Hesen Liu

Power System Transient Stability Assessment Based on Big Data and the Core Vector Machine

In this paper, an online power system transient stability assessment (TSA) problem is mapped as a two-class classification problem and a novel data mining algorithm the core vector machine (CVM) is proposed to solve the problem based on phasor measurement units (PMUs) big data. First of all, an offline training, online application framework is proposed, which contained four sub-steps, namely features selection, offline training, online application, and assessment evaluation. First, 24 features are selected to present the system status. Then in the offline training procedure, the PMU big data is generated by time domain simulation, and a CVM model is trained and tested. In the online application procedure, an interface between PMU data center and feature calculation program is set up to collect real time specific PMU big data and the CVM trained is applied to the TSA problem. Last but not least, the evaluation indices are calculated. Compared with other support vector machines, the proposed CVM based assessment algorithm has the higher precision, meanwhile, it has the least time consumption and space complexity. As long as online PMU big data are received, TSA can be done simultaneously. Case studies on the IEEE New England 39-bus system, and real systems in China and the U.S., exhibit the speed and effectiveness of the proposed algorithm.

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. This paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. The results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.

Distributed Data Analytics Platform for Wide-Area Synchrophasor Measurement Systems

As synchrophasor data start to play a significant role in power system operation and dynamic study, data processing and data analysis capability are critical to wide-area measurement systems (WAMSs). The frequency monitoring network (FNET/GridEye) is a WAMS network that collects data from hundreds of frequency disturbance recorders at the distribution level. The previous FNET/GridEye data center is limited by its data storage capability and computation power. Targeting scalability, extensibility, concurrency, and robustness, a distributed data analytics platform is proposed in this paper to process large volume, high velocity dataset. A variety of real-time and non-real-time synchrophasor data analytics applications are hosted by this platform. The computation load is shared with balance by multiple nodes of the analytics cluster, and big data analytics tools such as Apache Spark are adopted to manage large volume data and to boost the data processing speed. Future data analytics applications can be easily developed and plugged into the system with simple configuration.

Real-time power system electromechanical mode estimation implementation and visualization utilizing synchrophasor data

Wide area measurement systems (WAMS) could provide enough information to estimate the electromechanical modes which characterize the dynamic properties of the power grid. Utilizing the measured synchrophasor data from frequency monitoring network (FNET/GridEye), this paper proposes an approach to estimate the oscillation frequency and damping regardless of ring-down or ambient condition in real-time environment. The empirical mode decomposition (EMD) is used to detrend the frequency signal, and an auto-regressing moving-average (ARMA) model is used to describe the time series data and the modified yuler walker (MYW) algorithm is used to estimate the AR parameters. Additionally, this paper describes the details of the implementation of a real-time monitoring website showing the estimated dominant frequency and damping from the four interconnections in North America power grid.

A Distribution Level Wide Area Monitoring System for the Electric Power Grid–FNET/GridEye

The wide area monitoring system (WAMS) is considered a pivotal component of future electric power grids. As a pilot WAMS that has been operated for more than a decade, the frequency monitoring network FNET/GridEye makes use of hundreds of global positioning system-synchronized phasor measurement sensors to capture the increasingly complicated grid behaviors across the interconnected power systems. In this paper, the FNET/GridEye system is overviewed and its operation experiences in electric power grid wide area monitoring are presented. Particularly, the implementation of a number of data analytics applications will be discussed in details. FNET/GridEye lays a firm foundation for the later WAMS operation in the electric power industry.

A survey on next-generation power grid data architecture

The operation and control of power grids will increasingly rely on data. A high-speed, reliable, flexible and secure data architecture is the prerequisite of the next-generation power grid. This paper summarizes the challenges in collecting and utilizing power grid data, and then provides reference data architecture for future power grids. Based on the data architecture deployment, related research on data architecture is reviewed and summarized in several categories including data measurement/actuation, data transmission, data service layer, data utilization, as well as two cross-cutting issues, interoperability and cyber security. Research gaps and future work are also presented.

Adaptive wide-area damping control using measurement-driven model considering random time delay and data packet loss

One of the main drawbacks of the existing wide-area damping controller (WADC) that are usually tuned based on several selected typical operating conditions, is its limited adaptability to continuous variations in operating conditions. An adaptive WADC employing the lead-lag structure using measurement-driven model is proposed in this paper. The state subspace model is identified online using ambient data or ring-down data to represent system oscillatory behaviors. The parameters of the lead-lag time constants can be updated based on the new residue derived from the identified model, while the new control gain can also be determined based on the identified model to achieve maximum damping ratio. Moreover, a delay compensator adopting the lead-lag structure and the quadratic interpolation algorithm are utilized to handle random time delay and data packet loss, respectively. The effectiveness of the proposed adaptive WADC is validated by the case study in the two-area four-machine system.

The design and implementation of the enterprise level data platform and big data driven applications and analytics

In order to improve the capability of utilizing big data and business intelligence in the power industry, this paper presents a comprehensive solution through building an enterprise-level data platform based on the OSIsoft PI system to support big data driven applications and analytics. The platform has the features of scalability, real time, service-oriented architecture and high reliability. Compared to traditional platforms in the power industry, the significant benefit of the innovative platform is that end users can use the data with the global model to drive the self-customized services rather than depend on IT professionals to deploy the service. The paper also describes how to implement data integration, global model construction and big data driven analytics, which are difficult to achieve with traditional solutions. Meanwhile, the paper exhibits preliminary visualization results through data analysis in real scenarios.

A measurement-based control input-output signal selection approach to damp inter-area oscillations

Wide-area measurement systems enable the wide-area damping controller (WADC) to use remote signals to enhance the small signal stability of large scale interconnected power systems. Due to the global properties, conventional control input-output selection approach based on the detailed mathematical models are not available for the complicated systems. A new measurement-based damping control input- output signal selection approach is proposed based on the residue of a constructed linear autoregressive exogenous (ARX) model, which is applied to derive a low-order black-box transfer function model of a power system with power system stabilizers (PSSs) using wide-area signals. Fast Fourier transform (FFT) analysis is performed to preselect the feedback signals at the dominant mode for ARX model construction. Based on the identified ARX model, the residue is used to select the optimal control input-output pairs. Finally, the selected control input- output signal pair is verified by the control performance comparison in a 16-machine 68-bus power system.

Adaptive and coordinated oscillation damping control using measurement-driven approach

One of the main drawbacks of the existing oscillation damping controllers is that they are designed based on offline simulations for assumed system conditions and are not adaptive to the varying power system operating conditions. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, adaptive oscillation damping controllers can be designed, which can coordinate the control provided by the available actuators and effectively damp targeted oscillation modes. An adaptive and coordinated oscillation damping control using measurement-driven approach is proposed in this paper. The subspace state space model is identified using ambient data or ringdown data to update the parameters of damping controller. Additionally, an adaptive time delay compensator employing a lead-lag structure is utilized to reduce the impact of random time delay. The coordinated control for different oscillation modes is achieved by mode decoupling control through selecting observation signal and actuation signal with minimum interaction with other modes. The demonstration on hardware testbed has illustrated the effectiveness of the proposed adaptive and coordinated damping controller.