Direct Intelligent Wide-Area Damping Controller forWind Integrated Power System

Reza Yousefian, Student Member, IEEE, Rojan Bhattarai, Student Member, IEEE, S. Kamalasadan, Member, IEEEDepartment of Electrical and Computer Engineering

University of North Carolina at Charlotte, Charlotte, NCryousefi@uncc.edu,rbhattar@uncc.edu,skamalas@uncc.edu

Abstract—In this paper an intelligent wide-area dampingcontroller is proposed for a wind integrated power grid. Theproposed design is based on an energy function based intelligentoptimal controller evolved from adaptive critic design usingreinforcement learning technique. A Lyapunov function candidateis derived that links the energy function and the transient stabilityof the integrated system with the wide-area controller. Theproposed wide-area controller augments the excitation systemof the synchronous generator and local Maximum Power Point(MPP) tracking control of wind generator Doubly Fed InductionGenerator (DFIG). The theoretical results are validated byconducting simulation studies on 2-Area Kundur power systemfor damping the inter-area oscillations. It was observed thatthe method improves transient stability of the integrated systemdue to the active and reactive support from the DFIG andthus damping the inter-area oscillations much faster. It was alsoobserved that the speed oscillations of DFIG is within the limits.

Keywords—Wide-Area Controller, Wind Energy, DFIG.

I. INTRODUCTION

S IGNIFICANT integration of renewable energy resourcesand severe transmission congestion has led to maximum

use of the existing power network, resulting in smaller sta-bility margins. Generally, it is believed that the renewablegenerators do not significantly take part in damping of powersystem oscillations. However, it has been illustrated that theincreased penetration level of renewables in power system willhave a damping effect due to the reduction in the numberand size of synchronous generators that limit power systemoscillations and change of the tie-lines power flows [1]. Renew-able generators are usually located far from the synchronousgenerators, where the oscillations happen. Therefore, Wide-Area Measurements (WAMs) are generally required to capturethe oscillations and coordinate the control actions of theseresources with the rest of network. Such wide-area controller(WAC) generally uses Phasor Measurement Unit (PMU) formeasurements and coordinating with controllers.

Most of the works reported in the design of WAC generallyuses small signal stability methods as linear feedback con-trollers. Conventional control techniques are also widely usedfor the active and reactive power management of renewablesources, mimicing the excitation control of a synchronous

R.Yousefian, R. Bhattarai and S.Kamalasadan are with Power, Energyand Intelligent Systems Laboratory, Department of Electrical and ComputerEngineering, University of North Carolina-Charlotte. The authors acknowledgethe support from National Science Foundation through NSF Grant ECS-1309911 awarded to the second author.

generator design [2], [3]. In some studies optimally tunedlinear controllers are designed for damping the oscillations[4]. However, these techniques either uses a local systemlinearization methodology and depends on system model evenwhen the models are not tractable. Also, offline and/or onlineparametric tuning is not possible using such design methodsthat may be required especially with high penetration ofdynamic energy resources.

Generally, artificial intelligence-based techniques such asneural network on the other hand, have shown the capabilityof dealing with such nonlinearities and uncertainties inheritedin power system in a more reliable and stable way [5]. Thesemethods effectively learn and map the system dynamics fromspecified inputs and outputs relationship, without any priorknowledge of the system. However, most of the work in thearea of classical and modern power system stability has beendesigned as classification and remedial actions schemes usingsupervised learning algorithm, rather than real-time dampingcontrol. These actions are performed online, through matchingthe online monitored data with static offline expert knowledge.Moreover, this new knowledge can in turn improve the trainingfor further events or recursively at each iteration, as in the caseof Reinforcement Learning (RL) algorithms [6], [7]. RL-basedcontrol designs has been successfully implemented in classicalwide-area control designs; such as in [6], this techniqueis performed for real-time wide-area damping deployed onexcitation system of generators as agents.

In this paper a hybrid intelligent direct method has beenproposed as wide-area damping controller for wind integratedpower grid. An optimal controller using Adaptive Critic De-signs (ACD), as one of the techniques to handle the RLproblem, is used as the control architecture. Essence of thiswork is defining an optimal cost function that is linked totransient stability assessment and designing a controller tominimize inter-area oscillations. For this, a Lyapunov stabilityfunction is used to represent the energy acquired by thegenerators and wind farms at a given time subsequent to adisturbance. The proposed controller uses the RL techniqueas optimal controller and keep the system stable satisfyingLyapunov stability criteria. The strategy is independent of thenetwork topology and is based on the fact that derivative ofthe energy function of the system must decrease. The proposedcontrol only requires WAM; it can then be easily incorporatedto the local wind farm controller as an added control function.

The rest of the paper is organized as follows. The sec-ond section provides a brief overview of system modelingand direct energy function development. In section III, the

proposed 2-level Controller design is illustrated and SectionIV discusses the proposed RL-based WAC. Section V presentsthe test results on the real-time simulation of modified Kundur2-area system, followed by conclusion in section VI.

II. SYSTEM MODELING AND DIRECT ENERGY FUNCTION

Transient rotor angle stability focus on the behavior of themachine angles trajectory and the related transient energiesduring a disturbance. Transient instability of power system iscaused by the synchronous generators mechanical and elec-trical power mismatch in the swing equation. To accuratelyinclude the effects of the wind generators in the system,the third-order center-of-inertia model of the power systemincluding the swing equation is used here [8] as,

˙δi = ωi (1)

Mi˙ωi = Pei − Pmi −

Mi

MCPC (2)

T ′doiE

′qi = (Efi − Efrefi)− (Eqi − Eqrefi) (3)0 = Pei + Pwj + Pdk +Diωi (4)0 = Qei +Qwj +Qdk (5)

where, the generator variables of swing equation are withrespect to the reference of Center of Inertia (COI) as,

δi = δi − δC , ωi = ωi − ωC (6)

with,

δC =1

MC

∑N

iMiδi, ωC =

1

MC

∑N

iMiωi (7)

MC =∑N

iMi, PC =

∑N

i(Pmi − Pei) (8)

where,

i Generator index;C Center of inertia index;N Number of generators in the area;δ Generator rotor angle;ω Generator rotor (electrical) speed;M The synchronous generator inertia constant;Pmi, Pei Generator i mechanical and electrical power;D Damping coefficient;Eq, E

′q The q-axis internal voltage and transient emf.

T ′do The open-circuit transient time constant;Ef Excitation voltage;Pwj , Qwj Power output of wind generator j;Pdk, Qdk Load demand at bus k.

An energy-type Lyapunov function for such a generatormodel, VG, comprises of the sum of the system kinetic energyand potential energy of states with respect to the equilibriumpoints. For the third-order generator model a Lyapunov func-tion of a component proportional to the squared deviation ofthe transient emf, the so-called field energy is represented in[8]. Evolving from this we get,

VG(δ, ω,Eq, Ef ) =∑i

[1

2Miω

2i︸ ︷︷ ︸

KEi

−∫ ∆δi

∆δi

(Pi +Mi

MjPj)dδi︸ ︷︷ ︸

PEi

+1

2

αiβi

(E′qi − E′

qrefi)2]︸ ︷︷ ︸

FEi

(9)

Where, α and β are parametric coefficient based on syn-chronous and transient reactances and the transfer admittancematrix, ∆Xd = (Xd −X ′

d) with Xd and X ′d as synchronous

and transient reactances, respectively. Then,

VG(ω,Eq,Ef ) = −∑j

[∑i

Diω2i +

1

T ′d0i∆Xdi

(Eqi − Eqrefi)2

+1

T ′d0i∆Xdi

(Eqi − Eqrefi)(Efi − Efrefi)] (10)

with the assumption on reference values as Efref = Eqref ,and, (Ef −Efref ) = K(Eq −Eqref ), where the gain K > 0,and, by knowing that ∆Eq = Xad∆if , with ∆if = if − ifrefas the field current deviation, and ∆if = ε∆Vt, with ∆Vt asthe control input to the field component, then

VG(ω, Vt) = −∑i

[Diω2i +K ′(∆Vt)

2] (11)

where, K ′ is the coefficient of Eq in (10), which is based oninternal parameters of generator. The damping coefficient ofthis function is derived using small signal stability analysis.

A detailed model of a renewable sources with a highnumber of wind generators, may lead to an excessive num-ber of parameters equations, which makes it impractical toimplement in real-life experiments. In addition, it is believedthat renewables are not much effected by the low frequencyoscillations [2]. Therefore, the concept of negative load hasbeen applied to renewable source, here wind generator, toindicate their capability in mitigating the transient energyof synchronous generators [9]. The size of the renewablesource model may be reduced by aggregating several unitswith similar inputs into a aggregated model. For stabilityfunction representation, considering the renewable sources asaggregated negative loads, a function Vw can be defined as

Vw(δ, E, P,Q) =∑

j[Pj δj +

QjEk

∆Ek] (12)

The time derivative of this energy function yields to:

Vw(ω,E, P,Q) = −∑

j[∆Pjωj + ∆Qj

EjEj

] (13)

Where, ω = δ. Furthermore, this energy function is augmentedto synchronous generators energy function and linked to WAC.

III. LOCAL CONTROLLERS AND PROPOSED WACMETHODOLOGY

The controller action of overall system including the syn-chronous and wind generators can be presented as 2-levelcombination of local and wide-area parts as

u(t) = uloc(t) + uwac(t) (14)

as shown in Fig. 1. The wide-area global control tracking thestates through wide-area measurements x = [xG, xw], wherexG = ωi and xw = [ωj , Ej/Ej ] derives wide-area feedbackas uwac = [uwacG , uwacw ], where uwacG = ∆V wact and uwacw =[∆Pwacj ,∆Qwacj ] to minimize the energy function and enhancethe global system behavior.

In the case of synchronous generator, the input to the ex-citation field without damping controllers is the error between

Power Network Dynamics

TDL

Wide-Area Global Control

...

+

PSS/AVR

G

Wide area measurement

Wide area feedback

TDL

TDL

𝑢𝑤𝑎𝑐 = [𝑢𝐺𝑤𝑎𝑐 ,𝑢𝑤

𝑤𝑎𝑐 ]

𝑢𝐺𝑤𝑎𝑐

Renewable Source

+

MPPT/Droop𝑢𝑙𝑜𝑐

Synchronous Generator

𝑥 = [𝑥𝐺 , 𝑥𝑤 ]

𝑢𝑤𝑤𝑎𝑐

𝑢𝑙𝑜𝑐

𝑢𝑠𝑠

𝑢𝑠𝑠

Fig. 1: Structure of the 2-level control design combining local and wide-areacontrol

the reference and the terminal voltage of the generator Fig. 2.This voltage error could be augmented with ∆V loct as localdamping signals derived from PSS and enhanced by ∆V wactas a wide-area level controller, and ∆V sst as the long-termoperating set points.

In the case of wind generator, active power and reactivepower control is applied for damping the power system os-cillations [10], [11]. Therefore, the control action, uwac, canbe derived from WAC center, and augmented to local controlloop uloc, and steady state control feedback signals as depictedin the Fig. 3. Overall, the active power reference consistsof three terms: a term for load sharing or MPPT as localfeedback (∆P loc,∆Qloc), a longer term from secondary andtertiary controller as static set point (∆P ss,∆Qss), and aremote signal from the WAC control (∆Pwac,∆Qwac). TheDFIG is operated in droop control along with MPP (MaximumPower Point) Tracking mode, where the power output of thegenerator is varied as per the wind conditions. Besides, DFIGmaintains a constant voltage at it PCC along with the reactivepower support for the grid. Both the objectives can be achievedby a current control technique in the dq-frame aligned withthe stator flux vector [10]. The rotor side converter (RSC)helps in independent control of active and reactive powerof the DFIG by controlling the rotor side current. With thereference frame aligned with stator flux, the d-axis componentof rotor current controls the reactive power (Cid) and q-axiscomponent controls the active power of DFIG (Cid). Theouter loop control in DFIG is a slower control as compared tothe inner current control and is used to regulate the terminalvoltage and MPP operation of DFIG (Cp and Cq).

By controlling the active and reactive reference of therenewable resources, the energy deviation of synchronousgenerators due to local and wide area disturbances could bemitigated. Therefore, the task of the wide-area global controlis to monitor and identify the inter area oscillations in theform of transient energy and send the feedback to damp theseoscillation as an augmentation to local controllers. The RL-based WAC using Adaptive Critic Design (ACD) technique isdescribed next.

∑

Governor

Exciter ∑

Δω

Turbine GPower

Network

PSSAVR

+

-+

ΔVref Vt

ΔEf

∆𝑃𝑠𝑠

∆𝑉𝑡𝑠𝑠

∆𝑉𝑡𝑙𝑜𝑐

∆𝑉𝑡

𝑤𝑎𝑐

WAC Global Feedback

PMU

+

Fig. 2: Structure of the proposed WAC integration to synchronous generator

Outer Control Loop

Power Calculation

MPPT/ Droop Control

𝑉𝑜 , 𝐼𝑜

𝐸,𝜔

PMU

𝑃,𝑄

Δ𝑃𝑤𝑎𝑐 ,Δ𝑄𝑤𝑎𝑐

Δ𝑃𝑙𝑜𝑐 ,Δ𝑄𝑙𝑜𝑐 Δ𝑃𝑠𝑠 ,Δ𝑄𝑠𝑠

Wind Generator

Inner Control Loop

Σ

WAC Global Feedback

𝐸,𝜔𝑟

PCC

DFIG𝐶𝑃 𝐶𝑖𝑞

𝐶𝑖𝑑 𝐶𝑄

Fig. 3: Structure of the proposed WAC integration to renewable resource

IV. INTELLIGENT WAC CONSTRUCTION

The architecture deployed in this paper to construct theintelligent WAC is a modified RL-based ACD technique.This technique is, in general, parametric structures capable ofoptimization over time and under conditions of noise and un-certainty [5]. The goal of ACD is to learn the Hamilton-Jacobi-Bellman equation associated with optimal control through acritic network, and find the control signal through actionnetwork. Training of action network is based on selectingsequence of actions that minimize the estimated cost function(J). A common approach is to deploy neural-net to map thenonlinearities of the system identification, control and the costfunction. Here, using Feed Forward Neural Network (FFNN)the network output is computed by inner product between theweight vector W , a state-dependent feature vector Φ(.), and abias coefficients B. The wide-area NN identifier (WANNID),critic NN, and action NN, approximate the dynamics of thesystem, the control action, and the cost function by,

x(t+ 1) = WI(t)TΦI(x(t), u(t)) +BI(t) (15)

uwac = WA(t)TΦA(x(t)) +BA(t) (16)J(t) = WC(t)TΦC(x(t)) +BC(t) (17)

where, sub-scripts I , A, and C denotes WANNID, Action, andCritic networks respectively. Based on RL approach, the cost-to-go function is given as

J(t) =∑∞

k=0γkU(t+ k) (18)

Where, γ ∈ (0, 1] is the discount factor, and U is the utilityfunction used for reward/punishment in terms of RL concept,

Local control loops

x(t)

Critic

Action

J(t)

Power Network Dynamics

+

LC

LC

...

RS

G

+

WANNID

x(t+T)

Utility Function

U(t)

Dynamics with local controls

Energy function

u(t)

uWAC(t)

Fig. 4: Proposed wide-area control scheme

or incremental cost function in Lyapunov stability concept.This function can be represented as,

U(t) = VG(t) + Vw(t) (19)= −xG(t)TQxG(t)− uG(t)TRuG(t)− xw(t)Tuw(t)

Where, Q and R are required to be positive-definite holdingthe condition for Lyapunov stability criteria. Table I shows theNN architecture. Additional details can be found in [7].

V. REAL-TIME SIMULATION AND TEST RESULTS

In order to assess the capability of the proposed methodin comparison to conventional existing damping controllers,modified classic two-area four-machine system has been sim-ulated in real-time using a simulation platform developedin OPAL-RT, RT-LAB (Fig. 5). The model consists of twoareas connected with a tie-line. Area 1 has two synchronousgenerators, each with 690 MW power production and area2 also has two synchronous generators, with 710 MW and690 MW power production. In this paper, the full-order modelof the synchronous generators is used with local controllersof governor, exciter, and PSS. The dynamic parameters ofthe components are provided in [12]. In area 2, a 210 MWwind farm and a 140 MW load is connected to the grid.The parameters of inner and outer control loops of windDFIG is discussed in [3]. In order to damp the inter-areaoscillation, excitation control of generators and droop controlof wind farm is supplemented with the proposed WAC usingthe feedback signal of the WAM. Two case studies are carriedout on the proposed WAC compared to the local controller andconventional WAC.

1) Area 1 Fault: In this case voltage reference of G2 hasbeen increased from 1 pu to 1.3 pu for 300 ms to showtemporary over-voltage, which causes the area 1 generated

TABLE I: Configuration of Neural Networks

NN Inputs (Numberof nodes)

Numberof Delay

Number ofhidden nodes

Outputs (Numberof nodes)

WANNID xi,xj ,ui,uj (24) 2 35 xi,xj (6)Action xi,xj (12) 2 25 ui,uj (6)Critic xi,xj (12) 2 25 Ji,Jj (6)

G4

G3G1

G2

Wind Gen.210 MW

400 MW

710 MW

690 MW

690 MW690 MW

967 MW 1767 MW 140 MW

Area 1 Area 2

1 3

42

56 7

8 910

1112 13

14

Fault 2

Fault 1Vref

Fig. 5: Modified classic two-area four-machine system

power and subsequently tie-line power oscillates from itsoperating point. The purpose of this case study is to showthe frequency regulation and damping capability of the WACaugmented wind generator during inter-area oscillations. Fig. 6presents the dynamic responses of the system with local PSS,WAC with and without wind consideration. As it can beseen, the proposed WAC has provided better performance withrespect to overshoots and damping of inter-area speed andangle oscillations compared to local PSS. Fig. 6 also showsthat the dynamic responses of the synchronous generators hasbeen enhanced through the auxiliary damping control addedwith wind generator inner loop control.

The response of the wind generator along with the proposedWAC is shown in Fig. 7. It can be seen, that the control signalfrom WAC forces the wind generator (operating at MPPTmode) to lower its power output as the tie-line power has in-creased. Also fault itself has not effected the power generationfrom wind farm without the WAC, validating the assumptionof negative load modeling. As the active power from DFIG isreduced, the torque balance for the DFIG no longer exists. Withmechanical torque more than the electromagnetic torque, therotor of the DFIG accelerates. It is to be noted that in this workno additional PSS has been implemented in the DFIG to dampthe DFIG rotor oscillations. As the DFIG speed reaches morethan the rated speed, the pitch controller comes into actionAlso during the grid disturbance the reactive power output ofthe wind generator is increased. This is due to independentcontrol capability of P and Q. Also as the fault is cleared,both the mechanical power and electrical power slowly comeback to the nominal conditions and the torsional oscillationsin DFIG rotor damps out.

2) Fault in Area 2: This test presents a more severe tran-sient case study to evaluate the effectiveness of the proposedRL-based WAC scheme for damping the inter area oscillations.A 300 ms self-clearing short circuit fault has been applied tobus 13 in area 2. The dynamic responses of the rotor angle andarea speed is plotted for four scenarios: 1) with no dampingcontrol, 2) with local synchronous generator PSS, 3) withsupplementary damping wind PSS designed as conventionalWAC [3], and 4) proposed RL-based WAC. The plots in Fig. 8show that the proposed controller outperforms the static lead-lag controller, which is designed based on small signal stability.The proposed scheme is better suited for transient conditionswhere the linear control design is not practical. The intelligentWAC estimates the transient energy function of the system

0 1 2 3 4 5 6 7 8 9 10−5

0

5x 10

−3

ω31

(pu

)

PSSProp. WAC (no wind)Prop. WAC (with wind)

[t(s)]

0 1 2 3 4 5 6 7 8 9 10

0

20

40

δ 31 (

degr

ee)

PSSProp. WAC (no wind)Prop. WAC (with wind)

[t(s)]

0 1 2 3 4 5 6 7 8 9 100

200

400

600

PT

ie−

line (

MW

)

PSSProp. WAC (no wind)Prop WAC (with wind)

[t(s)]

0 1 2 3 4 5 6 7 8 9 100

100

200

300

PW

ind

G. (

MW

)

PSSProp. WAC (no wind)Prop. WAC (with wind)

[t(s)]

0 1 2 3 4 5 6 7 8 9 100

50

100

QW

ind

G. (

MV

ar)

PSSProp. WAC (no wind)Prop. WAC (with wind)

[t(s)]

Fig. 6: Dynamic responses with PSS, Proposed WAC with and without windintegration. (a)Inter-area speed.(b) Inter-area angle. (c)Tie-line power transfer.(d) Wind active power. (e)Wind reactive power.

0 1 2 3 4 5 6 7 8 9 101

1.1

1.2

Spe

ed (

pu)

Rotor SpeedTurque Speed

[t(s)]

0 1 2 3 4 5 6 7 8 9 100

0.5

1

Pm

(pu

)

[t(s)]

Fig. 7: Wind generator response in presnece of proposed WAC

0 1 2 3 4 5 6 7 8 9 10

−0.02

0

0.02

ω31

(pu

)

No controlPSSConv. WACProp. WAC

[t(s)]

0 1 2 3 4 5 6 7 8 9 10−50

0

50

100

δ 31 (

deg.

)

No controlPSSConv. WACProp. WAC

[t(s)]

Fig. 8: Inter-area angle and speed responses with different control scenarios.

including the wind farms (Fig. 9) and through the RL techniquefinds the close to optimal control action to minimize the energyfunction and damp the oscillation energy.

VI. CONCLUSION

In this paper an intelligent energy-based wide-area damp-ing controller is proposed on a wind integrated power grid.Reinforcement learning method has been deployed as anoptimal control for transient stability improvement of thewide-area power system. The wide-area control augmentsthe excitation system of the synchronous generator and localMPPT control of DFIG. Real-time results on the modified 2-area Kundur system shows better responses of proposed RL-based WAC compared to conventional schemes. In addition,

0 1 2 3 4 5 6 7 8 9 10

−0.1

0

0.1

u loc (

pu)

Synchronous Gen. (∆ Vt)

Wind Gen. (∆ P, ∆ Q)

[t(s)]

0 1 2 3 4 5 6 7 8 9 10

−0.1

0

0.1

u wac

(pu

)

Synchronous Gen. (∆ Vt)

Wind Gen. (∆ P, ∆ Q)

[t(s)]

0 1 2 3 4 5 6 7 8 9 100

0.5

1

J (p

u)

Estimated Energy functionActual Energy function

[t(s)]

Fig. 9: Local and WAC control action and energy function estimation

DFIG-based wind generator could enhance the damping ofinter-area oscillation. On the other hand, power modulationof the wind generator caused small oscillation on the rotorside, which could be damped by wind local PSS. Future workinclude evaluating the scalability of the proposed architectureintegrated with a new PSS design for the wind farm.

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