A Novel Approach using Adaptive Neuro Fuzzy based Droop Control Standalone Microgrid in Presences of Multiple Sources

London Journal of Engineering Research
Volume | Issue | Compilation
Authored by Srinivas Singirikonda , B. Shalini
Classification: FOR Code: 090699, 080108
Keywords: adaptive neuro-fuzzy interface system (ANFIS), battery energy storage system (BESS), state of charge (SOC), frequency control, Q/P droop control, standalone micro grid, voltage damping effect, voltage control.
Language: English

The frequency and voltage control strategy is applied for a Standalone micro grid with high penetration of intermittent renewable generation system. Adaptive Neuro Fuzzy logic Interface system (ANFIS) controller is used for frequency and voltage control for Renewable generation system. Battery energy storage system (BESS) is used to generate nominal system frequency instead of using synchronous generator for frequency control strategy. A synchronous generator is used to maintain the state of charge (SOC) of the BESS but it has limited capacity. For Voltage control strategy, we proposed reactive power/active power (Q/P) droop control to the conventional reactive power controller which provides voltage damping effect. The induced voltage fluctuations are reduced to get nominal output power. Simulation results prove the effectiveness of both frequency and voltage control and the outputs are represented in MATLAB SIMULINK software with ANFIS structure.

               

A Novel Approach using Adaptive Neuro Fuzzy based Droop Control Standalone Microgrid in Presences of Multiple Sources

Srinivas Singirikondaα & B. Shaliniσ

____________________________________________

  1. ABSTRACT 

The frequency and voltage control strategy is applied for a Standalone micro grid with high penetration of intermittent renewable generation system. Adaptive Neuro Fuzzy logic Interface system (ANFIS) controller is used for frequency and voltage control for Renewable generation system. Battery energy storage system (BESS) is used to generate nominal system frequency instead of using synchronous generator for frequency control strategy. A synchronous generator is used to maintain the state of charge (SOC) of the BESS but it has limited capacity. For Voltage control strategy, we proposed reactive power/active power (Q/P) droop control to the conventional reactive power controller which provides voltage damping effect. The induced voltage fluctuations are reduced to get nominal output power. Simulation results prove the effectiveness of both frequency and voltage control and the outputs are represented in MATLAB SIMULINK software with ANFIS structure.  

Keywords: adaptive neuro-fuzzy interface system (ANFIS), battery energy storage system (BESS), state of charge (SOC), frequency control, Q/P droop control, standalone micro grid, voltage damping effect, voltage control.

Author α: IEEE Member, Assistant Professor, TKRCET, Hyderabad, Telangana, India.

σ: IEEE Student Member, M.Tech scholar TKRCET, Hyderabad, Telangana, India.

  1. INTRODUCTION

Standalone micro-grids are generally exposed to frequency and voltage deviations. The grid becomes weaker than a conventional power system due to an isolated system. The high penetration of intermittent renewable generation such as PV and wind power makes problem [1], [2] worse. Mostly in an isolated power system, the diesel generator based on a synchronous generator which is used to generate nominal system frequency and voltage with the help of Adaptive Neuro-Fuzzy Interface system (ANFIS). The mapping point of an input to the output using Fuzzy Logic interface provides a basis from which decisions can be made and the patterns discerned. The SIMULINK software system can access the Fuzzy logic [3] test system in a block diagram. It describes all membership functions, logical operators and If – Then rules. This control strategy is applied specially to penetrate the intermittent Renewable power generation to control the frequency and voltage for stable operation of the system. Several methods are being examined to support frequency control. The strategies enabling to dispatch wind power to operate in a similar manner of conventional power plant. Wind power is a fluctuating motive source, the effectiveness of active power control of wind turbine generators (WTG) will depend upon wind speed [4]. WTG's are supplemented with doubly fed induction generator (DFIG) to expand the flexibility of wind power procurement and enhance the controllability [5]. The control approach of DFIG is to set a point of active power at fixed pitch angle [6]. With the proper reference frame, Idr will come up with the electromagnetic torque (Tem) against turbines mechanical torque (Tmech) at some rotor speed. Consequently, the torque difference between Tmech and Tem can make rotor to accelerate / decelerate.

        (1)

        (2)

Where, wr - Rotor speed, J - Inertia of motion, H -Inertia constant (s).

The primary frequency support from de-loaded wind turbines using variable droop was developed [7]. In micro-grids with high penetration of wind energy, the fluctuations in the wind form output due to variations in wind speed cause frequency disturbances. A frequency droop control was applied to PV power generation [8]. Even though the fuel cost is free but its cost of installation is high. PV's operate in the maximum power point tracking (MPPT) model to generate maximum income. However, as penetration of PV's increase the frequency regulation capability (mainly provided by synchronous generators) and inertia from synchronous generators decrease which lead to severe frequency fluctuations under some disturbances.

Moreover, load changes can lead to some significant frequency deviations if PVs don’t have frequency regulation capability. In order to avoid this, the PVs are designed with virtual governor to have frequency droop characteristics similar to that of synchronous generator. However, frequency control strategies using intermittent renewable generation are not beneficial economically. There are various ways to control voltage drop by installing regulators in substations, using online transformer tap changers, shunt capacitors, increasing the size of conductors etc. Some sensors, such as smart meters (SM) measure voltage in ref [9-10] and current at each branch send this information/ recorded data to the control center.              

Decentralized voltage control is another method which uses local data to control voltage issues. But this device operates independently and there is no communication between loads and the substation. The effect and the reliability must be maintained for these methods. The linear quadratic tracking method is one of the voltage control method used to obtain desired results. The voltage is considered at each node then the controller increases / decreases voltage to minimize the error. The monitoring of controller is based on entire system conditions. This process can be categorized as decentralized control, but it increases system complexity and it needs more study. Analyzing and modeling of power distribution would become more complex and time taking.

Most control strategies have applied optimization algorithms to meet specific objectives, such as minimizing loss, improving voltage profile, mitigating voltage fluctuation, maintaining voltage within regulated limits [11]. However, these methods will never be perfectly accurate, since they are based on forecasting load demand, wind speed and solar irradiance. The voltage compensator, shunt capacitors, LQT methods which leads to increased additional cost. Q/V droop control is widely used for voltage compensation, but the compensation is triggered by sensing the voltage deviation. Section II describes about proposed methodologies and the control strategy which includes Q/V droop control and ANFIS controller are explained in Section III. The test system operation and its simulation results are observed in different cases in Section IV.

  1. PROPOSED METHODOLOGY

In a remote power system, the Active power / frequency (P/F) and Reactive power / voltage (Q/V) droop control are used to generate nominal system frequency, voltage and some voltage compensation devices are used for control strategy. If the generating system units droop is increased, it's response to the system frequency deviation diminishes. However, frequency control strategies using intermittent renewable generation are not beneficial, because they cannot make the most of their ability to utilize free energy. BESS is used to support the frequency of micro-grid. The system stability and operational security can be improved by using BESS [12]. By improving the controllability of RES generators BESS provides a resolution to overcome the frequency control issues. Q/V droop control is widely used for voltage compensation, but the compensation is triggered by sensing the voltage deviation. The recommended strategies include.

  1. BESS is used to generate small system frequency instead of using diesel generators which does not depend on the mechanical inertia of a synchronous generator.
  2. SOC (state of charge) of the BESS is used by the diesel generator at a convinced value and the reference significance of the SOC is adjusted to limit the output power of the diesel generators to within a permissible range.
  3. Q/P droop power is added to the renewable generation which has damping effect to avoid voltage fluctuations induced by its own active power fluctuations.
  4. Adaptive Neuro fuzzy logic controller reduces the frequency and voltage fluctuations and improves the system performance.
  1. CONTROL STRATEGIES

To maintain frequency and voltage control there are many strategies to conventional power plant. A frequency control droop was added to PV generation. But these control strategies are not economically beneficial, since they cannot, maximize their usage of free energy. So, by adopting BESS (Battery energy storage system) the control strategies enabling to support system frequency deviating from its nominal value [13]. With the aid of Active power/frequency (P/F) and Reactive power/voltage (Q/V) droop control and voltage compensation devices are applied to the isolated power system. The Q/V droop control is widely used for mitigating voltage fluctuations, since the voltage fluctuations are triggered by sensing the voltage deviation.

4.1  System configuration

The proposed control strategy with ANFIS (Adaptive Neuro-Fuzzy Interface system) is tested on the below test system as shown in fig 1, which shows bus numbers, line parameters, loads and power generation system. The line parameters are calculated by considering the distance between the loads and the location of loads. The ratings of the power generation using diesel generator, wind power, PV power, BESS are 14, 9.7, 1, 15MW respectively.

Fig. 1: Power System configuration block diagram

The nominal system frequency and voltage are 50HZ and 11KV respectively and the load demands during the day are shown in the table-1. The inverters are modeled as two-level type and sinusoidal pulse width modulation were adopted to generate gate signals of the inverter.

Table 1: System Load Demand

BUS NUMBER

DAY(MW)

NIGHT(MW)

2

3

1.5

3

2

1

5

1

0.5

6

1

0.5

8

2

1

9

0.2

0.1

10

0.2

0.1

11

0.1

0.05

12

0.5

0.25

TOTAL

10

5

4.2  Frequency Control Strategy

BESS is used  to generate nominal frequency instead of using synchronous generator. Hence the system frequency is directly related to rotational speed of the rotor. To overcome this weakness we proposed BESS rather than diesel generator to form system frequency [14]. The BESS controls the nominal system frequency with the switching mechanism of power electronic devices. BESS is chosen to fulfill the frequency control strategy through its chargeable characteristic. This enables BESS to take twice the amount of load change than any other devices with same rate of power. The rapid charging and discharging characteristics of BESS can respond immediately to the output power fluctuations of renewable generation system[15]. However, BESS can neither adjust its SOC nor implement frequency droop by using control scheme of     figure 2.

Fig. 2: Control scheme of the grid-side inverter of the BESS

BESS should work in synchronization with Diesel Generator where the frequency, voltage and phase are to be matched. To generate nominal system frequency, the diesel generator should be controlled [16]. During normal operation of diesel generator in Fig.3, the switch is connected to node A, to maintain SOC at the reference value SOCref and hence the diesel generator is controlled. SOC load control is same as the load frequency control of conventional method. SOCref is chosen as 0.5PU. However, it can be adjusted by an operator to charge or discharge of the BESS.

Fig. 3: Control scheme of the output active power of the diesel generator

The output limiter and the anti windup function are added at the output of the PI-controller to keep diesel generator output active power within specified range, from 0 p.u to 1 p.u. Hence the system frequency is mostly depending upon the BESS control strategy. The reliability problem may arise due to tripping action of BESS. To prevent this problem, the switch is connected to node B, when the BESS is tripped out of the system. During the node B connection of switch, the diesel generator is controlled same as conventional one. The output of the PI controller provides Pdi,ref , which is mechanical input to the synchronous generator, via a valve actuator and a diesel engine. Tv and Td are the time constants of the valve actuator and the diesel engine with 0.05s and 0.5s respectively. Pdi is the output active power of the diesel generator. The diesel generator acts as secondary SOC control which is like frequency control, it supports the SOC rather than frequency.

4.3  Voltage control strategy

The nominal system voltage is maintained by the excitation of the diesel generator. Unlike frequency, the voltage problem must be resolved locally. Fig.4 represents the Control scheme of the grid –side inverter of the renewable generation system. To solve these voltage fluctuations caused by the intermittent renewable generation system, we propose Q/P droop control to the intermittent renewable generation system [17].

(3)

Where vid  and viq are the inverter terminal voltage of the d- and q-components respectively, iid  and iiq are the inverter terminal current of the d- and q- components, ws  is the angular frequency of the system voltage, Lf is the filter inductance, P and Q are the output active and reactive powers, KQP and KQV are the Q/P and Q/V droop coefficients, Po and Qo are the operating points of the active and reactive power ,Q maximum reactive power, Vbus is the bus voltage where renewable generation  is connected.

Fig. 4: Control scheme of the grid –side inverter of the renewable generation system

Qo is set to 0, so that the generation system can operate in unity power factor when the voltage drop is not activated. KQV ranges between 0 and 25 by considering the capacity of power generation. Wind speed or solar irradiance is given to MPPT control scheme to generate reference active power value (Pref). Pref, P measured value are given to PI controller converting it to iid,ref. id which is parks transformation value. iid,ref is compared with iid value and the error is fed to PI controller generating Vd*. It can be written as,

Vid,ref = Vsd + Vd*- 𝛚sLfiiq                                       (4)

Q/P droop control is the comparison of active power whereas Q/V droop control is the comparison of voltage control. KQP, KQV are the Q/P and Q/V droop coefficients respectively. KQP and KQV convert the error to reactive power component.

                              Qref= Qo + QP + QV                           (5)

Where Qo is the operating point of reactive power and is set to zero. Again Qref and Q are given to PI controller and generates iiq,ref. iiq,ref are compared with iq in the PI controller generating an error Vq*.    

Viq,ref = Vq* + Vsq + 𝛚sLfiid                (6)

Therefore, Vid,ref and Viq,ref are fed as input to the dq-abc transformation and is converted into Viabc,ref using inverse parks transformation for sinusoidal pulse width modulation which generates six pulses for the inverter. The speed of the generator is taken as 1.2 P.U, pitch angle is 0 and the speed of the wind is 11m/s. Instead of using PI controller in the control schemes, Adaptive Neuro fuzzy logic controllers are used to improve stability and performance of the system. Solar irradiance is about 660W/m2. By adding MPPT control scheme and Boost converters to maximize the utilization of free energy and to maintain constant output power.

4.4 Adaptive Neuro Fuzzy interface system (ANFIS)

The Effective technique called ANFIS (Adaptive Neuro-Fuzzy Interface system) which was developed by Dr. Roger Jang. Among various functions of methodologies in soft computing, the fuzzy logic and Neuro computing has visibility, which leads to Neuro-fuzzy systems. We can use Fuzzy Logic Toolbox software with MATLAB software to solve problems with Fuzzy logic. The combination of Artificial Neural Network (ANN) and Fuzzy Interface systems (FIS) has attracted the interest of researchers in various applications. Fuzzy logic interface system is a mapping point to map an input space to output space from starting point to the ending for all. Fuzzy logic is an intriguing area of research because it has a premium quality of trading off among significance and precision. An adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy system whose membership function parameters have been tuned using neuro-adaptive learning methods like those used in training neural networks. The backpropagation (BP) algorithm is used to trine the adaptive Neural network and 7*7=49 rule based fuzzy Logic command-line functions are used for training SUGENO-type fuzzy inference systems using given input/output training data [18] [19].

V.    SIMULATION RESULTS

To establish the effectiveness of the proposed control strategies, simulation results are observed during the day time in the standalone micro-grid with high penetration of renewable generation system. Voltage waveforms of PV, wind power, BESS and diesel generator are clearly presented in MATLAB simulation. The MATLAB simulation diagram for Adaptive Neuro Fuzzy Control Strategy for Standalone Micro Grid System with Multiple Renewable Sources show in Fig.5.

Fig. 5: MATLAB simulation diagram of Adaptive NEURO fuzzy control strategy for standalone micro grid system with Multiple Renewable sources

Case I: Day time

In day time, the speed of the wind ranges from 10.5 to 11.5 m/s and set to an average of 11m/s, the solar irradiance ranges at 660W/m2. The active and reactive power of wind and PV are set to be 0.413 and 0.495 respectively. The BESS responds to any output fluctuations where as the diesel generator is used to maintain SOC, thereby supporting BESS. There are some oscillations of SOC at diesel level. This is due to slow Dynamics of diesel generator and output fluctuations of active powers of renewable generation systems. Without droop control, SOC decreases slightly due to small losses, such as filter and inverter switching losses. Using droop control method SOC fluctuations are reduced with the support of diesel generation. Although it fluctuations SOC is maintained at reference value. The frequency also deviates from its nominal value without droop but the deviations are reduced with droop control with this method.

C:\Users\ganta\Desktop\shalini project\5-1.png

Fig. 6: Frequency control results for case I: (a) Active power of wind and PV (b) Active power of BESS (c) Active power of diesel generator (d) SOC (e) Frequency

The Fig.6 Shows the output active power of PV and wind, and active power of BESS and diesel generator respectively without droop control, the diesel generator takes full response for the output fluctuation of the renewable generation system with droop control, BESS supports diesel generator to meet the power demand with P/F droop control method. Fig.7 shows reactive power of wind and PV power respectively without droop control, the renewable generation system has same power factor, but by applying Q/V droop control, the reactive power is controlled by compensating voltage deviation. By applying control method, the reactive powers of PV and wind are controlled, also mitigates the voltage fluctuation.

C:\Users\ganta\Desktop\shalini project\5-2.png

Fig. 7: Voltage control results for case I: (a) Reactive power of wind power (b) Reactive power of PV power (c) Bus voltage of wind power (d) Bus voltage of PV

The bus voltage of PV and wind are kept closer to nominal value using Q/V droop control. However, the fluctuations are not effectively prevented. By adding Q/P droop control, the voltage fluctuations can be eliminated. The below fig 8 shows simulation results for Adaptive Neuro Fuzzy interface system (ANFIS) and PI-controller. ANFIS response rate is faster than PI controller and the simulation time to get output is less and easy to access.

C:\Users\ganta\Desktop\shalini project\5-3.png

Fig. 8: Comparison of Active power with Adaptive Neuro Fuzzy logic controller and PI-controller during Day time

Case II: Night time:

At night time, the solar irradiance is 0 W/m2 and the wind speed ranges from 7.1 to 10.2 m/s and set to an average of 8.5m/s. The reactive power KQP of wind and PV power are set to be as 0.473 and 0.514 respectively. Fig.10 shows output active power of PV and wind, also the output active power of BESS and diesel generator respectively. The output power response is like that of day time. While the output power diesel generator tends to fall in the simulation. At these times SOCref is increased. The active power of diesel generator can be operated within allowable range. The frequency deviation becomes greater than during the day, the proposed method maintains the frequency at the nominal value.

C:\Users\ganta\Desktop\shalini project\5-4.png

Fig. 9: Frequency control results for case II: (a) Active power of wind and PV (b) Active power of BESS (c) Active power of diesel generator (d) SOC (e) Frequency

C:\Users\ganta\Desktop\shalini project\5-5.png

Fig. 10: Voltage control results for case II: (a) Reactive power of wind power (b) Reactive power of PV power (c) Bus voltage of wind power (d) Bus voltage of PV

The Fig.10 shows the voltage control of output power fluctuation of wind is greater than that during the day. The reactive power is compensated more since output power fluctuation of wind power is more during the day. Since there is no solar irradiance, the voltage fluctuations are prevented. The voltage of the PV power differs little between the droop and proposed method. In the below Fig.11, the violet color indicates the ANFIS controller output whereas the green color denotes the PI controller.

C:\Users\ganta\Desktop\shalini project\5-6.png

Fig. 11: Comparison of Active power with Adaptive Neuro Fuzzy logic controller and PI-controller during Night time

Case III: Worst Case (When there is no solar irradiance and wind speed):

The worst case includes wind speed and solar irradiance varies from 0 to rated value. Since when solar irradiance has to be considered, the load demand is same as day time. Hence the KQP of wind and PV are set as 0.413 and 0.495 respectively based on day time data. Fig.12 shows active power of wind and PV power generation systems. However, frequency is maintained at nominal value.

C:\Users\ganta\Desktop\shalini project\5-7.png

Fig. 12: Frequency control results for case III: (a) Active power of wind and PV (b) Active power of BESS (c) Active power of diesel generator (d) SOC (e) Frequency

C:\Users\ganta\Desktop\shalini project\5-8.png

Fig. 13: Voltage control results for case III: (a) Reactive power of wind power (b) Reactive power of PV power (c) Bus voltage of wind power (d) Bus voltage of PV

The Fig.13 represents the voltage control simulation results. As a result, there are some deviations around some points but the proposed method performs better than others. The simulation results of Fig.14 show the comparison of both PI and Adaptive Neuro-fuzzy logic controllers even in worst case has better performance.

C:\Users\ganta\Desktop\shalini project\5-9.png

Fig. 14: Comparison of Active power with Adaptive Neuro Fuzzy logic controller and PI-controller during Worst case

Case IV: Load change and Tripping of BESS

We consider two cases (i) Load change (ii) Tripping of BESS for the frequency control strategy. Fig. 15 shows the load change in day time. There is a load decrement at 0.5MW at 3sec of time. SOC and frequency are maintained same as previous cases.

C:\Users\ganta\Desktop\shalini project\5-10.png

Fig. 15: Load change simulation results for case IV: (a) Active power of BESS and diesel generator (b) SOC (c) Frequency

C:\Users\ganta\Desktop\shalini project\5-11.png

Fig. 16: Comparison of Active power during load change with Adaptive Neuro Fuzzy logic controller and PI-controller

The Fig.16 shows the load change simulation of Adaptive fuzzy controller scheme and PI controller. Taking Time (sec) on X-axis and Active power (MW) on Y-axis. Fig.17 represents the results for the case of tripping of BESS. The BESS is tripped out of the system due to fault maintenance etc. The diesel generator operates its operation by changing its switch when BESS trips out of the system.  Fig.18 shows the Tripping of BESS simulation results of Adaptive fuzzy controller and PI controller scheme.

C:\Users\ganta\Desktop\shalini project\5-12.png

Fig. 17: BESS tripping simulation results for case IV: (a) Active power of BESS and diesel generator (b) Frequency

C:\Users\ganta\Desktop\shalini project\5-13.png

Fig. 18: Comparison of Active power with Adaptive Neuro Fuzzy logic controller and PI-controller during BESS tripping

Case V: Considering PV bus only

In this case, we consider active power fluctuation of the PV system while the output of wind power system is kept constant for voltage control strategy which affects the PV power system bus. The PV power system shows results in fig 19 and the reactive power is limited.

C:\Users\ganta\Desktop\shalini project\5-14.png

Fig. 19: Simulation results for case V: (a) Reactive power of PV power (b) Bus voltage of PV power

Case VI: Adjusting charge/ discharge of BESS

BESS should be controllable for the energy efficiency perspective. By adjusting the ramp rate of SOC, BESS is controlled to output the desired level of active power. Fig.20 shows charging of SOC at 1MW and active power of BESS and diesel generator.

C:\Users\ganta\Desktop\shalini project\5-15.png

Fig. 20: Simulation results for case VI: (a) Active power of BESS and diesel generator during charging (b) SOC during charging

If the diesel generator increments its output power the ramp rate of BESS is consequently adjusted to discharge the BESS as shown in Fig.21 simulation output. In this way, the BESS can be controlled to maintain the desired amount of active power by adjusting ramp rate of SOC which includes the controller of diesel generator.

C:\Users\ganta\Desktop\shalini project\5-16.png

Fig. 21: Simulation results for case VI: (a) Active power of BESS and diesel generator during discharging (b) SOC during discharging

Table 2: Comparison of results with and without controller

Cases

Without controller (pi controller)

With adaptive neuro fuzzy logic controller

1. Day time

Active power is 2.7MW at 1.4sec. Frequency is 49.8HZ with some oscillations. SOC is 0.48P.U

Active power is 2.8MW at 1.2sec. The frequency is maintained 50HZ constant. SOC is 0.5P.U with less damp of oscillations and settles faster than PI

2. Night time

Active power of wind and PV are 3.8MW, Diesel generator is 3MW. Reactive power of wind is 0.48, and PV is 0.52. Bus voltage of wind  and PV are 0.97 and 0.98 P.U

Active power of wind and PV are 4MW, Diesel generator is 3.2MW. Reactive power of wind is 0.473, and PV is 0.514. Bus voltage of wind  and PV are 0.98 and 0.99 P.U

3. Worst case (No solar irradiance and wind speed)

Active power is 2.6MW at 1.8sec time. Reactive power of wind and PV are 0.413 and 0.495 MVAR

Active power is 2.8MW at 1.5sec time. Reactive power of wind and PV are 0.5and 0.8MVAR

4. i) Load change

Active power is 4.9MW at 3.1sec of time

Active power is 4.9MW at 2.8sec of time

ii) Tripping of BESS

Active power is 2MW at 3.1sec of time

Active power is 2MW at 2.8sec of time and the frequency is maintained constant as 50HZ.

  1. CONCLUSION

To mitigate the problems of diminishing voltage and frequency fluctuations, Adaptive Neuro Fuzzy Interface system is used, which has quick response rate compared to PI-Controller. The response of BESS results in stable operation of the frequency without any deviation. For stable voltage control, Q/P droop control is added for reactive power controller of multiple Renewable generation system. The active power fluctuations are effectively prevented by voltage damping effect in the renewable generation. The output Active power of PI and Adaptive Neuro fuzzy controllers are compared and simulation results are observed on the graph during different cases. Simulation results are observed in MATLAB software by using these control strategies. The ANFIS controller improves system stability without any interruptions and produces effective performance.

REFERENCES

  1. D. Velasco de la Fuente, C. Trujillo, G. Garcera, E. Figueres, R. Ortega, “Photovoltaic Power System with Battery Backup with Grid-Connection And an Islanded  Operation  Capabilities”, IEEE. Trans. Ind. Electron., vol. 60, no. 4, pp. 1571 – 1581, Apr. 2013.
  2. M. Park and I. K. Yu, “Photovoltaic generation system simulation using real field weather condition,” Journal of KIEE, vol. 5, no. 2, pp. 169–174, 2001.
  3. D. Sumina, “Fuzzy logic excitation control of synchronous generator”, Master thesis, Faculty of electrical engineering and computing, 2005.
  4. P. Keung, P. Li, H. Banakar, and B. Ooi, “Kinetic energy of wind turbine Generators for system frequency support,” IEEE Trans. Power Syst., vol. 24, no. 1, pp. 279–287, Feb. 2009.
  5. J. Ekanayake, L. Holdsworth, and N. Jenkins, “Control of DFIG wind Turbines,” Power Eng. J., vol. 17, no. 1, pp. 28–32, Feb. 2003.
  6. J. Ekanayake and N. Jenkins, “Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency,” IEEE Trans. Energy  Convers., vol. 19, no. 4, pp.800–803, Dec. 2004.
  7. G. Diaz, C. Gonzalez-Moran, J. Gomez- Aleixandre, and A. Diez, “Scheduling of droop coefficients for frequency and voltage regulation in isolated micro-grids,” IEEE Trans. Power Syst, vol. 25, no. 1, pp. 489– 496, Feb. 2010.
  8. G. Lalor, A. Mullane, and M. O’Malley, “Frequency control and wind Turbine technologies,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 1905– 1913, Nov. 2005.
  9. M. S. P. Carvalho, P. F. Correia, and Luı´s A. F. M. Ferreira, “Distributed reactive power generation control for voltage rise mitigation in distribution networks,” IEEE Trans. Power Syst., vol. 23, no. 2, pp. 766–772, May 2008.
  10. J. Schlabbach and D. Blume, “Voltage quality in electric power system,” Inst. Elect. Eng. Power Energy Ser., vol. 36, 2001.
  11. Y.-J. Kim, “Optimal control of DG output voltage considering switching operation of ULTC and SC in distribution power systems” M.S. thesis, Dept. Elect. and Comput. Eng., Seoul Nat. Univ., Seoul, Korea, Aug. 2010.
  12. Divya, K.C., Østergaard, J.: ‘Battery energy storage technology for power systems – an overview’, Electr. Power Syst. Res., 2009, 79, (4), pp. 511–520.
  13. Shayeghi, H., Shayanfar, H.A., Jalili, A.: ‘Load frequency control strategies: a state-of-the-art survey for the researcher’, Energy Convers. Manage., 2009, 50, (2), pp. 344–353.
  14. Scott GW, Wilreker VF, Shaltens RK. Wind turbine generator interaction with diesel generators on an isolated power system. IEEE Trans Power App Sys 1984;103(5): 933–7.
  15. Divya, K.C., ostergaard, J.: ‘Battery energy storage technology for power systems – an overview’, Electr. Power Syst. Res., 2009, 79, (4), pp. 511–520.
  16. T. Senjyu, Y. Miyazato, A. Yona, N. Urasaki, and T. Funabashi, “Optimal distribution voltage control and coordination with distributed generation,” IEEE Trans. Power Del., vol. 23, no. 2, pp. 1236–1242, Apr. 2008.
  17. Serban, I., Marinescu, C.: ‘Frequency control issues in microgrids with renewable energy sources’. Proc. Int. Symp. Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, May 2011, pp. 1–6.
  18. Jang, J.-S. R., "ANFIS: Adaptive-Network- based Fuzzy Inference Systems," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, May 1993.
  19. Srinivas Singirikonda et al Int. Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.389-395.



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Subscribe to distinguished STM (scientific, technical, and medical) publisher. Subscription membership is available for individuals universities and institutions (print & online). Subscribers can access journals from our libraries, published in different formats like Printed Hardcopy, Interactive PDFs, EPUBs, eBooks, indexable documents and the author managed dynamic live web page articles, LaTeX, PDFs etc.

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