Control Strategy Based on Wavelet Transform and Neural

Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2013, Article ID 375840, 8 pageshttp://dx.doi.org/10.1155/2013/375840

Research ArticleControl Strategy Based on Wavelet Transform and NeuralNetwork for Hybrid Power System

Y. D. Song,1,2 Qian Cao,2 Xiaoqiang Du,2 and Hamid Reza Karimi3

1 School of Automation, Chongqing University, Chongqing 400044, China2 School of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Correspondence should be addressed to Qian Cao; [email protected]

Received 31 July 2013; Accepted 26 September 2013

Academic Editor: Tao Zou

Copyright © 2013 Y. D. Song et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energymanagement is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paperconsists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which areall connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference betweenthe generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposesthe power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neuralnetwork is introduced to calculate the reference power of battery. Super capacitor (SC) is controlled to regulate the DC bus voltage.The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

1. Introduction

Faced with shortage of oil, rising of the petroleum priceand the increasing pollution of the environment, people allover the world are searching for solutions to energy crises.Since the end of the twentieth century, great attention hasbeen paid to renewable energy sources [1]. Wind energyand solar energy are both sustainable and nonpollutingsources and they are potentially to be alternative sourcesover traditional energy [2]. Utilizing renewable energysources like the sun and wind, hybrid renewable energysystem is becoming popular in economic and environmentalways.

However, the power of wind generator and solar panelsis fluctuant and discontinuous, so it is highly necessary toadd storage system to a single energy or hybrid energysupply to smooth the electrical power. Researches are beingstudied all over the world to solve the energy managementproblem of a DC microgrid with hybrid power generators,especially with renewable energy power generators. KalantarandMousavi [3] studied the dynamic behavior of stand-alone

hybrid power generation system with battery storage, usinga supervisory controller based on programming to balancethe energy within the system. Chen et al. [4] designed andimplemented an energy management system (EMS) withfuzzy control for a DC microgrid system. Ko et al. [5] pro-posed a fuzzy controller and utilized sliding mode nonlinearcontrol to keep a hybrid power system robust. Zhang etal. [6–8] investigated the problem of robust static outputfeedback (SOF) control and step tracking control problemfor discrete-time nonlinear systems. Yin et al. [9–11] pro-posed a method of fault diagnosis scheme with parametersdirectly identified from the process data and compared theresults of this data-driven method with process monitoringmethod.

Lithium ion batteries possess high energy density, relativehigh power density, long life span, and environmental friend-liness and thus have been used in a wide range of areas [12].Other types of batteries do not have the above advantages atthe same time. For example, lead-acid batteries have muchlower energy density than lithium ion batteries and oftenneed to be in float charging state.The favorable characteristics

2 Journal of Applied Mathematics

of lithium ion battery are very beneficial for hybrid energysystem to improve its energy capacity. Yet the transient powerfrequency and fluctuating output voltage of the generatingsystem may pose great pressure on the battery, which mayreduce its lifetime span and worsen its performance. Besides,the relatively low power density makes it difficult for batteryalone to meet the rapid changing of power generated bythe nonstable renewable energy. Therefore, another auxiliarystorage unit, along with battery storage system, may improvethe system performance in prolonging the life span of lithiumion battery and providing more smoothed power flow for theloads.

Recently, super capacitors are being researched for manygood qualities, such as considerably higher power densi-ties than those of batteries and tremendous higher energydensity than that of regular capacitors [13]. The high powerdensity of SC is suitable for smoothing and the differencebetween the rapid changing generated power and the loaddemands caused by the power fluctuation. Besides, sincetheir operation does not employ a chemical reaction, SCsare much more robust than batteries, which provide a longcycle life that is at least 500 times more than that of standardbatteries [14]. For the listed reasons above, we can come tothe conclusion that battery system with super capacity asauxiliary storage unit is beneficial for a hybrid power systemto provide high quality power andmeet the loads demands. InKamel’s research, ultracapacitor based energy storage systemis developed to smooth the output power of wind turbine andenhance MG’s performance in islanding mode [15]. Erdincadded ultracapacitor to hybrid vehicular power system toimprove the efficiency and dynamic response of a vehicularsystem [16].

Due to the different characteristics of the renewableenergy and the storage system, an energy managementstrategy is proposed in this paper to reduce the differencebetween the generated power and the power demandedby loads. Wavelet transform is applied to decompose thedifferent power into smoothed component and high fluc-tuation component. The two components are suitable forbattery and SC to compensate, respectively, according to theirdifferent features. What is more, to prevent the battery fromovercharge and deep discharge, neural network algorithm isemployed after the wavelet transform. The battery is mainlyin charge of compensatingmost of the difference between thegenerated power and the demanded power, while the SC ismainly responsible for stabilizing the voltage on the DC busof the whole system, which indirectly compensates the rest ofthe power difference.

This paper describes a standalone hybrid renewableenergy power generation system with hybrid storage systemconsisting of lithium ion battery and SC, using a waveletneural network based control strategy. The contents areorganized as follows. Section 2 illustrates the modeling of thesystem components, respectively, including wind power gen-erator, PV generator, lithium ion battery, and SC. Section 3elaborates the wavelet neural network control strategy for theenergy management system. The test results and discussionare given in Section 4.

2. System Description andComponents Modeling

The proposed hybrid system consists of a wind turbine, aPV panel and a lithium ion battery, and SC based energystorage system.All the components are connected to a voltageuniformed DC bus with converters as is shown in Figure 1.

2.1. Wind Turbine Modeling. We assume a wind turbinedriven by a permanent synchronous generator (PMSG) inthis study.The rated output power and ratedwind speed of thewind turbine are 600Wand 13m/s.The startingwind speed is3m/s, and themaximumwind speed is 18m/s.Themaximumoutput power is 800W. The model of wind turbine is builtin Matlab/Simulink software. According to the aerodynamictheory, the output power of thewind turbine can be expressedas

𝑃𝑚= 0.5𝜌𝜋𝑅

2

𝑉2

𝑤𝐶𝑝(𝜆, 𝛽) , (1)

where 𝑃𝑚is the output power extracted from wind turbine

generator. 𝜌, 𝑅, and 𝑉𝑤represent the air density, the blade

radium, and the wind speed, respectively. 𝐶𝑝is the power

coefficient, which can be expressed as a function of tip speedratio 𝜆 and the blade pitch angle 𝛽 as follows:

𝐶𝑝(𝜆, 𝛽) = 0.73 (

151

𝜆− 0.58𝛽 − 0.002𝛽

2.14

− 13.2) 𝑒−18.4/𝜆

.

(2)

in which

𝜆 =1

(1/ (𝜆 − 0.02𝛽) − 0.003/ (𝛽3 + 1)). (3)

A 13m/s turbulent wind is generated from Bladed/FASTsoftware and plotted inMatlab, shown in Figure 2.The outputpower of the wind turbine under MPPT control is shown inFigure 3.

2.2. PV Array Modeling. PV array is built by several PV cellsconnecting in series and parallel. The short-circuit current ofeach PV cell is calculated by the following equation:

𝐼sc = 𝐼sc0(𝐺

𝐺0

)

𝛼

, (4)

where 𝐼sc and 𝐼sc0 represent the short-circuit currents understandard and normal solar radiation 𝐺 and 𝐺

0and 𝛼 reflects

all the nonlinear effects.The open-circuit voltage of the PV cell is

𝑉oc =𝑉oc0

1 + 𝛽 ln (𝐺/𝐺0)⋅ (

𝑇0

𝑇)

𝛾

, (5)

where 𝑉oc and 𝑉oc0 represent the open-circuit voltage understandard and normal solar radiation 𝐺 and 𝐺

0and 𝑇 is the

temperature of PV cell. 𝛽 is a specific technology-relatedcoefficient of PV cell and 𝛾 reflects the nonlinear effects oftemperature.

Journal of Applied Mathematics 3

MPPT

MPPTPV panels

Windturbine

Power generator system

DC bus

Energy storage system

Batteries

Supercapacitor

Bidirectional

Bidirectionalconverter

converter

Loads

DC loads

Inverter AC loads

Figure 1: Hybrid power system structure.

0 10 20 30 40 50 60 70 80 90 1008

10

12

14

16

18

Time (s)

Turb

ulen

t win

d (m

/s)

Figure 2: Turbulent wind of 13m/s from Bladed/FAST software.

0 10 20 30 40 50 60 70 80 90 100Time (s)

0100200300400500600700

Out

put p

ower

of w

ind

turb

ine (

W)

Figure 3: The output power of the 600W wind turbine withturbulent wind of 13m/s.

The maximum output power from the PV array with 𝑁𝑠

PV cells connected in series and 𝑁𝑝series groups in parallel

can be written as

𝑃max = 𝑁𝑝⋅ 𝑁𝑠⋅𝑉oc0/𝑛𝑘𝑇𝑞 − ln (𝑉oc/𝑛𝑘𝑇𝑞 + 0.72)

1 + 𝑉oc0/𝑛𝑘𝑇𝑞

⋅ (1 −𝑅𝑠

𝑉oc/𝐼sc) ⋅ 𝑉oc ⋅ 𝐼sc.

(6)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Out

put p

ower

of P

V ar

ray

(W)

0100200300400500600700

Figure 4: The maximum output power of PV array.

The maximum output power from PV array is shown inFigure 4.

Since the power of wind turbine and PV array are bothobtained, the total generated power can be calculated byadding them together. In addition, we assume that the loaddemand is constant 1 kW. So the power difference betweenthe generated power and the demanded power by the loadsΔ𝑃 is

Δ𝑃 = 𝑃WP + 𝑃PV − 𝑃𝐿, (7)

in which 𝑃WP, 𝑃PV, and 𝑃𝐿are wind turbine output power, PV

array output power, and load demanded power, respectively.Variation of Δ𝑃 over time in this paper is shown in Figure 5.

2.3. Energy Storage System. As is mentioned before, thepower generated from the renewable energy includes manysharp and transient variations, which makes it hard to meetthe relatively smoothed load power demand. Therefore, itis necessary to compensate the gap between the generatedpower and the demanded power. In this scheme, the powerthat generated by the renewable energies and power thatis demanded by the load are coordinated by the energy


−1000−800−600−400−200

0200400

Pow

er d

iffer

ence

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 5: Variation of Δ𝑃.

storage systemwhich is composed of lithium ion battery bankand super capacitor (SC). Battery has high energy density,whereas it has relatively slow charging and discharging speed.On the other hand, super capacitor has the advantage of quickcharge, large power density, and long cycle life [17]. SC ina hybrid energy storage system can quickly respond to thepower smoothing instructions and better complete powersmoothing tasks [18]. Based on the above characteristics, amodified coordinated control strategy, by which the totalgenerated power can be smoothed and the loads demand canbe met as well, is proposed and elaborated in the followingchapters.

(1) Lithium Ion Battery Bank Modeling. The battery modulefrom theMatlab/Simulation software is adopted in this paper.This model allows users to apply parameters based on batterytype and nominal values. The battery bank in this paperconsists of four batteries connected in parallel, each of whichis of 24Vnominal voltage and 5Ah rated capacity.Thatmakesthe total capacity of the battery bank 20Ah. The parametersof the lithium ion battery are listed in Table 1.

(2) Super Capacitor Modeling. A classical equivalent modelfor SC is shown in Figure 6, which consists of a capacitance(𝐶), an equivalent series resistance (𝑅

𝑆) representing the

charging and discharging resistance, and an equivalent par-allel resistance (𝑅

𝑃) representing the self-discharging losses

[19]. Instead of using the common RC equivalent circuit, amodified electrical equivalent circuit for super capacity, as isshown in Figure 7, is applied in this paper. The RC branchin the modified equivalent circuit composed of 𝑅

𝑆and 𝐶

1is

called the “fast branch” and is used to represent the immediatebehavior of the SC in the time range of seconds. The RCbranch comprising 𝑅

2and 𝐶

2is called the “slow branch” and

this RCbranch presents the internal energy distribution at theend of charge or discharge [20].

3. System Control Strategy

In this section, a wavelet transform and neural network basedcontrol strategy is introduced to manage the system energy.The advantage of wavelet analysis, as opposed to conventionaltechniques, is that wavelet transform decomposes a signalinto a series of short duration waves or local basis functions(wavelets) on the time axis which allows the analysis of

RS

RP

+

−

VSC C

Figure 6: Classical equivalent model for SC.

RS

RP

+

−

VSC C1 C2

R2

Figure 7: Modified equivalent model for SC.

Table 1: Lithium ion battery bank parameters.

Parameter ValueNominal voltage 24VRated capacity 20Ah (4∗ 5Ah)Maximum capacity 20Ah (4∗ 5Ah)Fully charged voltage 27.9357VNominal discharge current 8.6957AInternal resistance 12mΩ (48/4mΩ)

local phenomena in signals consisting of many transients[21]. In this case, we apply a three-level Haar wavelet. Theoriginal signal for wavelet transform is Δ𝑃, the differencebetween the total generated power 𝑃WP + 𝑃PV and the loaddemand 𝑃

𝐿. Then Δ𝑃 is decomposed into approximation

component Δ𝑃𝑎and detailed component Δ𝑃

𝑑by wavelet

transform. According to the different respond speed andpower density characteristics of the two types of storagedevices mentioned above, the majority of Δ𝑃

𝑎, which is the

smoothed component of the total difference, is convenient tobemet by battery, while the detailed partΔ𝑃

𝑑, which contains

a lot of high frequency components, is suitable for supercapacity to compensate. The decomposition is illustrated inFigure 8.

However, if we assignΔ𝑃𝑎to be the exact reference power

of battery directly, it is highly possible that the batterieswould reach out their acceptable SOC limit, which wouldcause some certain damages to the batteries and reduce itslifetime.Therefore, neural network is introduced to maintainthe SOC of batteries within a reasonable range, so thatthey can function in good condition. In this paper, adaptivelinear (ADALINE) neural network is applied to obtain thereference power of battery storage system. Figure 9 showsthe general neural network model. 𝑝

1, 𝑝2are the inputs

of network controller; 𝑊1and 𝑊

2are the corresponding


Total detail

Approximation

400

200

−200

−400

−600

−800

−1000

150

100

50

0

0

−50

−100

−150

400

200

−200

−400

−600

−800

−1000

0

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

ΔP

Figure 8: Decomposition of power difference Δ𝑃 using Haar wavelet transform.

ADALINEInputs

Purelin(n)b

a

p2(SOC)

W1

W2

n = Wp + b∑

p1(ΔPa)

Figure 9: Model of a simple ADALINE neural network.

weight of the two inputs parameters; 𝑏 represents bias and𝑛 represents the net input; 𝑎 is the output of the networkcontroller. In this case, the inputs of the neural network areΔ𝑃𝑎—the approximation component of the total different Δ𝑃

after wavelet transformation, and the SOC of the battery. Byutilizing the input parameters, the neural network controllerdetermines the reference power for battery.The first half dataof Δ𝑃

𝑎is used to train the perception. The target value of

battery SOC is set as 0.7. If the SOC of battery is aroundthe desired value, then the reference power for battery isΔ𝑃𝑎. However, if the SOC is more/less than the desired

value, the reference power is more/less than Δ𝑃𝑎, in order

to decrease/increase the SOC to the target. This way, thebattery would be protected from being overcharged or deepdischarged.

The whole configuration of the wavelet neural networkcontroller is illustrated in Figure 10.This scheme is composedof a battery bank and a super capacity using two bidirectionalDC/DC converters, respectively, for power tracking andvoltage regulation. The reference power for the battery isobtained by the wavelet neural network elaborated above.A PI controller is then followed to track the battery’s actualpower and the system response can be achieved by generatingPWM switching signals to DC/DC converters. Besides, theDC bus voltage is regulated by controlling the bidirectionalDC/DC converter coupled with SC.

4. Simulation Results

We set the learning step of the neural network 0.008 andthe mean square error 0.005. The network is trained usingthe first half of operation data according to the principlesmentioned in Section 3. The output power of battery isdemonstrated in Figure 11. It is obvious that the variation ofbattery output power is similar to Δ𝑃

𝑎the approximation

component of power difference after wavelet transform. As


DC bus

Battery Bidirectional DC/DC

Bidirectional DC/DCSuper

capacitor

PWM

PWM

PBat

ΔP

PPV

+

−

−

+

−

PL

PWP fWT(ΔP)

SOC

ΔPa

PI

PI

Udc

AdalineNN

U∗dc

P∗Bat

Figure 10: Wavelet and neural network controller based energy management system.

−400−300−200−100

0100200300400500600700

Out

put p

ower

of b

atte

ry (W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 11: The output power of the lithium ion battery.

0.550.6

0.650.7

0.750.8

0.850.9

Varia

tion

of b

atte

ry S

OC

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 12: The variation of SOC of the lithium ion battery.

we can see, the changes of the battery power possess less sharptransition part than the original power difference Δ𝑃 shownin Figure 5. This protects the battery from being damaged byextremely fast charging and discharging operation.

The variation of battery SOC is demonstrated inFigure 12. The SOC is sustained near 0.7 as we designed inthe neural network controller.

−100−80−60−40−20

0204060

Out

put p

ower

of S

C (W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 13: The output power of the super capacitor.

0 10 20 30 40 50 60 70 80 90 100Time (s)

0102030405060

Volta

ge o

f DC

bus (

V)

Figure 14: The variation of DC bus voltage.

Figure 13 shows the output power of super capacitor,which resembles the detailed part of the power differenceafter wavelet transform.

The variation of the DC bus voltage is shown in Figure 14.The voltage is sustained within a reasonable limit by gen-erating proper PWM signals to super capacitor, which isimportant for the loads connected on the DC bus. As isillustrated in the figure, the voltage of the DC bus maintainedat the point of 48V after a short fluctuation at the beginning.

The simulation results show that the proposed methodeffectively keeps the microgrid operating under stable state


with power fluctuation injected in it bymaking full use of twodifferent storage units. The salient features of the proposedmethod include: (1) effective in dealing with modelinguncertainties; (2) structurally simple and computationallyinexpensive; and (3) the design parameters can be readilydetermined, whichmakes it much easier than tuning conven-tional PID controller.

5. Conclusion

This paper proposes a wavelet transform and neural networkbased energy management system for hybrid power system.The hybrid power system consists of wind power subsystem,solar power subsystem, and an energy storage system. Thewind turbine and PV array are all controlled under MPPT toobtain maximum electrical power.The energy storage systemincludes lithium ion battery bank and super capacitor whichare controlled under the proposed energy management sys-tem. In the proposed control strategy, wavelet is in chargeof decomposing and then reconfiguring the power differencebetween generated power and consumed power by loads.The approximation part is compensated by battery bank.In consideration of sustaining its SOC within an acceptablelimit, neural network controller is introduced. Then thevoltage of system DC bus is maintained by rapidly chargingand discharging the super capacitor.

The simulation results demonstrate that the proposedstrategy is capable of compensating the variation powerdifference caused by the renewable energy and the loads,as well as maintaining the DC voltage stable. Furthermore,the SOC of batteries is within the recommended range, thusprotecting them frombeing damaged by overcharge and deepdischarge. Since the modeling of the system is complex anddifficult, future work will be more focused on data-drivencontroller design for microgrid.

Acknowledgments

This work is supported in part by the National Basic ResearchProgram of China (973 Program no. 2012CB215200) and inpart by the National Natural Science Foundation of China(no. 51205046).

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