage droop control |

[22] | Non-linear current controller | Adaptive voltage controller with active disturbance injection |

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**MDPI and ACS Style**

Murillo-Yarce, D.; Alarcón-Alarcón, J.; Rivera, M.; Restrepo, C.; Muñoz, J.; Baier, C.; Wheeler, P. A Review of Control Techniques in Photovoltaic Systems. *Sustainability* **2020**, *12*, 10598.
https://doi.org/10.3390/su122410598

**AMA Style**

Murillo-Yarce D, Alarcón-Alarcón J, Rivera M, Restrepo C, Muñoz J, Baier C, Wheeler P. A Review of Control Techniques in Photovoltaic Systems. *Sustainability*. 2020; 12(24):10598.
https://doi.org/10.3390/su122410598

**Chicago/Turabian Style**

Murillo-Yarce, Duberney, José Alarcón-Alarcón, Marco Rivera, Carlos Restrepo, Javier Muñoz, Carlos Baier, and Patrick Wheeler. 2020. "A Review of Control Techniques in Photovoltaic Systems" *Sustainability* 12, no. 24: 10598.
https://doi.org/10.3390/su122410598