A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables

Abstract

Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-versus-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)-(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

“…System. Developing fuzzy logic control for the MPPT [61][62][63][64][65][66][67] involves determining the scaling factor parameters and the shape of the fuzzy membership functions. The two inputs and one output for this purpose are ( ), which is tracking error, Δ ( ), which is change of tracking error, and Δ ( ), which is the change of the duty cycle.…”

Maximum Power Point Tracking of a Small-Scale Compressed Air Energy Storage System

“…Situations where the MPPT FLC has made wrong judgments in a PV system are reported with solar irradiance changes causing the loss of direction [94]. The SS-CAES system is therefore The simulation results in Figure 5.5 illustrate the dynamic response and speed when the pressure changes from 6 to 4 bar.…”

“…One of the drawbacks of the FLC based MPPT algorithm presented by Shiau et al [94] for a PV system is that it tends to make the wrong judgment about the direction of search. This occurs as a result of input power fluctuations due to irradiance variations.…”

“…As can be seen, the slope of dP am /dD <0 when the operating point is on the low speed side of the MPP, and vice versa, as shown in Figure 4.3. and a fuzzy logic techniques [93,94], the rate of change of P am in (3.2) with respect to changes in speed is used in terms of the derivative product rule. It is given by As the operating point approaches the MPP, the speed step changes ideally need to be reduced in magnitude so that the MPP is not overshot.…”

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

“…Such non-linear and non-minimum phase characteristics further confuse the MPPT of the boost converter [5]. To overcome these problems, different conventional and intelligent MPPT algorithms have been proposed such as Incremental Conductance (IC) [6][7][8], Open Circuit Voltage (OCV) [9], Short Circuit Current (SCC) [10], Perturb and Observe (P&O) [11], fuzzy logic [12][13][14][15], feedback linearization [16], neural network [17][18][19][20][21][22], neuro-fuzzy [23][24][25] and sliding mode [26,27]. Nevertheless, there still remains the concern of fast and accurately determining the locus of the MPP during high weather variations and external load changes occurring.…”