International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

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Comparison of Frequency Controllers for Frequency Control of PV Generator in a Grid Interconnected PV-Diesel System

Divya Raj1 , Daru Anna Thomas2
  1. M.Tech Student, Saintgits College of Engineering, Pathamuttom, Kerala, India1
  2. Asst. Prof., Dept. of EEE, Saintgits College of Engineering, Pathamuttom, Kerala, India 2
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Abstract

Frequency is an important parameter which is to be controlled in a power system. In PV system power is fluctuating in nature due to changing insolation condition . Hence frequency has to be controlled . Fluctuating PV power causes frequency deviations in the power utilities when the penetration is large. Usually, an energy storage system (ESS) is used to smooth the PV output power fluctuations and then the smoothed power is supplied to the utility . Here, PI based frequency controller is implemented which is a fixed gain feedback controller. Then it is compared with a fuzzy based frequency controller ; Where fuzzy control is used to generate the PV output power command. This fuzzy control uses average insolation, change in insolation, and frequency deviation as the inputs. Fuzzy based frequency controller is found to be effective in performing duties like frequency control . PI Controller cannot compensate the parameter variations like insolation variations. They cannot adapt to changes in the environment. The settling time and peak time of PI controller is found to be more than Fuzzy controller. Simulation results shows that fuzzy based controller is more effective than conventional PI controller..Simulation platform used is MATLAB simulink.

Keywords
Frequency control ; insolation ; frequency deviation ; PI controller ; fuzzy logic.

INTRODUCTION
Frequency is an important parameter which is to be controlled in a power system . One of the inherent advantages of PV electricity generation is the absence of any mechanical parts (unless tracking of the sun is included). Professionally installed PV arrays are characterized by a long service lifetime, exceeding20 years, high reliability, and low maintenance requirements, which are highly desirable for remote area power supplies. In sunny locations, PV generators compare favorably with wind generators, despite the higher by the damage in strong winds. investment cost for PV.
The penetration of PV systems is rising. Two factors have been boosting this: improved generation efficiency of PV modules and governmental subsidies for the initial cost of residential PV generation systems. However, PV power fluctuates depending on the weather conditions, season, and geographic location, and may cause problems like voltage fluctuation and large frequency deviation in electric power system operation. To date, it has not been necessary for small PV generators to provide frequency-regulation services to the isolated utility. In the future, with an increasing penetration of PV generation, their impact upon the overall control of the power system will become significant. This will lead a situation, where the PV generators will be required to share some of the duties, such as frequency control. Therefore, for the large penetration of PV system’s output power in the isolated utility, suitable measures must be applied to the PV system’s side [5].
For the frequency control by the PV generator, a new control method based on simple fuzzy logic is proposed for the PV–diesel hybrid system [6]. This method uses fuzzy control to produce the output power command. Three inputs are considered for fuzzy control: frequency deviation of the isolated utility; average insolation; and change of insolation. The output power command of the system is decreased to response to a low frequency and is increased to response to a high frequency. Fuzzy based frequency control method is compared with frequency control using PI This paper is organized as follows. Section II provides thesystem description and methodology. It includes the modeling of PV ,Diesel generator and speed governor , Fuzzy logic controller design and PI frequency controller design. Section III describes the results and discussions. Conclusion is drawn in Section IV. Here the simulation platform used is MATLAB SIMULINK.

METHODOLOGY
The power utility used in this paper is shown in Fig. 1.This is actually a parallel PV–diesel system consisting of a diesel-generator set, a PV generator equipped with an ESS, an Inverter, grid and ac load.. The diesel generator supplies the load demand when no PV power or a /few PV power is available The isolated power system model used for simulation is shown in Fig. 2. where Si is the insolation, Voc is the open circuit voltage of the PV array, I sc is the shortcircuit current of the PV array, Pmax is the boost converter output power, P∗ inv is the command power of the PV Inverter, Pinv is the output power of the PV inverter, P∗ ESS is the ESS command power, PESS is the ESS output power, Pd is the generated power by diesel-generator set, R is the droop and Ki is the integral control gain of speed governor, Tsm is the time constant of the valve actuator servo mechanism, Td is the time constant of diesel engine ,M is the inertia constant and D is the damping constant of the diesel-generator set, Δfe is the frequency deviation, PL is the ac load, and P sys is the PV–diesel system’s output power.
A. Modelling of PV array
A photovoltaic system is a system which uses one or more solar panels to convert solar energy into electricity. It consists of multiple components, including the photovoltaic modules, mechanical and electrical connections and mountings and means of regulating and/or modifying the electrical output. The PV module is a nonlinear device and can be represented as a current source model, as shown in figure 3.The traditional I − V characteristics of a PV module, neglecting the internal series resistance, is given by the equation (1)
image
Where Io and Vo are the output current and output voltage of the PV module, respectively, Ig is the generated current under a given insolation, Isat is the reverse saturation current, q is the charge of an electron, K is the Boltzmann’s constant, A is the ideality factor, T is the temperature (K) of the PV module, Np is the number of cells in parallel, and Irsh is the current due to intrinsic shunt resistance of the PV module. The saturation current Isat of the PV module varies with temperature according to the following equation (2).
image(2)
image(3)
where Ior is the saturation current at Tr , Tr is the reference temperature (K), Eg is the band-gap energy, It is the short circuit current temperature coefficient, and Isc is the short-circuit current of PV module. The current due to the shunt resistance is given by (4)
image(4)
Where Ns is the number of cells in series and Rsh is the internal shunt resistance of the solar module. For the solar MATLAB/SIMULINK based computer simulations
B.Diesel Generator Model.
The standard model of the diesel generator and speed governor is illustrated in Fig 4. This model is widely used and describes well the dynamic behavior of small diesel generator sets, as it has been shown in [2]. The diesel engine and the valve actuator servomechanism are represented by first-order lags, with time constants Td and Tsm Parameters of the speed governor are the droop R and the integral control gain Ki The objective of the integral control is to eliminate the steady-state frequency error. The diesel engine must be able to follow the variation of loads and PV power. The size of frequency variation indicates how well the diesel and its governor maintain the balance of active power in the system .Under transient conditions, the frequency and the voltage will not be absolutely constant because PV power and load variations change constantly
C . Modelling of PV inverter.
Circuit diagram of the PV inverter is shown in the figure 5. Here the inverter that is being used is a three phase 3level diode clamped inverter .Here diode is used as the clamping device to clamp the dc bus voltage so as to achieve steps in the output voltage. Three-level inverters have a circuit configuration consisting of (3) DC bus levels and (12) IGBT’s. A 3-level inverter has 3 levels of switching namely 0, +Vdc/2 , and Vdc. Advantages of using multilevel inverter is that as the number of levels is high the harmonic content can be reduced. Lower switching frequencies can be used and hence reduction in switching losses.
D. Fuzzy Logic Controller Design
Here a simple active power control according to the load and isolation variations is presented by using the fuzzy logic. In order to control the output power of PV system considering the power utility and insolation conditions, output power command P∗ inv is generated by the output power command generation system shown in Fig. 6. This command system consists mainly of two fuzzy reasonings. Fuzzy reasoning is described by a set of “if-then”-based fuzzy rules. Fuzzy reasoning [3]-[4] is effective when mathematical expressions are difficult by inherent complexity, nonlinearity, or unclarity. Therefore, no deterministic model is required.
For fuzzy reasoning I there are two inputs frequency deviation Δfe , and the average insolation ⃗ which is given by (5).
image(5)
where t is the present time, T is the integral interval, an Si is the instantaneous insolation of PV system. Fuzzy rules and membership functions of fuzzy reasoning I are shown in Table I and Fig. 7, respectively. Here, the power control of the PV system according to the power system condition is accomplished by using frequency deviation Δfe as the input of the fuzzy reasoning. Fuzzy rules and membership functions that yield an output to reduce the frequency deviation are defined by trial and error.
Frequency deviation Δfe and the change of insolation ΔSi are used as inputs o fuzzy reasoning II, where ΔSi is expressed as follows.
image(6)
Power command that depends on the power system condition rather than the insolation condition is decided by using the frequency deviation Δfe as input for both of the fuzzy reasonings. In addition, the change of insolation ΔS i is used as one of the inputs since the objective is to decrease the frequency deviation .Fuzzy rules and membership functions of fuzzy reasoning II are shown in Table II and Fig 8. respectively. rules and parameters of membership functions are determined to prevent the increase of frequency deviation
The sum of the outputs of fuzzy control I γ I and fuzzy control II γ II become the central power command by using the following:
image(7)
where Prated is the rated power of the PV system, Ts is the sampling time, and f(t) is a periodic function .
E. Comparison of Fuzzy Based Frequency Controller and PI Based Frequency Controller.
Frequency controller based on PI controller is shown in the Fig 9 .

RESULTS AND DISCUSSIONS
In this paper, effectiveness of the proposed method to provide frequency regulation is examined by simulation with the system model [5] and parameters mentioned in Table III. Simulation parameters of the power utility, the PV array, the power conversion system, and the diesel generator are shown in Table III. Here, the integral time T is 100 s, and the sampling time Ts to obtain discrete value of output power command is10 s. The total simulation time is 30 min.
A.Frequency control performance with Fuzzy controller.
From the simulation results shown in Fig 10 the variation of inverter and diesel power according to change in insolation condition is shown. Fig 11. a shows the frequency deviation with fuzzy controller. From the figure we can understand that the frequency deviation settles to zero at 8 seconds From these results we can understand that the power produced by the proposed method is controlled according to the load variation to minimize the frequency deviations. Therefore ,it can be said that the proposed method can provide frequency control effectively.
B. Comparison of PI and Fuzzy controller
The frequency response of both PI and fuzzy controller is compared.. The frequency response curve with PI controller mentionedin Fig 9 is shown in Fig 11 .b in which the frequency deviation settles to zero only at 8 seconds .Comparison of the frequency control performance of both fuzzy and PI controller is shown below.

CONCLUSION
This paper presents a fuzzy based frequency control method for the PV generator in a PV–diesel grid connected system considering load condition and insolation condition.. This method uses fuzzy control to produce the output power command. Three inputs are considered for fuzzy control: frequency deviation of the isolated utility; average insolation; and change of insolation. The output power command of the system is decreased to response to a low frequency and is increased to response to a high frequency.PV-Diesel system is extended to grid and is found to be effective in performing duties like frequency regulation. Comparison of fuzzy with PI based frequency controller is done. As PI controllers are fixed gain feedback controllers , they cannot compensate the parameter variations like insolation variations.They cannot adapt to changes in the environment. Therefore it can be concluded that the proposed method is effective in performing frequency control.

Tables at a glance
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Table 1 Table 2 Table 3 Table 4
 

Figures at a glance
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Figure 1 Figure 2 Figure 3 Figure 4
Figure 5 Figure 6 Figure 7 Figure 8
Figure 5 Figure 6 Figure 7 Figure 8
Figure 9 Figure 10 Figure 11
Figure 9 Figure 10 Figure 11
 

References

  1. A.G. Bakirtzis and E.S.Gavanidou,”Optimum operation of a small autonomous system with unconventional energy sources,”,Electr.Power Syst.Res,vol23,pp.93-102,1992.
  2. S. A. Papathanassiou and M. P. Papadopoulos, “Dynamic characteristics of autonomous wind-diesel systems,” Renewable Energy, vol. 23, pp. 293–311, 2001.
  3. Y. K. Chen, C.-H.Yang, and Y.-C.Wu, “Robust fuzzy control photovoltaic Inverte r with Taguchi method,” IEEE Trans. Aerosp.Electron.Syst.,vol. 38, no. 3, pp. 940–954, Jul. 2002.
  4. B. Willer, M Wollny, Optimised design-Terrschelling PV/wind/diesel/battery system,” Eur.Photovotaic Solar Energy Conf., Amsterdam, The Netherlands, 1994.
  5. R. Hara, H. Kita, “Testing the technologies,” IEEE Power Energy Mag., vol. 7 no. 3,pp. 77–85, May/Jun. 2009.
  6. T. Tadokoro, K. Taira, A photovoltaic-diesel generation system for small islands,” in Proc. IEEE 1st World Conf. PhotovoltaicEnergy Convers., 1994
  7. D. Burkhardt and P. Bonissone, “Automated fuzzy knowledge base generation and tuning,” in Proc. IEEE Int. Conf. Fuzzy Syst., San Diego, CA,1992, pp. 179–188A.
  8. Manoj.Datta,TomonobuSenjyu,”A Frequency control approach by photovoltaic generator in a PV-Diesel Hybrid Power System,”IEEETrans.Energy Convers.vol26,no.2,pp.559-571 ,June.2011