The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

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.

/pdf/power-system-dynamic-state-estimation-motivations-lul365qej6.pdf

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Challenges of renewable energy penetration on power system flexibility: A survey

This paper describes the usefulness of renewable energy throughout the world to generate power. Renewable energy adds a remarkable scope in power system. Renewable energy sources act as the prime mover of a microgrid. The Microgrid is a small network of power system with distributed generation (DG) units connected in parallel. The integration challenges of renewable energy sources and the control of microgrid are described in this paper. The varied nature of DG system produces voltage and frequency deviation. The unknown nature of the load produces un-modeled dynamics. This un-modeled dynamic introduces measurable effects on the performance of the microgrid. This paper investigates the performance of the microgrid against different scenarios. The voltage of the microgrid is controlled by using different controllers and their results are also investigated. The performance of controllers is investigated using MATLAB/Simulink SimPowerSystems.

/pdf/a-survey-on-control-issues-in-renewable-energy-integration-2ylhj9r8as.pdf

A survey on control issues in renewable energy integration and microgrid

Due to the communication channel noises, GPS synchronization process, changing environment temperature and different operating conditions of the system, the statistics of the system process and measurement noises may be unknown and they may not follow Gaussian distributions. As a result, the traditional Kalman filter-based dynamic state estimators may provide strongly biased state estimates. To address these issues, this paper develops a robust generalized maximum-likelihood unscented Kalman filter (GM-UKF). The statistical linearization approach is presented to derive a compact batch-mode regression form by processing the predicted state vector and the received measurements simultaneously. This regression form enhances the data redundancy and allows us to detect bad phasor measurement unit measurements and incorrect state predictions, and filter out unknown Gaussian and non-Gaussian noises through the generalized maximum likelihood-estimator. The latter minimizes a convex Huber function with weights calculated via the projection statistics (PS). Particularly, the PS is applied to a proposed 2-dimensional matrix that consists of temporally correlated innovation vectors and predicted states. Finally, the total influence function is used to derive the error covariance matrix of the GM-UKF state estimates, yielding the robust state prediction at the next time instant. Extensive simulations carried out on the IEEE 39-bus test system demonstrate the effectiveness and robustness of the proposed method.

Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics

The introduction of the microprocessor-based numerical relay in the 1980s resulted in multifunctional, multidimensional, communications-enabled complex protection systems for zone and system protection. The increasing capabilities of this technology created new unintended challenges: 1) complexity has increased and selecting coordinated settings is a challenge leading to occasional miscoordination; 2) protection functions still rely on a small number of measurements (typically three voltages and three currents) limiting the ability of protection functions to dependably identify the type of fault conditions; and 3) present approaches are incapable of dealing with hidden failures in the protection system. Statistically, 10% of protection operations are misoperations. This paper presents a new approach to protection that promises to eliminate the majority of the problems that lead to misoperations. The approach is described, demonstrated in the laboratory, compared to traditional protection functions and its application to a substation coordinated protection system capable of detecting and dealing with hidden failures is described. This paper also discusses the planned field testing of the approach.

Dynamic State Estimation-Based Protection: Status and Promise

Series compensated transmission lines challenge legacy protection schemes. In this paper, a dynamic state estimation based protection (EBP) method is proposed to address these challenges. The method requires GPS synchronized measurements at both ends of the line and a high-fidelity model of the protection zone. The paper presents the dynamic model of the protection zone and its impact on the performance of the protection scheme. Numerical simulations show that the method can correctly identify faults, independently of position and type. The paper also compares the proposed method versus legacy protection functions such as distance protection and line differential protection. The comparison shows faster detection of internal faults, immunity to current inversion caused by series capacitors (SCs) and improved detection sensitivity for high-impedance faults.

Dynamic State Estimation Based Protection on Series Compensated Transmission Lines

Accurate fault locating minimises labour and outage time. Efforts to increase the accuracy of fault locating methods are on-going. In this study, a dynamic state estimation-based fault locating (EBFL) method is proposed. Best implementation requires GPS synchronised sample value measurements at both ends of the line. The dynamic state estimator operates on the sampled values using a detailed dynamic model of the line, which includes the fault location as a state. Specifically, the dynamic line model is represented as a multi-section transmission line model, inclusive of phase conductor type and size, shield wire(s) type and size, and tower ground impedances, integrated with the fault model. This study presents extensive numerical experiments demonstrating that the method has higher accuracy than traditional fault locating methods for different fault types, locations and impedances. Additionally, the method works for both two-terminal and three-terminal transmission lines.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2017.0371

Dynamic state estimation-based fault locating on transmission lines

Microgrid protection presents challenges because of the presence of inverter interfaced resources which results in insufficient separation between fault and load currents This paper uses the Dynamic State Estimation (DSE) based protection (EBP) method for the protection of microgrid components The paper demonstrates that this method provides a reliable protection system as opposed to traditional methods Specifically, we verify that traditional protection functions such as overcurrent, distance etc fail to provide selectivity in protection of microgrids The EBP method can be viewed as an evolution of current differential protection and requires GPS synchronization if measurements come from different metering devices The method mainly determines whether the measurements are consistent with the model of the protection zone by use of the chi-square test Numerical simulations prove that the method can correctly respond to different fault types and locations The method works equally well for three-phase balanced or unbalanced systems, two-phase or single-phase systems, and radial or networked microGrids

Dynamic State Estimation based protection of microgrid circuits

High penetration of Converter Interfaced Generations (CIGs) presents challenges in both microgrid (μGrid) circuit and other system with CIG resources, such as wind farms and PV plants. Specifically, protection challenges are mainly brought by the insufficient separation between fault and load currents, especially for μGrids in islanded operation, and the short connection length in μGrids. In addition, CIG resources exhibit limited inertia and weak coupling to any rotating machinery, which can result in large transients during disturbances. To address the above challenges, this paper proposes a Dynamic State Estimation (DSE) based algorithm for protection and control of systems with substantial CIG resources such as a μGrid. It requires a high-fidelity dynamic model and time domain (sampled value) measurements. For μGrid circuit protection, the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements. For CIG control, the algorithm estimates the frequency at other parts of a μGrid using CIG local information only and then utilizes it to provide supplementary feedback control. Simulation results prove that DSE based protection algorithm detects internal faults faster, ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods. DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.

/pdf/protection-and-control-of-microgrids-using-dynamic-state-3kpmp4m0ai.pdf

Liangyi Sun

Papers

Dynamic State Estimation-Based Protection: Status and Promise

Dynamic State Estimation Based Protection on Series Compensated Transmission Lines

Dynamic state estimation-based fault locating on transmission lines

Dynamic State Estimation based protection of microgrid circuits

Protection and control of microgrids using dynamic state estimation