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This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

Over 400 million people in India, including 47.5% of those living in India’s rural areas, still had no access to electricity [

The control signal provided by PI controller is dependent upon two terms and is given by;

U(s) is control signal and E(s) is error signal, which is the difference between the reference signal R(s) and system output Y(s).

Genetic Algorithm is a soft computing technique which is used for optimization of PI parameters of STATCOM. Despite excellence performance by GA in searching globally acceptable optimum solution of PI controller constants, some researchers have pointed out deficiencies in GA performance viz. poor premature convergence, loss of best solution found and no absolute assurance that a genetic algorithm will find a global optimum. In recent years, Fuzzy Inference Systems (FISs) and Artificial Neural Networks (ANNs) have attracted considerable attention as candidates for novel computational systems because of the variety of the advantages that they offer over conventional computational systems [

The biggest challenge is the uncertainty associated with wind power and load demand. In order to deal with these load uncertainties in load flow problem, many probabilistic load flow methods have been proposed in the literature [

In decentralized power system, probabilistic load model (PLM) is considered using random variables as uniform PDF, Weibull PDF, normal PDF, lognormal PDF and beta PDF [

Hence, this paper contribution may be summarized as: i) effect on the performance of dynamic compensation using wide range of probabilistic pattern in composite load model and input wind power; ii) estimation of tuning parameters for STATCOM using GA, ANN and ANFIS based soft-tuning methods.

Promotional schemes of Government of India are continuously motivating to private investors for installing decentralized hybrid power systems like Wind diesel based system shown in

Synchronous generator and STATCOM generate while induction generator and load demand reactive power. In [

From Equation (3), incremental change in STATCOM reactive power can be expressed as [

In _{P} and K_{I}. The design criterion is based on minimization of integral of square error (ISE) performance criterion in all tuning methods.

It has been concluded that power system voltage stability behaviour and choice of compensation techniques significantly depends on selection of the load model and its parameters [

The simulation study of the system requires a transfer function,

These static and dynamic load model transfer functions are investigated in detail in [

The transfer function of reactive power change to voltage change

The knowledge of induction motor responses is essential for dynamic load modelling. An algorithm to achieve ^{th} order model of induction motor as dynamic load model is given below:

1) Collection of manufacturer data.

2) Identification of equivalent circuit parameters of induction motor.

3) Estimation of Induction motor responses.

4) State space modelling of induction motor.

5) Configuration of transfer function of reactive power change to voltage change.

Finally state space model of dynamic load is used to evaluate

Pattern 1: 10% step disturbances in load and input power;

Pattern 2: Introducing 1% probabilistic pattern with 10% step disturbances in load and input power;

Pattern 3: Introducing 10% probabilistic pattern with 10% step disturbances in load and input power.

The performance index used in the parameter optimization is based on the integral square error ISE criterion. If

The value of proportional and integral constant (K_{P} and K_{I}) can be selected for minimum value of ISE. To tune parameters with genetic algorithm, value of proportional and integral constant (K_{P} and K_{I}) evaluated for conventional tuning method is used as a reference value.

Like any other optimization algorithm GA starts with defining the optimization variables, the fitness function and the fitness. It ends by testing for convergence. The general steps implemented when using GAs are:

1) Generate a random initial population;

2) Create the new population by applying the selection and reproduction operators to select pairs of strings. The number of pairs is the population size divided by two, so the population size will remain constant between generations;

3) Apply the crossover operator to the pairs of the strings of the new population;

4) Apply the mutation operator to each string in the new population;

5) Replace the old population with the newly created population;

6) Copy the best-fitted individuals to the newly created population to warrantee evolution;

7) If the number of iterations is less than the maximum go to step two, else stop OR If the fitness of the best result does not get better over certain number of iteration, then stop.

Adaptive control is an attempt to redesign the controller according to its performance and to tune its parame- ters automatically. Both ANN and ANFIS can be utilized to fine tune the PI controller parameters to be an adap-

tive PID controller. Unlike other classical control methods, these methods do not require exact mathematical model of the system [

The training process of ANN model has been performed using the ANN toolbox of MATLAB. The multi-layer feed-forward network used in this work was trained using the back propagation (BP) paradigm developed.

The ANFIS combines the advantages of fuzzy logic controllers and artificial neural network controllers, avoiding their problems on the other hand [

In this paper, simulation results reveals that intelligent methods provide better performance than the conventional method in terms of various performance specifications. In this paper, the controller tuned by the various methods has been used for concentration control of a STATCOM. The intelligent methods provide better performance in terms of various performance specifications than the conventional method while the steady state error remains same at zero.

This study is compared with three different disturbance patterns as given in

For ANN based tuning, three layers neural network (having an input layer with one input representing the voltage change, a hidden layer including 20 neurons and an output layer for the two control parameters K_{P} and K_{I}) have been used, together with supervised training via a back-propagation technique. It should be mentioned that all Neural Networks are developed using NFTOOL Toolbox of MATLAB.

For ANFIS based tuning, a single-input three-output ANFIS is designed. For initializing the fuzzy system, a FIS file is trained with 100 epochs for the study. The number of MFs for the input variables is 3. The gbellmf membership function used for input variables and output membership function type is a linear type. It is worth noting that three ANFIS models are designed for the two control parameters K_{P} and K_{I} due to a limitation of ANFIS toolbox to have only one output per ANFIS. The results for K_{P} and K_{I} are tabulated in

Voltage performances for three predefined disturbances pattern in input wind and load demand are compared and plotted as shown in Figures 6-9. As

Values | Tuning methods | ||||
---|---|---|---|---|---|

Conventional PI | GA | ANN | ANFIS | ||

Pattern 1 | K_{P} | 34.3 | 12.06 | 11.7 | 12.7 |

K_{I} | 3990 | 2498 | 2511 | 2470 | |

Pattern 2 | K_{P} | 34.3 | 8.92 | 13.4 | 13.1 |

K_{I} | 4152 | 3247 | 4237 | 4210 | |

Pattern 3 | K_{P} | 35 | 1.44 | 4.4 | 4.04 |

K_{I} | 3020 | 758 | 987 | 969 |

Pattern 1 | Pattern 2 | Pattern 3 | |
---|---|---|---|

t_{s} (sec) | 0.0136 | 0.0077 | - |

V_{dip} (pu) | −0.0462 | −0.0476 | −0.0474 |

V_{rise} (pu) | 0.0051 | 0.0042 | 0.0048 |

t_{r} (sec) | 0.0011 × 10^{−}^{7} | 0.1700 × 10^{−}^{6} | 0.2887 × 10^{−}^{5} |

Pattern 1 | Pattern 2 | Pattern 3 | |
---|---|---|---|

t_{s} (sec) | 0.0134 | 0.0075 | - |

V_{dip} (pu) | −0.0463 | −0.0462 | −0.0452 |

V_{rise} (pu) | 0.0048 | 0.0097 | 0.0038 |

t_{r} (sec) | 0.0037 × 10^{−}^{7} | 0.0705 × 10^{−}^{6} | 0.1395 × 10^{−}^{5} |

Pattern 1 | Pattern 2 | Pattern 3 | |
---|---|---|---|

t_{s} (sec) | 0.0142 | 0.0074 | - |

V_{dip} (pu) | −0.0460 | −0.0463 | −0.0454 |

V_{rise} (pu) | 0.0057 | 0.0095 | 0.0041 |

t_{r} (sec) | 0.0092 × 10^{−7} | 0.8059 × 10^{−}^{6} | 0.1590 × 10^{−}^{5} |

GA, ANN and ANFIS tuning based methods give system voltage variations within a certain acceptable band. It can also be revealed that ANFIS gives better performance than ANN and GA in terms of settling time. With 10% probabilistic disturbances system is not stabilizing but due to intelligent tuning especially with ANFIS method, system voltage response approaches closure to steady state value. System first peak overshoot also decreases with ANFIS.

This paper presents a design methodology based on ANN and ANFIS for an adaptive PI voltage reactive powers control. A wide range of disturbances are considered to demonstrate the effect of system with different tuning methods. GA is employed to obtain the parameters of the PI controller yielding optimal responses. The data obtained through GA are used to train both ANN and ANFIS agent, which give the optimal controller parameters within the specified range. Both ANN testing and ANFIS testing denote notable effectiveness in learning and mapping the system characteristics. The system voltage responses also denote the effectiveness of intelligent controller over conventional controllers especially in presence of highly fluctuating disturbances.

Nitin KumarSaxena,AshwaniKumar, (2015) Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System. World Journal of Engineering and Technology,03,195-204. doi: 10.4236/wjet.2015.34020

System capacity, voltage and ratings of the system components used in this simulation study of wind diesel based isolated hybrid power system are as following:

Base power = 250 kW;

Base voltage = 400 V;

SG rating = 100 kW;

IG rating = 150 kW;

Static load rating = 200 kW;

Dynamic load rating = 50 kW;

Composite load rating = 250 kW.