Search

This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.

Recent expansion of inverter-based renewable generation (RG) has introduced unprecedented stability issues. One major challenge stems from tripping RGs offline during disturbances based on predefined operation thresholds (ride through curves) on voltage magnitudes and frequency at the point of common coupling (PCC). The current practice uses a single set of thresholds for the whole year across all RGs in the system. This could result in loss of these resources during critical times, particularly endangering system voltage security, i.e a significant reduction in the load margin (LM). On the other hand, relaxing these limits could result in them staying online even during islanding scenarios, which is a safety concern to the restoration crew. Thus, an approach to find minimal relaxations to these limits to enhance voltage security is proposed here. This process first constructs an optimization model aiming to minimize adjustments on RG voltage limits to reach anticipated LM increase and then solves it with a multi-stage screening method including base power-voltage (P-V) curve acquisition, critical RGs identification, successive screenings of candidate solutions based on sensitivities, and optimal solutions validation. This largely increases the scalability of the algorithm, which is demonstrated on the IEEE 14-bus, 118-bus systems, and a 500-bus system.

This article presents a novel data recovery framework for missing synchrophasor measurements. The imputation accuracy for the existing data recovery methods is significantly reduced when there are consecutive data losses across multiple data streams. Besides, the recovered data do not necessarily meet the physical constraints of the power grid. To tackle these issues, a regularized low rank tensor completion (LRTC) method is proposed to incorporate the domain knowledge (e.g., Kirchhoff’s voltage and current laws, and three phase circuit relationships) as the regularization terms in order to efficiently exploit the data inter-dependencies for a better recovery. We leverage the tensor decomposition and completion as powerful tools to extract the latent structures of phasor measurement unit (PMU) data for the recovery process. Specifically, we first construct the tensor model of the PMU data and then formulate the LRTC problem as a rank minimization by leveraging the low rank property of the PMU measurements and adding the regularization terms into the LRTC problem in order to improve the imputation accuracy. An efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the regularized LRTC problem. The experiments using the real PMU dataset show that the proposed approach exhibits better imputation accuracy, compared with the conventional matrix completion methods.

This article proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These sub-networks can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during power systems contingencies, thereby mitigating disasters. Towards identifying the best microgrid candidates, this work formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and distributed energy resources; and presents a solution methodology using the sample average approximation (SAA) technique. Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with- a) an upper bound on the probability that the identified microgrids are supply-deficient, and b) a lower bound on the objective value for the true optimization problem. Performance of the ODNP formulation is illustrated through case-studies on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost. Virginia Tech Open Access Subvention Fund Published version This work was supported in part by the Virginia Tech Open Access Subvention Fund.

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system swing dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances. Accepted version

Utilities across the world are seeing increased penetration of inverter-based renewable generation (RG) in their systems. These RGs have ride through curves programmed in them which define operating conditions that need to be satisfied during electrical disturbances. If they are violated, the RGs are tripped offline. In systems with large amounts of RGs tightly coupled electrically, there can be disturbances that cause sudden loss of a large number of RGs, which will then considerably exacerbate the stability of the system. Such a phenomenon is not captured by the existing direct methods for TSA. In this paper, by treating RG-rich systems as non-linear switched systems as opposed to the traditional approach of treating this tripping phenomenon as an instability, an approach utilizing multiple low voltage ride through constrained stability regions (CSRs) is proposed for capturing unstable fault clearing times. The CSRs are estimated through Lyapunov functions found using sum of squares programming. The effectiveness of the proposed technique is demonstrated using a three-machine system.

Phasor Measurement Unit measurement data have been widely used in nowadays power system applications both in steady state and dynamic analysis. The performance of these applications running in utilities' energy management system depends heavily on an accurate positive sequence power system model. However, it is impractical to nd this accurate model with transmission line parameters calculated directly with the PMU measurements due to ratio errors brought by instrument transformers and communication errors brought by PMUs. Therefore, a methodology is proposed in this paper to estimate the actual transmission lines parameters throughout the whole system and, at the same time, calibrate the corresponding instrument transformers. A PMU positive sequence measurement error model is proposed targeting at the aforementioned errors, which is applicable to both transposed and un-transposed transmission lines. A single line parameters estimation method is designed based on Least Squares Estimation and this error model. This method requires only one set of reference measurements and the accuracy can be propagated throughout the whole network along with the topology acquired by the introduced Edge-based Breadth-rst Search algorithm. The IEEE 118-bus system and the Texas 2000-bus system are used to demonstrate the effectiveness and efciency of the proposed method. The potential for deployment in reality is also discussed. This work was supported by the Virginia Tech Open Access Subvention Fund.

With increased share of renewable energy sources in the generation mix, there is a need for reliable base generation that can meet the grid requirements without adversely affecting the environment. Combined heat and power (CHP) plants are $a$ promising high-efficiency distributed energy resource (DER) that can be used to improve renewable integration while simultaneously providing a host of benefits to the grid. Owing to their geographic distribution and long hours of operation, these units provide an improvement in grid resiliency during emergencies, deferred transmission system upgrades and reduced emissions. This work explores some barriers that impede grid participation of CHPs and proposes a multi-objective optimization approach for better involvement of distributed industrial CHP units at the wholesale energy market level. The CHPs considered are not third party generating units that produce power to meet their own requirements. The effect of including CHP DERs in the market, thermal storage addition and the CHP participation levels are studied in detail.

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

It is well known that physical interdependencies exist between networked civil infrastructures such as transportation and power system networks. In order to analyze complex nonlinear correlations between such networks, datasets pertaining to such real infrastructures are required. However, such data are not readily available due to their proprietary nature. This work proposes a methodology to generate realistic synthetic power distribution networks for a given geographical region. A network generated in this manner is not the actual distribution system, but its functionality is very similar to the real distribution network. The synthetic network connects high voltage substations to individual residential consumers through primary and secondary distribution networks. Here, the distribution network is generated by solving an optimization problem which minimizes the overall length of the network subject to structural and power flow constraints. This work also incorporates identification of long high voltage feeders originating from substations and connecting remotely situated customers in rural geographic locations while maintaining voltage regulation within acceptable limits. The proposed methodology is applied to the state of Virginia and creates synthetic distribution networks which are validated by comparing them to actual power distribution networks at the same location.

Frequently recurring transient faults in a transmission network may be indicative of impending permanent failures. Hence, determining their location is a critical task. Large scale deployment of Phasor Measurement Units (PMU) in modern power grids has given utilities access to precise measurements at a high temporal resolution that may be utilized for estimating fault location. This paper proposes a novel image embedding aided deep learning framework called DeVLearn for faulted line location using PMU measurements at generator buses. Inspired by breakthroughs in computer vision, DeVLearn represents measurements (one-dimensional time series data) as two-dimensional unthresholded Recurrent Plot (RP) images. These RP images preserve the temporal relationships present in the original time series and are used to train a deep Variational Auto-Encoder (VAE). The VAE learns the distribution of latent features in the images. Our results show that for faults on two distinct lines in the IEEE 68-bus network, DeVLearn is able to project PMU measurements into a two-dimensional space such that data for faults at different locations separate into well-defined clusters. This compressed representation may then be used with off-the-shelf classifiers for determining fault location. The efficacy of the proposed framework is demonstrated using local voltage magnitude measurements at two generator buses.

Widespread integration of distributed energy resources (DERs) in the power distribution system has left the conventional protection schemes ineffective in adequately identifying and isolating a fault. Hence, modern protection schemes should be able to adapt to these variations and modify the protective device's settings to ensure effective protection coordination. Although prior works have addressed this issue using extensive communication-based methods (centralized or peer-to-peer), the actual implementation of these methods is economically less viable shortly considering the present infrastructure. However, with the advent of modern digital relays, protection schemes can be made adaptive yet keeping them decentralized and hence, easily implementable and deployable. The work proposed in this paper aspires to achieve this goal by utilizing the commonly used protective devices such as directional overcurrent relays in the power distribution systems by storing versatile setting groups enabling better observability of the relays towards fault identification and isolation in the absence of extensive communication links.

Large-scale integration of Distributed Energy Resources (DERs) in power distribution networks has raised concerns for distribution system's protection coordination. Due to the bi-directional flow of power, Directional Overcurrent Relays (DOCRs) are being widely used in distribution systems. The pickup settings and time dial settings of these DOCRs needs to be modified with changing topologies and added DERs, to ensure proper selectivity and sensitivity towards fault identification and isolation. However, the setting groups that can be stored in DOCRs to accommodate these numerous topologies are limited. The work in this paper discusses the implementation of K-Medoids clustering technique to cluster the numerous grid-connected and islanded topologies of a distribution system based upon the various pickup currents and operating time indices. The testing results generated on a modified IEEE 13 node test feeder system demonstrate the effectiveness of the methodology over prior works.

Standard power system models are parameter dependent differential-algebraic equation (DAE) type. Following a transient event, voltage collapse can occur as a bifurcation of the transient load flow solutions which is marked by the system trajectory reaching a singular surface in state space where the voltage causality is lost. If a fault is expected to cause voltage collapse, preventive control decisions such as changes in AVR settings need to be taken in order to get enhance the system stability. In this regard, the knowledge of sensitivity of critical clearing time (CCT) to controllable system parameters can be of great help. The quasi-stability boundary of DAE systems is more complicated than ODE systems where in addition to unstable equilibrium points (UEP) and periodic orbits, singularity plays an important role making the problem challenging. The stability boundary is then made up of a number of dynamically distinct components. In the present work, we derive the expression for CCT sensitivity for the phenomenon where the critical fault-on trajectory intersects the singular surface itself which is one such component forming the stability boundary. The results are illustrated for a small test system in order to gain visual insights., To be presented in IEEE PES General Meeting 2020 in Montreal

Large-scale integration of distributed energy resources (DERs) in the power distribution system has challenged the conventional notion of a radial system with unidirectional power flow. The bi-directional flow of power from the consumer end raises concern for the protection systems in place to detect islanding phenomenon. However, the current standards/practices do not allow the DERs in an electric distribution system to stay connected in islanded mode except in a micro-grid mode of operation, which has designated controllers to maintain the stability of an islanded system. The work presented in this paper proposes the implementation of a technique to assign distinct priority levels to various critical loads in real-time based on voltage sensitivity factor. The proposed module decides the shedding order of these loads in real time depending upon system operating conditions in order to sustain an island from the point of disconnection stabilizing the power balance within the island.

Inverter based renewable generation (RG), especially at the distribution level, is supposed to trip offline during an islanding situation. However, islanding detection is done by comparing the voltage and frequency measurements at the point of common coupling (PCC), with limits defined in the form of ride-through curves. Current practice is to use the same limit throughout the year independent of the operating conditions. This could result in the tripping of RG at times when the system is already weak, thereby posing a threat to voltage security by heavily limiting the load margin (LM). Conversely, heavily relaxing these limits would result in scenarios where the generation does not go offline even during an islanding situation. The proposed methodology focuses on optimizing low-voltage ride-through (LVRT) settings at selective RGs as a preventive control for maintaining a desired steady-state voltage stability margin while not sacrificing dependability during islanding. The proposed process is a multi-stage approach, in which at each stage, a subset of estimated poor-quality solutions is screened out based on various sensitivities. A full continuation power flow (CPFLOW) is only run at the beginning and in the last stage on a handful of remaining candidate solutions, thereby cutting down heavily on the computation time. The effectiveness of the approach is demonstrated on the IEEE 9-bus system., Accepted to be published in the Proceedings of the 2020 IEEE PES General Meeting, August 2-6 2020, Montreal, Quebec, Canada

Growing penetration of renewable generation in power systems brings more challenges in transient stability assessment using direct methods. One of the reasons is the inability to assess the risk of instability brought by tripping of a large amount of RGs due to violation of their ride through curves. In this paper, a scalable approach is proposed based on the Lyapunov functions family to estimate the stability region under ride through constraints. An inner polytopic approximation to the feasibility region is proposed to convexify the overall problem. The acquired constrained stability region estimate helps capture trajectories that trigger undesirable tripping of renewable generation. A 2-machine system is used to visualize its effectiveness., Comment: Accepted to be published in the Proceedings of the 2020 IEEE PES General Meeting, August 2-6 2020, Montreal, Quebec, Canada

Wide area measurement system relies on phasor measurement unit (PMU) data to monitor, protect, and control high-voltage transmission networks. However, errors in instrument transformers (ITs) located at the inputs of a PMU can significantly degrade its output quality. This study proposes two methodologies for voltage and current transformers calibration using PMU data. The first method calibrates ITs using one good quality voltage measurement located at a tie-line. This method tolerates errors in both the ITs (which are to be estimated) as well as the PMUs. The second method attains the same objective as the first one, with the additional constraint that some portion of the data is unusable. Thus, the second method can be used even when the incoming data is intermittent.

As large scale deployment of smart devices in the power grid continues, research efforts need to increasingly focus on efficient communication of generated information. This paper describes a strategy for static routing and scheduling of messages in a multi-hop wireless Smart Grid Neighborhood Area Network (NAN) with multiple source nodes and a common set of destinations or gateways. The problem is formulated as a Mixed Integer Linear Program (MILP) and solved using commercial optimization solver CPLEX. Feasibility of the scheme is demonstrated using different network models, constraints, message injection rates, and initial conditions. It is shown that the proposed approach can be used to generate an optimal link schedule for collecting user-generated bids in a transactive energy market in the least possible time. It is also shown that the methodology is applicable to multiple destination nodes and that their location affects message delivery time.

With the growth of renewable generation (RG) and the development of associated ride through curves serving as operating limits, during disturbances, on violation of these limits, the power system is at risk of losing large amounts of generation. In order to identify preventive control measures that avoid such scenarios from manifesting, the power system must be modeled as a constrained dynamical system. For such systems, the interplay of feasibility region (man-made limits) and stability region (natural dynamical system response) results in a positively invariant region in state space known as the constrained stability region (CSR). After the occurrence of a disturbance, as it is desirable for the system trajectory to lie within the CSR, critical clearing time (CCT) must be defined with respect to the CSR instead of the stability region as is done traditionally. The sensitivity of CCT to system parameters of constrained systems then becomes beneficial for planning/revising protection settings (which impact feasible region) and/or operation (which impact dynamics). In this paper, we derive the first order CCT sensitivity of generic constrained power systems using the efficient power system trajectory sensitivity computation, pioneered by Hiskens in [1]. The results are illustrated for a single-machine infinite-bus (SMIB) system as well as a multi-machine system in order to gain meaningful insight into the dependence between ability to meet constraints, system stability, and changes occurring in power system parameters, such as, mechanical power input and inertia., 11 pages, Accepted for Publication in IEEE Transactions on Power Systems. arXiv admin note: text overlap with arXiv:1902.08893

We study the resilience of real world power grids to targeted adversarial attacks. Prior blackouts have shown that failures in the power grid can cascade, starting from a single failure, leading to a large number of failed nodes. In this paper, we study the problem of identifying a set of k critical nodes, whose failure/attack leads to the maximum number of tripped nodes. There has been a lot of work on this problem, but it has been mainly restricted to simple networks and failure models with either steady state analysis or DC power flow. In this paper, we perform AC power flow based transient analysis on a detailed power grid model. We find that a simple greedy approach yields node sets with higher criticality than a degree based approach, which has been suggested in many prior works. Furthermore, we observe that the cascades exhibit a non-monotonic behavior as a function of k.

A phasor measurement unit placement scheme is developed in this article that ensures complete observability of large systems while reducing the computational burden of optimization. Redundancy in measurement of the critical buses of the network that are identified based on system studies and/or topologies is also provided by the proposed methodology. The community-based islanding approach initially partitions the system into smaller islands. Placement of phasor measurement units in these islands is then computed using integer linear programming. A bound is also developed to find the maximum error from a global optimal solution. The proposed technique is applied to standard IEEE systems as well as on more realistic power system networks. The results indicate that the proposed technique optimizes the benefits of having phasor measurement units at strategic locations of a large power system network without the associated computational burdens.

This paper takes advantage of wide-area measurements to propose a centralized nonlinear controller that acts on power system stabilizers (PSSs), to cooperatively increase the damping of problematic small-signal oscillations all over the system. Details are given to utilize existing stabilizers while causing minimum changes to the equipment and warranting improvement, or at least no detriment, of current system behavior. A set of gains is designed by linear state feedback control to respond to representative system conditions, and these are coordinated with an equal number of decision trees in charge of scheduling by switching. The resulting nonlinear controller is implemented on a model that resembles the characteristics of the Central American System, exemplifying its feasibility to be applied in large systems. The effect of time delay in the control loop is assessed, and the limits for tolerance to latency in this application are determined.

Phasor measurement units (PMUs) play an important role in the wide-area monitoring and protection of modern power systems. Historically, their deployment was limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem was to find minimum number of devices, which when carefully placed throughout the network, maximised observability subject to different constraints. Due to improvements in relay technology, digital relays can now serve as both relays and PMUs. Under such circumstances, the substation installations consume the largest portion of the deployment cost, and not the devices themselves. Thus, for minimising cost of synchrophasor deployment, number of substation installations must be minimised. This study uses binary particle swarm optimisation to minimise number of substations in which installations must be performed for making all voltage levels observable, while being subject to various practical constraints. Standard IEEE systems have been used to explain the technique. Finally, a large-scale network of Dominion Virginia Power is used as the test bed for implementation.

Uncalibrated instrument transformers present at the inputs of phasor measurement units (PMUs) can significantly degrade their outputs. This also causes problems in downstream applications that use PMU data. This paper presents a method for calibrating voltage transformers online using synchrophasor measurements. The proposed approach aims to find the optimal locations where good quality measurements must be added in order to bring the calibration error of all the measurements below a predefined threshold. The IEEE 118-bus system, the IEEE 300-bus system, and a 2383-bus Polish system have been used as the test systems for this analysis. The advantage of the proposed approach is its effectiveness and robustness.

This paper introduces a dynamic reduction strategy that preserves the original interarea modes of oscillation and produces a physical model of the power system. Although the proposed method is designed to be applicable to any multiarea system, a real system of considerable size is used in this paper as a practical example. The strategy is applied to an academic version of the Central American interconnected power system, consisting of 130 machines and six control areas. The result is a more computationally manageable model where all generators are actually present in the real system, allowing a model that is familiar to its users, and can be implemented in a format suitable to any commercial power system analysis software.

The development of real-time Phasor Measurement Units (PMUs) has made it possible to monitor and record dynamic information in power systems. PMUs are becoming an integral element of power systems as they provide synchronized measurements of real-time phasors of voltage and currents. Research has been carried out to develop a PMU model that can be easily integrated into most power system simulations. This paper tests a PMU model developed in LabVIEW using real-time data by integrating with a data acquisition (DAQ) system that is compliant with LabVIEW. This model is configurable and provides a great experimental setup for students to gain knowledge about PMUs and synchronized phasor measurements.

Phasor Measurement Units (PMUs) have been used for several years to record information on power system, especially in transmission system. Their ability to synchronize to Global Positioning System (GPS) time, enables them to make accurate measurements of angle differences. However, due to their cost and the small angle differences they are rarely used in distribution system. The recently developed micro Phasor Measurement Unit (μPMU) claims a capability of micro-second resolution and milli-degree accuracy, which is 100 times the resolution of traditional transmission-type PMU. Therefore, μPMUs could be an extremely useful tool for distribution systems and microgrids. To better understand the advantages of μPMUs compared to traditional PMUs, this paper provides the experimental results of a comparative testing of these two devices based on the C37.118.1 standard for Synchrophasor [1].

Modern power grids are characterized by the increasing number of active demand users, smart meter deployment, and distributed energy sources. At distribution level, a distributed management of the electricity transactions seems to be more suitable than a centralized structure because of the large number of the potential participants. A short-term transactive energy scheme is useful to solve the imbalance caused by the randomness characteristics inherited individual generation and consumption patterns. Therefore, effective devices that allows to quantify the electricity transactions on short-term basis are required for a transactive energy scheme. This paper describes the structure of a short-term transactive energy environment based on auctions and presents the design of an efficient metering unit that allows quantifying power transactions for a 5 minutes market.

There has been significant increase in penetration of renewable generation (RG) sources all over the world. Localized concentration of many such generators could initiate a cascade tripping sequence that might threaten the stability of the entire system. Understanding the impact of cascade tripping process would help the system planner identify trip sequences that must be blocked in order to increase stability. In this work, we attempt to understand the consequences of cascade tripping mechanism through a Lyapunov approach. A conservative definition for the stability region (SR) along with its estimation for a given cascading sequence using sum of squares (SOS) programming is proposed. Finally, a simple probabilistic definition of the SR is used to visualize the risk of instability and understand the impact of blocking trip sequences. A 3-machine system with significant RG penetration is used to demonstrate the idea., To appear in PESGM 2018, Portland, OR, 2018

Many cascading methodologies tend to be steady-state load flow analyses to avoid the computational burden of examining cascades from a dynamic standpoint. Load flow, however, has the drawback of losing information about generator transient angular stability. On the other hand, time-domain simulations can accurately identify angular stability issues at the cost of extra computational burden. This work aims to incorporate time-domain angular stability in load-flow through machine learning. A Random Forest (RF) machine learning classifier is trained on generator transient angular instability data obtained from time-domain simulations with machine features derived from steady-state load-flow analysis. RFs require very little computation once trained and can have high predictive accuracy, resulting in a classifier that can be used in steady-state cascading methodologies to predict transient stability.

In this paper, a strategy for online detecting and warning of wide area power system islanding disturbances using real time PMU data is introduced and tested. The proposed method is composed of an intelligent decision tree algorithm for islanding detection and identification with the aid of an islanding database made of islanding simulations, real world islanding records, and system geographical/topology information. The practical utilization adopting this method in the Dominion Virginia Power system and the functional module design are also introduced.