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The input signals are fuzzified and represented in fuzzy set notations by membership functions. Fuzzy logic control engages three steps: fuzzification, interference and defuzzification. Fuzzification transforms the non-fuzzy input variable measurements into the fuzzy set linguistic variable that is a clearly defined boundary. The membership functions belonging to the other phases are identical. The membership functions for the inputs are shown in Fig. Z axis represents the output modulation technique.

Define only membership function does not complete fuzzy logic designing. The Rule Viewer displays a roadmap of the whole fuzzy inference process. It is based on the fuzzy inference diagram. The three column plots represent rules of E, CE and output.


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Each rule is a row of plots, and each column is a variable. The rule numbers are displayed on the left of each row. You can click on a rule number to view the rule in the status line.

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The defuzzified output is displayed as a bold vertical line on this plot. The Rule Viewer allows you to interpret the entire fuzzy inference process at once. The Rule Viewer also shows how the shape of certain membership functions influences the overall result. Based on these rules output duty cycle range is decided. The optimization of duty cycle is most important output of any PV grid array. Which is predicted by fuzzy logic tune with BFO based algorithm.

In BFO, bacteria move in random direction in search of their food takes lot of time into convergence. The initial position of bacteria is based on the position of particles in PSO and updated velocity of bacteria constitutes the direction of bacteria in BFO which is accurately tuned rather than random.

The ranges of membership function of two inputs and one output are defined.


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  8. These ranges are fixed once and are not changes during the simulation. The membership function range based on the input conditions is tuned and a hybrid bio- inspired algorithm is proposed. The different steps used to apply proposed algorithm is to initialize the random positions and directions of bacteria. Now consider the searching space dimension for 12 membership function values to be tuned. Initialize the chemotactic, swarming, reproduction and dispersion steps and the initial step size of bacteria is taken as 0. Initialize the weighting parameters of PSO as 1.

    In each chemotactic step, for every bacteria fitness function is evaluated and position of bacteria is updated by using position updation formula given in Eq. In swarming step the previous fitness function output is compared with the next position output of same bacteria.

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    If found less then position of bacetria is updated again by position updation formula. The present position of bacteria is termed as current position of particle for PSO and output of fitness function is J local for the PSO. The velocity of each particle is further updated from random initial velocity to a PSO tuned velocity by using the formula given in Eq.

    The chemotactic and swarming loop continues till all initialized steps are completed. In each loop PSO updates the direction of bacteria and move the bacteria into the direction of fast convergence.


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    8. Reproduction steps take place for bacteria with high fitness function values. To disperse or kill the weak bacteria, a probability of 0. If random probability is higher than it, bacteria are dispersed or vice versa.

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      Result will be positions of bacteria with minimum fitness function output. To tune the range values of membership functions of fuzzy logic. Moreover, two points of each membership function are common to others. Trapezoidal and triangular functions are used in above algorithm so a total of 51 values should be tuned but in actual these are just 12 values which requires tuning as per change in initial conditions. The proposed model of the system is shown in Fig. For easy user access a block for fuzzy controller tuning is provided separately so that clear comparison between algorithms can be visualised.

      Modelling of all three sub blocks provided is same except MPPT control part. Every curve has a unique maximum point which is called maximum power point.

      Modern Optimisation Techniques in Power Systems

      We worked towards raising the value of this point. Note that with decrease in radiation intensity MPP point also reduces. Varying intensity radiations are used as the input in the model and the stability of model is analysed by the constant DC voltage. If new values are carefully analysed then we will find that these are satisfying all constraints of the PV system models. Since DC voltage is the evaluation parameter for the stability of the PV grid model. The PV array output power by three methods is plotted in Figs. The change in power and voltage is based on change in duty cycle of boost converter.

      Department of Electrical Engineering,Tsinghua

      A comparative duty cycle variation of all three methods is shown in Fig. This difference in duty cycle causes variation in output power of PV array. Gualtiero Fantoni. Maria Isabel Aldinhas Ferreira. Marco Ceccarelli. Hartmut Bremer. Marja Helena Kankaanranta.

      Gianni Conte. Spyros G. Kenzo Nonami. Yong-Hua Song. Danwei Wang.

      Andrea Simonetto: Data‐Driven Optimization in Power Systems

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