# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Maximum Power Point Tracking (MMPT)
#### 3.2.1 Fuzzy Logic  
Traditional Logic [20] is limited to only two values of truth (True and False), this limits its versatility and makes it difficult to model some systems. Fuzzy Logic (FL) [21] is an extension of Traditional Logic. The main benefit of FL is that it can give a true value between 0 and 1. FL is specialized in addressing uncertainty in inputs and obtaining high performance under rapidly changing conditions, such as atmospheric ones. These kinds of techniques can also be used to aid other systems in order to improve their performance. In this section, the most important techniques related to FL are reviewed. The first implementation focused on MPPT can be found in [22]. This system uses 7 membership functions for each variable with two input variables: error in the power and the change in error; the output inferred by the fuzzy system is the change in duty cycle that controls the pulse width generation block. The main benefit of FL control is that it does not require changes or variations in its membership functions. In order to evolve these systems, it was necessary to find a method to modify the parameters of the fuzzy systems. In [23], a Fuzzy Controller that is able to perform online parameter auto-tuning is found. This system used 2 kinds of control. First, a traditional PID control to manage the small deviations and an Adaptive Fuzzy Controller to deal with the larger deviations, since this system is ideal for obtaining rapid responses. A switching function was set to determine the controller to be used. The initial fuzzy controller used triangular-shape functions as membership functions, changing the curve depending on the error. This system reduces the oscillation near the Maximum Power Point, reducing the loss of power. Another implementation of the adaptive behavior can be found in [24]. This proposal combines Fuzzy Cognitive Networks (FCN) with Fuzzy Logic Control (FLC). FCN is constructed as an extension of another system called Fuzzy Cognitive Maps (FCM) [25]. FCMs are composed of nodes and weighted arcs. Nodes represent the concepts represented, and the arcs represent the causal relationships between them. FCN relies on the knowledge of experts for the description of the nodes and the construction of the graph but does not need an initial estimation of the weights of the arcs. The combination of FLC and FCN makes the system able to track and adapt to any kind of physical variation. A Fuzzy controller needed 12 iterations to reach the same MPP that this algorithm found with 5. Figure 4: Taxonomy of most used IA method for MPPT. In order to further improve the tracking speed and accuracy of FLC, the classic Open-Circuit Technique [26] is used to find an initial estimation of the MPP voltage. The implemented system showed a good response even under variable atmospheric conditions. The work in [27] used Type-2 Fuzzy Logic [28], an extension of classical FL [29]. The main feature of Type-2 FL is that membership functions are also fuzzy, and it is used in applications where determining the exact function of a set is difficult [30]. The Type-2 FL functions are three-dimensional, they depend on three different parameters, allowing them to directly model and handle uncertainties. The changes in PV power and voltage are set as the input variables, each one using seven membership functions. The simulation showed that the system tracked MPPT even in irradiance and load variation. Oscillations around MPPT are greatly reduced and are useful for rapidly changing conditions. The overall output energy due to the proposed MPPT method was around 27.7%. The implementation and simulation were performed with MATLAB/Simulink simulation studies [31]. Type-2 Fuzzy Logic is also used for MPPT in [27; 32; 33; 34]. Another important FL extension used in MPPT is the Takagi-Sugeno Fuzzy Model (T-S FM) [35; 36; 37], T-S FM [38] is usually used in approximating complex nonlinear systems [39] and is really important due to the fact that they enable a kind of control called parallel distributed control.