- Distribution Systems and Dispersed Generation CIGRE SC C6 COLLOQUIUM Yokohama 2013
S3-6
A Hardware-in-the-Loop Test Platform for the Real-Time State Estimation of Active Distribution Networks using Phasor Measurement Units M. Paolone, M. Pignati, P. Romano, S. Sarri, L. Zanni, R. Cherkaoui École Polytechnique Fédérale de Lausanne (EPFL) Abstract: The paper describes the development and the performance assessment of a Hardware-in-the-Loop (HIL) test platform built as a proof-of-concept of a sub-second State Estimator (SE) of Active Distribution Networks (ADNs). The SE relies on the availability of data coming from Phasor Measurement Units (PMUs). The paper firstly illustrates the architecture of the experimental setup, then, by using a SE process developed by the Authors, based on the use of Iterated Kalman Filter (IKF), presents the experimental assessment of the time latencies of the whole process together with the SE accuracy assessment. Keywords: Active Distribution Network (ADN), Real-Time State Estimation (RTSE), Phasor Measurement Unit (PMU), Real-Time Simulation (RTS), Phasor Data Concentrator (PDC).
1.
improve the quality of the system SE and assessed its performances for different types of measurements (conventional or PMUs) and parameter errors. The works presented in [10], [11] have illustrated an implementation of PMU communication models, to approximate the delay of phasor measurements sent from PMUs to PDC and control signals from the Wide Area Monitoring and Control (WAMC) system back to the substation switches. In [12] Armenia et al. provided a flexible and lower-cost alternative to system operators and transmission owners, called Flexible Integrated Phasor System-FIPS, which can be used for a variety of applications. In [13] Ouellette et al. used a RTS to evaluate the performance of PMUs, whereas in [14] Golshani et al. presented an investigation on PDC features and a preliminary study based on the deployment of PMUs, in order to provide for a number of applications, related to monitoring, protection and control in power systems. However, none of the above-listed contributions analyzed the timing and accuracy of a SE in RT taking advantage of the a-priori knowledge of the simulated system state. Therefore, this paper has two aims. The first is to describe a RT HIL simulation platform specifically defined in order to emulate the behavior of a realistic ADN. The second aim is to evaluate the accuracy and the time-determinism of a RTSE [4] that uses data streamed by PMU prototypes suitably designed for ADN applications [15] and physically coupled with the RTS and with a PDC. The structure of the paper is the following: Section 2 describes the setup that was used to perform the RTSE, namely the PMUs, the PDC and the RTSE. Section 3 presents the RT power network digital simulator that was adopted in the context of this paper, whereas Section 4 illustrates the experimental setup and the RTSE performances assessment in terms of latency and accuracy. Finally, Section 5 concludes the paper with the final remarks.
INTRODUCTION
As known, the concept of Active Distribution Networks (ADNs) refers to electrical grids where the energy resources (i.e., distributed generation, storage, loads, etc.) are actively controlled by a suitable Energy Management System (EMS), in order to achieve specific operation objectives (i.e., [1], [2]). Typical objectives refer to optimal voltage control, management of line-congestion, fault detection and location, post-fault management, local load balance, losses minimization, etc. All these functionalities are significantly improved if the knowledge of the network state is available (e.g., [3]). As these functionalities are deployed in time frames that vary between few hundreds of ms (fault management) to few tens of seconds (voltage control and line congestions), they might require the knowledge of the network state with relatively high refresh rates. In this respect, it is worth observing that typical refresh rates of State Estimation (SE) processes are in the range of a few minutes and, therefore, cannot be used to perform the above-mentioned Real-Time (RT) functions. It is therefore crucial the development of the so-called “Real–Time State Estimators” (RTSEs) in order to achieve high-rates of the power network state assessment. In this respect, the time scales for the whole execution of the RTSE process should be in the order of few tens/hundreds of milliseconds. A drastic reduction of the computational burden of RTSEs can be achieved using Phasor Measurement Units (PMUs) (e.g., [4], [5]). The data that is measured by PMUs can be acquired and stored in a RT database (provided by Phasor Data Concentrators – PDCs), suitably coupled with the RTSE. However, since it is not easy to verify the SE accuracy in the real–field as the ‘true’ network state is unknown, one of the possibilities to overcome this limitation is represented by the use of Real-Time Simulators (RTSs). Indeed, this technology allows the numerical computation of the network true state and to accurately reproduce general power networks conditions, including those that may be dangerous for the physical system [6-8]. In [9] Valverde et al. presented a laboratory setup, based on a RT update of a database with PMU measurements, in order to
2.
REAL-TIME STATE ESTIMATION SETUP
Based on the scheme shown in Fig. 1, the RTS generates three-phase voltage and current analog signals of the monitored network buses. These signals are acquired by n PMUs that estimate the voltage and current synchrophasors, encapsulate them according to the IEEE Std. C37.118.2-2011 [16] and stream the relevant frames through the telecom network composed by a single switch.
Contact Address: Mario Paolone. E-mail:
[email protected]
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has been presented together with its implementation into a PMU prototype. The proposed algorithm belongs to the category of DFT-based synchrophasor estimation algorithms. As described in [15], it has been taylored for its deployment into ADNs and it is composed of four main steps. The first two sequentially apply a suitable windowing function w(n) to the acquired signal s(n), n [0,N-1], and perform a DFT-analysis of the windowed input signal producing the discrete spectrum S(k), k [0,N-1], respectively. The third one realizes a first estimate of the signal synchrophasor by means of an interpolated-DFT. In particular, as the real frequency value f1 may fall between two subsequent DFT bins, it can be expressed as a function of the discrete DFT frequency discretization step f = 1/T as
The data frames are received in a GPS-synchronized workstation where both the PDC 1 and the SE run. Once the frames hit the workstation, they are decapsulated and time-aligned by the PDC, which saves every complete dataset in a local historian. This process is executed at the same rate the PMUs stream the data. At the same time, a MATLAB-based service sends a query to the PDC web service, to get the most recent set of complete measurements. The dataset of interest is received as an XML file, containing all the information needed for the SE. At this point, the SE can return the estimated state of the RT simulated network. Due to: the time needed by (i) the querying process, (ii) the reading of the XML file, and (iii) the SE algorithm itself, this process runs at lower speed compared to the one of the PDC. In order to achieve a direct comparison between the estimated and the true state that is saved by the RTS, both of them are time-stamped using the GPS-UTC reference.
f1 = ( k1 + bin ) f
(1)
where bin is the deviation of f1 from the relative DFT maximum k1 f. As shown in [20], with the hypothesis of adopting a sampling frequency fs much greater than the fundamental tone frequency f0, the discrete spectrum S(k) can be linearly interpolated in the neighborhood of k1. Therefore the bin can be expressed as
bin = ±
2 1+
(2)
where is the ratio between the highest and second highest tone magnitudes of the DTF spectrum. Based on the bin estimate, a first estimation of the synchrophasor can be given [20]:
A1 = 2 S ( k1 ) 1
Fig. 1.
sin (
= S ( k1 )
bin 2 )
bin )
bin
(3) (4).
Finally, the fourth step makes an iterative correction of the harmonic interference between the DFT bins of the positive and negative image of the spectrum, as explained in [21]. The algorithm has been fully deployed into a National Instruments Compact-Rio embedded control and acquisition system characterized by (i) a reconfigurable Virtex-5 LX110 FPGA and (ii) a realtime micro-controller for the streaming of the estimated synchrophasor based on the protocol specified in [16]. The experimental validation of the developed PMU, based on the limits imposed in [19], has been successfully performed in both static and dynamic conditions as shown in [15]. In particular, this PMU has demonstrated to satisfy every accuracy requirements of class-P PMUs.
Real-Time State Estimation Setup.
2.1 PMU-BASED DISTRIBUTED METERING SYSTEM In the proposed HIL setup the monitoring system adopted to generate the synchrophasors dataset is only based on PMUs (see Fig. 1). This technology is experiencing a fast evolution triggered by an increasing number of power system applications [17]. Among them, the most challenging ones refer to ADNs, where PMUs may represent fundamental monitoring tools. Compared to transmission networks, ADNs are more demanding in terms of PMU performances. These conditions refer to the reduced line lengths and limited active power flows that result into very small phase differences between node voltage phasors (generally in the order of tens of mrad or less). Additionally, distribution networks are characterized by distortion levels and dynamic behaviors more important than those of transmission networks, like, for instance, the electromechanical transients subsequent to an islanding maneuver [18]. These characteristics, with reference to the use of node voltage synchrophasors for the network SE, lead to the necessity of the development of extremely accurate PMUs, characterized by accuracy levels much higher than the limits specified in the latest IEEE Std. C37.118.1-2011 [19]. Based on the above considerations, in [15] a novel and extremely accurate algorithm for the synchrophasors estimation 1
bin (1
2.2 PHASOR DATA CONCENTRATION As known a PDC collects synchrophasor data and other quantities (i.e. frequency, rate of change of frequency, powers, etc.) estimated from geographically distributed PMUs and transmits them to other applications like simple visualization tools or others that perform more sophisticated functions such as protection, SE, voltage control, etc. [14]. For RT applications the most important function that a PDC has to provide is to aggregate and time-align synchrophasor data from multiple PMUs and feed them with minimum time latency. In general, PDCs buffer data for a short time period waiting for all the measurements with the same time-stamp to reach the PDC itself. Once a measurement set, characterized by the same
In the HIL setup the OpenPDC software platform has been used.
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1) Time update (prediction) part: The “a-posteriori” estimated state of the previous time step xk-1 is used as the “a-priori” estimated state xk,0 for the next time step. The initial error covariance matrix is calculated using the error covariance of the previous time step and the process noise covariance matrix Q. 2) Measurement update (correction) part: When a first estimation of the system state is obtained, then an iterative process starts. The iteration loop includes the calculation of the Kalman Gain and the update of the system state and the error covariance matrix. The correction process stops when the absolute differences of the phase-to-ground voltage phasors between two consecutive iterations become lower than a given threshold.
time-stamp, is complete, it is forwarded to the desired application. Indeed when dealing with real networks and devices, measurements may be delayed due to non-determinism in the data streaming, network traffic, different reporting rates, etc. In this case there is no way to exactly determine the amount of time the data packet takes to be prepared. In our test setup the solution is to set a time-out variable representing the amount of time each buffer has to actively wait for the rest of phasors with the same time-stamp. When the time-out is up, existing data set is forwarded without waiting for the missing measurements to arrive. This solution adds a ceiling limit in the total delay that can be experienced always ensuring to forward the available measurements in an acceptable time range and, thus, increasing the determinism of the process [11].
2.3 REAL-TIME STATE ESTIMATION Existing literature on RTSE (e.g., [22-24]) focuses on the acquisition of real measurements and on the RT network monitoring. However, the SE part has not been performed in RT, but in the so-called “offline mode”. In this paper the whole process, including the SE, has been performed in RT conditions, in order to characterize the time-determinism of the whole process. Fig. 2 shows the RTSE process adopted in this paper, based on what was proposed in [4], in order to obtain the system state and compare it with the true network state. V,
PDC P, Q
Three-phase State Estimation using Iterated Kalman FIlter
Time update (prediction)
Network topology
1) Kalman Gain: Kk,i=Pk,i-1HkT(HkPk,i-1HkT+Rk)-1
2) Initial error covariance value: Pk,0=Pk-1+Q
3) Error covariance update: Pk,i=(I-Kk,iHk)Pk,i-1
2) State update: xk,i=xk,i-1+Kk,i[zk-hk(xk,i-1)] max
(
(
k ,i
max V
V, : voltage magnitude and phase
h(x): non-linear measurement function
P, Q: injected/absorbed power
H: linearized measurement Jacobian
n: number of buses
R: measurement noise covariance matrix
k ,i
k ,i 1
V
)
