PID CONTROL FOR MICRO-HYDRO POWER PLANTS BASED ON NEURAL NETWORK

PID CONTROL FOR MICRO-HYDRO POWER PLANTS BASED ON NEURAL NETWORK

Lie Jasa , Ardyono Priyadi , Mauridhi Hery Purnomo ^{A) B) C)}

A) Electrical Engineering Udayana University, Bali, Indonesia. Email: liejasa@unud.ac.id

B) Electrical Engineering of Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. Email: priyadi@ee.its.ac.id

C) Electrical Engineering of Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. Email: hery@ee.its.ac.id

ABSTRACT

Micro-hydro power plants are power plants with small capacity, which is built in specific locations. The main problem of micro-hydro is the voltage generated is not stable at 220 VA and frequency of 50 Hz. A micro- hydro that was constructed by Lie Jasa et al. in Gambuk village at Pupuan sub-district, Tabanan district of Bali province, Indonesia in 2010 is still an open loop system in which spin turbine is stable when it is set from the high water level in reservoirs. This will be problematic when the generator load changes. This study will overcome the problem by proposing to build a closed loop system from the change in output frequency for the control circuit. The control circuit is a circuit constructed neural network- based PID control by using the Brandt-Lin algorithm to control the governor. The governor function is to regulate the amount volume of water running into turbine. By applying Matlab simulation, the result shows that the best output is obtained when the the change in frequency will stabilize at about 40 seconds and using the value of Kp = 0.0637533, Ki=0.00021801 and Kd=0.00301846.

KEY WORDS

PID, Turbine, Neural network, Micro-hydro, Frequency

1. Introduction

There is a growing research related to micro-hydro, such as the advanced control of micro-hydro [1], the simulation of ANN Controller of Automatic Generation Control micro-hydro [2], Artificial Neural Networks to Predict River Flow Rate into Dam of Micro-hydro [3].

Scholars also have studied neural network focused on control sensor base linearization Neural Network [4], and Experimental Study of Neural Network Control System for Micro Turbines [5]. Research on a series of neural- based PID control with a variety of algorithms has also been conducted, such as PID-Neural Controller based on the AVR Atmega 128 [6], the PID-Controller based on BP neural network in the application of wind power generation [7], Application of Neural Network to Load- Frequency Control in Power Systems [8], Automatic

tuning of PID controller using Particle Swarm Optimization (PSO) algorithm [9], and Design for Auto- tuning PID controller based on Genetic Algorithms [10].

The study specifically control systems based on PID neural network with Brandt-Lin algorithm to control the micro-hydro that does not exist. Researchers have also studied micro-hydro using PI controller based on NN- perceptron [1], however, there is no research on PID control system is based Neural Network with Brandt-Lin algorithm. This paper discusses this system to determine the values of Ki, Kp and Kd simulated with a micro-hydro plants, which has already existed [13].

The main purpose of this paper is to build a model- based neural network PID control that is used to control the Micro-Hydro Power (MHP). This control is set to be able to control the turbine rotation to become stable at a certain round when the load changes. The system is a closed loop control using feedback from the output of the generator. By adjusting the volume of water from the spill away through the governor, the turbine rotation can be maintained automatically. With the stability of turbine rotation, the generator will generate a voltage stabilized at 220 VA with a frequency of 50 Hz.

2. Plant Models and Control for MHP Plants

Automatic control system of micro-hydro is built in a closed loop. First some water are flow in the valve, it continue to the spill way and rotate the turbine.

Water

Turbine

Generator Sensor Frequency Frequency

Output Frequency

set point 60 Hz Integral controller

Amplifier Regulator

Valve

+-

Delta Frequency

Speed Change

Figure 1. System Control of Micro-Hydro Proceedings of the IASTED Conference

April 2 - 4, 2012 Phuket, Thailand

Modelling, Identification, and Control (AsiaMIC 2012) Asian

DOI: 10.2316/P.2012.769-039

The generator will produces electricity in the next step and output it will through on the sensor frequency.

The frequency measurement will be compared with the reference frequency. Difference frequency (∆f) will be entered into the control integrator. It will used to set and behind the valve. The Illustration it is of control system shown in Fig 1.

Plants models for MHP plant was controlled using a servo motor as governor in the study by M. Hanmandlu [1]. Consists of five blocks: (1). PI control, (2). Governor, (3). Servo motor, (4). Turbine and (5). Generator, Detail of model MHP as shown in Fig 2.

PI

1 / R

1 (1+sT2)

(1-sTw1) (1+0.5sTw1)

Kp 1+sTp

PL 1

(1+sT3)

Figure 2. Model of MHP using servomotor as a Governor [1]

The transfer function for the servo motor based governor was written like equation 1 as :

) 1 (

1 ) 1 ( ) 1 (

2

1

sT

s sT

G = + +

...1) Where T1 = mechanical time constant and T2 = Electronic time constant. In addition, unity gain is applied as a feedback. A PI Controller with the following transfer function is superimposed on the servomotor based governor as :

s Kp Ki s

G ( ) = +

...2)

Where Kpl = Proportional constant, Ki = Integral sonstant 2.1 Existing Plant Model

Plant models of a micro-hydro that was constructed by Lie Jasa et al. in Gambuk village at Pupuan sub- district, Tabanan district of Bali province, Indonesia in 2010. Existing Plant Model MHP shown in Fig. 3. The part components of the plant were: 1). 2 meters diameter of water turbine; 2). 25 meters of spill away; 3). Tansfer pulley; and 4). generator. The video of this plant can be

watched on

http://www.youtube.com/watch?v=IdyVX_1RQGs. The plant is now capable to generate electrical energy of approximately 1000 VA 5000 VA installed capacity. The amount can be supplied to 10 houses for power at night.

The plant, however, has not been yet equipped with the control circuit that can control the governor to produce the

output of generator of the frequency of 50Hz and voltage at 220V. This problem becomes a central focus on this research.

Figure 3. Existing Plant Model MHP at Gambuk, Pupuan, Tabanan, Bali, Indonesia[13]

Figure 4. Existing Turbine Plant Model MHP [13]

a. Spill away

Spill away is used to channel water from top to bottom and direct the water flow onto the turbine. The length of pipe diameter will affect the volume of water that runs. The larger the volume of water passes the bigger water impetus to the turbine. The spill away allows placing micro-hydro in the secure area from flooding during the wet season.

b. Governor

To set the influx of water from spill away to the turbine, governor is used. Governor model can be classified in several forms, such as hydraulic mechanical, electro-hydraulic and mechanical governor. Which governor used is based on the size of spill away that has been set. To set governor, so far it is done manually by an operator. Arrangements are made by turning the faucet on the end of spill away.

c. Water turbine

Turbines are used to change water energy into mechanical energy. Turbine that is connected with some pulleys is used to turn a generator. Past studies used turbine ^[13] sizing diameter of 2 meters, width of 30 cm, weight of 300 kg and material of iron. The larger the volume of water turning turbine, the greater mechanical energy produced. Besides the volume of water, water pressure falls on the turbine help to speed the turbine rotation. Overshot water turbines works with the water that falls into the blades of upper side, because of the gravity of water, turbine wheel can spin. Existing of turbine plant model MHP is show Fig. 4.

d. Generator

Generator is used to transform energy mechanic into electric energy. By rotating magnetic field on the rotor, it will cause the magnetic field in the stator. The magnetic field that occurs at the stator with certain patterns will produce electric. The larger the generator is used, the greater the electrical energy generated.

2.2 Neural Network Control for MHP Plants a. PID Control

A PID-Controller with the following transfer function is superimposed on the servomotor based governor as :

s Kds Kp Ki

s

G ( ) = + +

...3) Where it’s Kp = proportional constant, Ki = integral constant and Kd = derivative constant.

System control close loop with feedback control system is illustrated in Fig. 5; where r, e, u, y are respectively the reference, error controller output and controlled variables.

PID-Controller block receives input e (t) and produces output u, where u is the combined output of all components Ki, Kp and Kd such as shown in equation 3.

MHP

Plant ^y(t)

e(t) PID Controller +

- r(t)

u

Figure 5. Micro-hydro power with feedback control [9]

Where is PID-Controller in time described in equation (3) as:

dt t de ( ) K dt e(t) K e(t) K

u(t) =

_p

+

_i

∫ + d .………4)

Where u(t) is the controller output, et is the error, and t is the sampling instance.

b. Brandt-Lin Algorithm Neural Network

The Brandt-Lin algorithm which is originated from gradient descent considers a complex system consisting of subsystems, called nodes which interact with each other through connection weights. Fig. 6 shows a typical system, which is decomposed for Brandt-Lin algorithm.

Node 11

Node 21

Node 12

Node 22

Node 31

Input Layer

Hiden Layer

Output Layer W₁₁

W₂₂ W₁₂

W₂₁

W₁₃

W₂₃ y₁

y₂

x₁₁

x₂₂ x₂₁

x₁₂ y₁

y₂

y₁ x₁₁

x₂₁ 1

1

2 2

2

2 2 2

2

2 2

2

3 3 3

3 3

Figure 6. A typical decomposition of a systems for Brandt-Lin algorithm [6]

Brandt-Lin algorithm is given in the following theorem.

Theorem : For the systems with dynamics given by

 

 



=  ∑

=

− p

j i i ij i j i

j

F w y

y

1 1 1

If connections weights are adapted according to

w y x y F

E w y

w

w

^v _{ij i}^I

q

k j j

i jk i jk ij

1 1 1 1

1 1

1 1

1

1 ( )

₋

=

+

+

 





 





∂

− ∂

= ∑ γ

Then the performance index it will decrease monotonically with time.

c. PID-Controller with Neural Network

The controller based on neural network has ability to make the unstable system because of its nonlinearity and input-output mapping. In addition, training procedure enables the controller to adapt changes of plant or noise.

PID-Neural control system is shown in Fig.5. The PID- Neural controller has 3 inputs and 1 output. The inputs are created by proportion, integration and derivation of error between reference input and output.

The structure of neural network used in PID-Neural controller has shown in Fig.6. The neural network it has 2 layers, input layer it has 2 neurons, output layer has 1 neuron. The neurons is activation function of input layer are tansig-

function ^x

x

e x e

+

= − 1 ) 1

1

( σ

, gauss-function

2

2

( ) 1 



 



 +

− −

=

_{x x}

x x

e e

e x e

σ

, and that of output layer is linier function

σ

₃

( x ) = x

.

s 1 s

MHP PLANTS

Ti Td Kp

y(t) Neural Network

e (t) u(t)

r(t)

+ -

eI eD eP

Figure 7. Training blocks PID Neural Network [6]

W¹11

W¹₃₂

W¹21

tansig

Gauss W²₁₁

W²21

Purelin e_p

e_D

e_i

U S¹₁

S¹₂

Z¹₁

Z¹2

S²₁

Figure 8. Structure of the neural network [6]

During the period of settling time, ep and eD decrease. At first, ep and eD are large, raising the need of large control step for quick going into settling time state. Then when nearly coming to settling state, ep and eD are smaller and smaller, requiring small control step for accurate control.

During the period of settling time ei increases, at first, ei is small, raising the need of large control step for quick going into settling state. Then when nearly coming to settling state, ei is larger and larger, requiring small control step for accurate control.

Calculating output of the neural network is following[6] :

1

S

1 =

W

₁₁¹ . ep +

W

₃₂^{1 .e}D and

Z

₁¹= σ1(

S

₁¹)

1

S

2 =

W

₂₁¹ . ei and

Z

¹₂= σ1(

S

¹₂) u =

W

₁₁¹.

Z

₁¹ +

W

₂₁¹.

Z

¹₂

Trained the neural network using Brandt-Lin algorithm is following[6] :

2

W

11 = γ

Z

₁¹e

2

W

21 = γ

Z

¹₂e

1

W

11= σ1 (

S

₁¹)

1 1 1

1 1 1 2 11 2 1 11 1

)

( Z

e E S W

z W e

p p

σ γσ σ

−

2 1 11

1

W Z e

E β

δ δ = −

σ1 (

S

₁¹) =

) 1 )(

1 (

2

1 1 1

1 1

1 1

s

s

e

s = + e

⁻

−

⁻

δ

δσ

1

W

11 = σ1 (

S

₁¹)

 



 

 + e

Z W W

e

_{p 1}

γ

1 2 2 11 11

= σ1 (

S

₁¹)

 



 

 + e

Z e W Z

e

_p

γ γ

1 1 1 2 1 11

= 2γ

γσ

₁

( S

₁¹

) e

_p

eW

₁₁²

2 11 1

1 1 1

32

2 ( S ) e eW

W = γσ

_i

2 21 1

2 2 1

21

2 ( S ) e eW

W = γσ

_D

σ2 1

S

2)

=



 





 





 





 

 +

− − +

− −

=

−

−

−

− 2

1 2 2

) 1

)

2

₁

2 1 12

1 2 12

1 2 1 12

1 2 12

s s

s s s

s

s s

e e

e e e

e e e δ s

δσ

d. Data Simulation of MHP Plants

Data simulation in this paper uses combination data research [1][13], total rate capacity change from 50 KW to 5 KW, the normal operating load of 25 KW was changed to 1 KW. This is done to adjust with the existing MHP plant, detail as shown in Table 1.

Table 1. Data plant MHP simulation

No Data Value

1. Total rated capacity 5 Kw 2. Normal Operating Load 1 Kw

3. Inertia Constant H 7.75 seconds (2<H<8) 4. Regulation R 10 Hz/pu kW (2<R<10)

Assumption: Load-frequency dependency is linier.

Nominal Load = 48%=0.48; ∆Pd =3%=0.03. The dumping parameter [4,7],

D = ∂p/∂

Hz x pukW

x

D f 0 . 0016 /

5 60

1 48 .

p/ = 0 =

∂

∂

=

Generator parameter are : Kp = 1/D = 625 Hz/pu kW

Tp =

onds

xD f

xH 161,458 sec 2

0

=

3. Formulation of Plant Models for MHP Plant

The block diagram of the MHP Plant with PID- Controller is shown in Fig. 9. This plant can be reduced to a simpler transfer function representation as in Fig.10.

Kpi+Ki/s+Kd s 1 / R

1 (1+sT2)

(1-sTw1) (1+0.5sTw1)

Kp 1+sTp

PL 1

(1+sT3)

f

XE1 XE2 XE3 Pg

f

f

PID Controoler Governor Servo motor Turbin Generator

Figure 9. Models of MHP plant using servomotor as governor with PID-Controller

Each block of Fig. 9 as following equation :

s Kds Kp Ki

G

₁

= + +

,

) 1 (

1

2

2

sT

G = +

,

) 1 (

1

3

3

sT

G = +

,

) 1 5 . 0 1 (

) 1

(

2 1

4

sT

G sTw +

= −

,

) 1

5

(

sT

P

G Kp

= +

,

K R 1

1

=

,

Pl K

₂

= ∆

G1 G2 G4 G5

K2 G3

K1

Figure 10. Model of micro-hydro power plant with transfer function

With the simplification process of the transfer function, Fig.9 above can be changed to be seen in Fig. 10.

K1.G2.G3.G4.G5 1+G2G3

K2.G5 G1

K1

y e u

r

Figure 11. Simplifying the model MHP Plant The transfer function for plant analysis MHP is:

5 2 3

2 5 4 3 2 1

) 1

( K G

G G

G G G G s K

G

_c

+

= +

………(5)

Equation 5 shows the transfer function of the MHP Plant, while the block G1/K1 is part of the PID-Controller consisting of components of Ki, Kp and Kd as in equation 3. Firstly, the value of Ki, Kp and Kd is counted using trial and error method. Secondly, training process is applied offline employing Brandt-Lin algorithm, in order to calculate the weight of each neuron as in Fig. 8. With Matlab simulation results obtained, each value of Kp = 0.0637533, Ki and Kd = 0.00021801 = 0.00301846. By entering KI, Kp and Kd values into formula (3) is obtained an equation (6) as:

s s

G 0.00021801 0.00301846

0.0637533

1

= + +

……….6)

4. Simulation PID-Controller for MHP Plants

The transfer function is of PID-Controller equation 6 must be transform in simulink. Detail of Simulink Matlab model of the PID-Controller based on neural network, shown in Fig. 12.

Figure 12. Simulink model of PID-Controller

The Simulink MHP plant (Fig. 13) was the reference of MHP plant Fig. 9, Table 1 and it was of Simulink PID- Controller Fig.12.

Figure 13. Model of MHP Plant using the Servomotor with PID-Controller

Figure 14. ∆ f for one gate schema using servomotor with PID-Controller

To run the simulation model MHP Fig. 13 above, we chose the Following values: Kn = 1, KaKg / Kc = 1; Tf = 0001 second; Kp = 0.0637533, Ki and Kd = 0.00021801 = 0.00301846. The simulation results showed that to make

the MHP plant to be stable, it takes a ∆f of 35 Hz as shown in Fig. 14 and ∆ P1 by 40 second as in the Fig. 15

Figure 15. ∆ Pl for one gate schema using servomotor with PID-Controller.

In this paper showed that MHP Plant-based PID- Controller using neural network obtained better results.

By using the value of Kp = 0.0637533, Ki and Kd = 0.00021801 = 0.00301846. MHP plant will stabilize at about 40 seconds. PID-Controller is able to maintain stability in 35 seconds starting from the beginning of the load changes.

Acknowledgement

The Author to convey gratitude to the Ministry of Education and Culture that has provided scholarships through the BPPS program and the national strategic research fund in 2010.

References

[1] M.Hanmandlu, Himani Goyal, Proposing a new advanced control technique for micro hydro power plants, Journal Electrical Power and Energy System, 30(2008), pages 272-282.

[2] J.A. Jaleel, T.P. Imthias Ahammed, Simulation of Artificial Neural Network Controller for Automatic Generation Control of Hydro Electric Power System IEEE Region 10 Conference Publication, Tencon 2008,, pages 1-4.

[3] K.Ichiyanagi Y.Goto K.Mizuno, Y. Yokomizu T.

Matsumura, An Artificial Neural Network to Predict River Flow Rate into a Dam for a Hydro- Power Plant, Proceding IEEE International Publication Neural Network, 1995 Vol. 5, pages 2679-2682.

Time (sec)

∆f (Hz)

Time (sec)

∆Pl per uni MW

5. Conclusion

[4] G.L. Dempsey, N.L Alt, B, A, Olson, and J.S.Alig, Control Sensor Linearization Using a Microcntroller-Based Neural Network, C, IEEE International Conference Computational Cybernetics and Simulation, Vol. 4, 1997, Pages 3087-3083.

[5] Tian-hong ZHANG, Xianghua HUANG and Qiu-hua LI, The Experimental Study of Neural Network Control System for a Micro Turbin Engine, Proceding of the 7th Asian Control Conference, Hongkong, China, 27-29 August 2009, Pages 1264-1267.

[6] Xuan Khanh Phan, Doan Que Anh Vo, Ngoc Hai Nguyen, The Phong Cao, PID-Neural Controller Based on AVR Atmega128, IEEE International on Control Automation, Robotics and Vision, Hanoi, Vietnam, 17-20 December 2008, Pages 1573-1576.

[7] Francoise Beaufays, Youssef Abdel-Magid, Bernard Widrow, Application of Neural Network to Load-Frequency Control in Power Systems, Journal Neural Networks, Vol. 7, No. 1, 1994, Pages 183-194.

[8] KAN Jiangming, LIU Jinhao, Selft-Tuning PID Controller Based on Improved BP Neural

Network, IEEE, International Conference on Intelligent Computation Technology and Automation, 2009, pages 95-98.

[9] S.J. Bassi, M.K. Mishra, E.E. Omizegba, Automatic tuning of Proportional-Integral- Derivative (PID) controller using Particle Swarm Optimization (PSO) Algorithm, International Journal of Artificial Intelligence & Application (IJAIA) Vol. 2, No. 4 October 2011, pages 25-34.

[10] Liu Fan, Er Meng Joo, Design for Auto-tuning PID Controller Based on Genetic Algorithms, IEEE, ICIEA 2009, pages 1924-1927.

[11] Karl J. Astrom, Bjorn Wittenmark, Computer- Controlled Systems Theory and Design, 3th, ( USA : Prentice-Hall,1997).

[12] Richard C.Dorf, Robert H.Bishop, Modern Control System (New Jersey : Prentice-Hall, 2001).

[13] Lie Jasa, Putu Ardana, I Nyoman Setiawan, Usaha Mengatasi Krisis Energi Dengan Memanfaatkan Aliran Pangkung Sebagai Sumber Pembangkit Listrik Alternatif Bagi Masyarakat Dusun Gambuk –Pupuan-Tabanan, Seminar Nasional Teknologi Industri XV, ITS Surabaya, 2011, pages B0377-B0384