Robust state estimation in distribution networks

A robust state estimation method of distribution network is proposed based on robust estimation theory. The estimator operates on a pi-section branch model, utilizes voltage magnitude and phase angle of each node as state variables, and provides a steady state solution for a balanced three-phase distribution network with generation. According to characteristics of complex distribution networks with generation, a novel equivalent weight function is proposed based on the influence function theory. Moreover, a procedure is designed which can determine correlative parameters of the equivalent weigh function automatically and self-adaptively. The proposed estimator is robustified synthetically in both factor space and measurement space. Test results are very promising. (4 pages)

The measurements given by Supervisory Control And Data Acquisition (SCADA) systems are generally insufficient for Distribution network State Estimation (DSE). The accuracy of DSE seriously relies on the pseudo measurements, which are generally inaccurate. In historical scene, measurements given by smart distribution transformer meter (SDTM) can be used to enrich the measurements for DSE. The hybrid measurements of SCADA and SDTM can satisfy the observability of DSE with certain redundancy. By using rectangular coordination and introducing intermediate variables and quadratic equalities, the SCADA/SDTM hybrid DSE is modeled as quadratic constrained quadratic estimation (QCQE) problem, which can be solved by traditional state estimation methods with sparse techniques efficiently and conveniently. Simulations on the IEEE 33-bus system show the accuracy of the hybrid DSE. The proposed DSE has been implemented in distribution networks in Zhejiang and shows good efficiency and accuracy.

Global state estimation for whole transmission and distribution networks

This paper proposes a robust three-phase state-estimation method that is applicable to both balanced transmission networks and unbalanced distribution networks with nonlinear measurements. We extend the recently introduced bilinear formulation of state estimation problems to a robust three-phase model. The original nonlinear state estimation is solved by resorting to two auxiliary linear model and a nonlinear transformation in between. Bad measurements are further suppressed by introducing a robust state estimation model. Numerical tests on IEEE benchmark systems, including a distribution network and a transmission network, demonstrate the robustness and efficiency of the method.

Many of the past work on distribution state estimation have been focused on the adoption of a transmission state estimation approach, without necessarily accounting for specific requirement distribution networks analysis. On distribution networks, typically, there are very few real-time measurements, and power consumption of each load is very uncertain. This paper presents an alternative approach to distribution state estimation with limited Real-Time Measurements Points (RTMP). This method includes two steps; in the first step Correlation Coefficient (CC) between power consumption of each LP and RTMP with usage of least squares (LS) is determined. In the second step; with data of RTMP, CC and forward backward load flow modification, excellent state estimation of electrical distribution networks can be determine.

State estimation of distribution network based on hybrid measurement combined with multi-source asynchronous data

The AC-DC hybrid microgrid is a credible evolution path for the microgrid. State estimation in complex distribution network is a significant foundation for the safe operation. In AC-DC hybrid microgrid, centralised state estimation algorithms have quite a few deficiencies such as large communication capacity and privacy protection. In response to these obstacles, this paper proposed a novel distributed state estimation algorithm. First, a three-stage state estimation model was established for AC-DC hybrid microgrid, in which the second stage is a nonlinear transform for the linearisation of the nonlinear state estimation progress so that the first and third stage can solve the linear state estimation problem. Furthermore, the Lagrangian relaxation method was ultilised for the distributed problem solution. In the proposed algorithm, AC subsystems and DC subsystems respectively execute state estimation through local information, and the overall consistency of the system state estimation can be eventually achieved according to the transfer iteration of the boundary information. The computational efficiency and convergence of the proposed method was ensured owing to the combination of bilinear theory and Lagrangian relaxation. Simulation results indicate that the proposed method can achieve its goal and be superior to existing method in efficiency.

Active distribution system state estimation incorporating photovoltaic generation system model

The accuracy of state estimators using the Kalman Filter (KF) is largely influenced by the measurement and the process noise covariance matrices. The former can be directly inferred from the available measurement devices whilst the latter needs to be assessed, as a function of the process model, in order to maximize the KF performances. In this paper we present different approaches that allow assessing the optimal values of the elements composing the process noise covariance matrix within the context of the State Estimation (SE) of Active Distribution Networks (ADNs). In particular, the paper considers a linear SE process based on the availability of synchrophasors measurements. The assessment of the process noise covariance matrix, related to a process model represented by the ARIMA [0,1,0] one, is based either on the knowledge of the probabilistic behavior of nodal network injections/absorptions or on the a-posteriori knowledge of the estimated states and their accuracies. Numerical simulations demonstrating the improvements of the KF-SE accuracy achieved by using the calculated matrix Q are included in the paper. A comparison with the Weighted Least Squares (WLS) method is also given for validation purposes.

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a flying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge.

A three-phase state estimation in active distribution networks

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.

This paper presents a novel probabilistic approach to distribution network observability. The observability analysis is an important part of the state estimation process. Traditionally, it determines whether the state of a network can be estimated based on the available set of measurements. This works well in transmission networks, where numerous metering devices are normally available. In distribution networks, however, only a few metering devices are usually installed. Pseudo measurements with large margins of error are often used in the absence of real measurements to perform state estimation. This implies that if a large number of pseudo measurements is used to make a network observable, the estimated state can be significantly different from the actual state even if the network is classified as observable. To overcome this limitation, a new approach to observability is proposed in this paper. The proposed method takes the uncertainty of the state estimation into account, and therefore, assesses the network observability depending on the accuracy of the estimated network state. This paper also demonstrates how a meter placement method based on the proposed observability assessment can be implemented. The presented methods have been tested on a modified 34-bus IEEE test feeder. Results are compared with existing methods.

System state monitoring is crucial in order to maintain the system security, reliability and quality of a power system. Considering the limitation of monitoring devices in distribution network, state estimation technique could be employed. State estimation is an action of appraising an unknown system variables based on limited number of real-time measurements. The main objective of this paper is to find the suitable state estimation that able to estimate the state of the islanded distribution network. In this paper, state estimation for a distribution network is developed and enhanced by incorporating composite load model. By incorporating this model, the calculated state variables of the network will be more accurate (to obtain the most practical value of the system state variables). Moreover, the proposed state estimation results are compared with backward-forward sweep load flow technique to justify the system state variables.

Chapter 4 - Kalman Filtering and Robust Estimation