Dynamic State Estimation for Sustainable Distribution Systems Considering Data Correlation and Noise Adaptiveness

Guest Editorial: Situational awareness of integrated energy systems

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

Transformers are protected by relays to ensure the overall reliability of the generation, transmission, and distribution electric systems. Among all kinds of transformer faults, the inter-turn faults are considered to be the most difficult faults to be detected. Inter-turn faults are extremely harmful to power transformers. When only few turns are shorted at the windings, the currents in the shorted loop can be very high while the transformer terminal currents change little. It is very difficult for the commercial relays or legacy protection methods to detect the existence of inter-turn faults. In this paper, the dynamic state estimation (DSE) based protection scheme is used to detect inter-turn faults. This method monitors the transformer terminal measurements, and it feeds the measurement data and transformer model to a dynamic state estimator. A chi-square test is performed to evaluate the probability (confidence-level) that the transformer model matches measurement data. Any mismatch between the model and measurement indicates something wrong inside the transformer, and protection action should be taken. Numerical simulations are conducted on a 115/10kV three-phase transformer with a 1% inter-turn fault. Three protection methods are compared, namely the percentage-differential scheme, negative-sequence differential scheme and the proposed DSE-based scheme. Analysis shows that the DSE-based protection is more sensitive and reliable than the other two methods, and in general provides faster protection speed.

A review of single-phase-to-ground fault regulation devices for distribution networks

A dynamic state estimator for power system dynamic security assessment

Monitoring and controlling the electrical distribution system for real time is becoming very important to improve its operating performance after transition to active distribution systems. So, many sensors are needed to monitor all parts in the systems. But if sensors are installed at all buses, investment cost becomes huge. To reduce the number of sensors, state estimation approach can be used to estimate the voltage of buses, which do not have sensors. State Estimation (SE) algorithms are broadly classified into Static State Estimator (SSE) and Dynamic State Estimator (DSE). This chapter classifies most algorithms used in active distribution networks, also State estimation in unbalanced distribution systems, and Role of PMU in Distribution System State Estimation.

The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for visual tracking problem. The VBI-based KF algorithm works to estimate the unknown measurement noise covariance matrix (MNCM) and process noise covariance matrix (PNCM), and the MCKF works to deal with non-Gaussian process noise and measurement noise. In order to cater for the visual tracking target scenario, where the MNCM and PNCM are unknown and non-Gaussian, we modify the VBI-based KF algorithm to work for suddenly occurring outliers and modify the MCKF work for small bandwidth. Simulations show that the proposed VBI-MCKF algorithm performs well when both covariance matrices of Gaussian process noise and non-Gaussian measurement noise are unknown or inaccurate.

Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems.

The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.

Mitigating the impact of false data in wide area control of power systems

The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. We show how computations for optimal dynamical state estimation may be accomplished when signals are encoded in spikes. This provides a novel way to design dynamical state estimators, and a novel way to interpret the structure and function of the cerebellum.

HVDC transmission is an integral part of various power system networks. This article presents an Unscented Kalman Filter dynamic state estimator algorithm that considers the presence of HVDC links. The AC - DC power flow analysis, which is implemented as power flow solver for Dynamic State Estimation (DSE), creates an updated admittance matrix. First, a hybrid AC/DC network model is developed to combine the AC network and DC links. Then a non-linear state estimator can solve for hybrid AC/DC states by applying the unscented Kalman filter (UKF) algorithm. It is demonstrated that UKF is easy to implement and accurate in estimation. The dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle, are estimated to study transient behavior of the power system network. Finally, a dynamic state estimation model is built for a 14 bus power system network to evaluate the proposed algorithm for hybrid AC/DC networks.

Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of generators. However, distortion of measurement results of synchronous generator operation parameters is inevitable due to various reasons, such as device failure and operating environment interference and so on. Meanwhile, it is hard to transmit gigantic volumes of data to the information center due to limited communication bandwidth. To tackle these challenges, this article proposes a dynamic state estimation method for synchronous generators with event-triggered scheme. The proposed method first establishes a non-linear model to describe the dynamics of generators. Then, a measure-based event-triggering scheme is adopted to schedule the data transmission from the sensor to estimator, thus reducing communication pressure and enhanced resource utilization. Finally, an improved regularized particle filter (IRPF) algorithm is designed to guarantee the estimation performance. To this end, the genetic algorithm is used to optimize the particles sampled by regularized particle filter algorithm, which can solve particle exhaustion problem. The CEPRI7 system is used to verify the performance of the proposed method.

To address fast-increasing security incidents of drones, in this paper, we investigate fundamental solutions to defend against unsophisticated commercial drones. We have examined several common flight control and navigation algorithms and identified weaknesses in the prevalent EKF-based dynamic state estimation schemes. Exploiting the unique circumstances and characteristics of these drone algorithms, we have developed a theoretical framework of False Data Injection (FDI) specific to drone dynamic state estimation, different from existing FDI attacks on static state estimation. We have further defined two FDI attacks that allow compromised magnetometer “measurements” to pass the drone anomaly detection for misleading a drone’s dynamic state estimation, such that its navigation and flight control are misguided. We have evaluated two practical scenarios to show the effectiveness of the proposed attacks with simulations on the popular ArduPilot flight control system. Our evaluation shows that the proposed attacks on these common algorithms can seriously affect drone navigation, stability, and power consumption. We believe this work is the first FDI attack against common drone dynamic state estimation schemes, which opens a new area of research. We are currently expanding our investigation on exploiting other related issues in this direction.

Static state estimators have been in use in control centres for over four decades now. Recent availability of synchronized phasor measurements for bus voltages and line currents allows development of dynamic state estimators which can track the frequency and synchronous machine state variables and complement the information provided by static state estimators on the network states. This chapter provides a brief overview of the static state estimation problem followed by an introduction to dynamic state estimation. The problem of dynamic state estimation is formulated and a dynamic state estimator whose implementation lends itself to large size power grids is described. The chapter also contains a discussion of observability analysis for dynamic state estimators which presents additional challenges due to the time varying and nonlinear nature of the system and measurement equations.