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 . 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.  introduces improved system operation.
- The maximum power trapezium (MPT) method is considered as a classic MPPT . 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.  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  to systematically schedule the search for the global peak, by using the maximum power triangle method.
- Ref.  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 . 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 . Additionally a novel simplified propagation process is considered.
- A fuzzy logic MPPT optimized by a combination of PSO and GA is presented in . The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem solved by using PSO-GA.
- Ref.  poses an improved gray wolf optimizer. In this nature-inspired algorithm a convergence factor is integrated to improve the dynamic performance.
- A PLL based on a decoupled double synchronous reference frame is presented in . This structure is suitable for unbalanced grid and variable frequency conditions.
- Ref.  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 .
- Ref.  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 . 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  for unideal grid conditions.
- Ref.  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 . 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  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 , 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 , 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 . An enhanced DFT is proposed. The controller provides a feedback for each harmonic being able to compensate different harmonics .
- A flexible method of selective compensation based on instantaneous power theory is presented in . 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 . This technique is based on cascaded LPFs and synchronous rotating DQ frame.
- Ref.  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 . 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 . This approach is focused in module integrated converters with pseudo dc-link.
- Ref.  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 . 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 . 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 . This method exploits powerful classification capability. Algorithm collects measures of current, voltage, power, frequency and THD.
- Ref.  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 . 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 : auto-regressive integrated moving average model with an ANN model considering weighing factors computed periodically by means of least squares method.
- Ref.  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 . 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 . The method is performed at three stages: PV systems modeling, machine learning methods for mapping weather features with solar power and forecast adjustment.
- In  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 . 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  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.
5.1. 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.
Conflicts of Interest
|AFD||Active Frequency Drift|
|ANFIS||Adaptive Neuro-Fuzzy Inference System|
|ANN||Artificial Neural Network|
|DFT||Discrete Fourier Transform|
|ESS||Energy Storage System|
|LPF||Low Pass Filter|
|MPPT||Maximum Power Point Tracking|
|MPT||Maximum Power Trapezium|
|NWP||Numerical Weather Prediction|
|PCC||Point of Common Coupling|
|PLL||Phase Locked Loop|
|PSO||Particle Swarm Optimization|
|P&O||Perturb & Observe|
|SVM||Support Vector Machine|
|SOGI||Second Order Generalized Integrator|
|THD||Total Harmonic Distortion|
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|Active power control|
|Passivity based control|||
|Active disturbance rejection|||
|PLL in quadrature|||
|Novel synchronization algorithm|||
|2||Power quality||Active filters||[46,47,48,49,50,51,52,53,54,55]|
|Anti-islanded protection||Passive techniques||[57,58,59]|
|3||Active power limiting||Direct power control|||
|Energy storage||Power control||[17,81,82]|
|PV monitoring||Neural network|||
|Power forecasting||Artificial intelligence||[86,87,88]|
|Reference||Inner Loop||Outer Loop|
|||PI algorithm for inner current loop||Active perturbation rejection|
in the outer voltage control
|||PI current control||Outer loop controller is used to voltage|
|||Inner current and voltage loops||Outer current loop|
|||Finite control set-model predictive|
control as inner current loop
|Interconnection damping assessment-|
Passivity based controller as outer
|||Vector oriented control as current|
|Model predictive control with multiple|
steps as voltage and power control
|||Inner loop current droop control||Outer loop voltage droop control|
|||Non-linear current controller||Adaptive voltage controller with|
active disturbance injection
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