A Computationally Efficient State Estimation Algorithm for the Supervision of Low Voltage Grids

Distribution system state estimation is essential for real-time monitoring and control of distribution systems. In recent years, an increasing number of Automated Meter Reading (AMR) systems have been installed to provide customer consumption information. The deployment of smart meters in low voltage (LV) distribution networks, within the context of Advanced Metering Infrastructure (AMI) systems, will enhance the electric energy consumption data storage, processing and analysis capabilities. Because of the limited number of real-time measurements in distribution systems, increased smart metering data availability from LV consumers is necessary to provide load estimates, which can be treated as pseudo-measurements to guarantee the network observability and estimate the system states. This paper presents two distinctive load estimation methods which utilize near real-time consumption data and short past consumption data, respectively. The accuracy of estimated loads is tested with a combined load and state estimation approach. Numerical results with a distribution system will show the effectiveness of the proposed algorithms.

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.

Accurate and reliable state estimation is the core stone for flexible operation and control of active smart grids. Through the current expansion of integrating DG units in low voltage networks, the network operation is becoming more complex than before. However, real-time monitoring and control through state estimation is a routine task for the transmission system operators (TSO's) due to the availability of measurement data. The distribution system operators (DSO's) are trying to extend their monitoring and control for medium and low voltage network in order to enable smart grid applications. With the current rollout of smart meters, which are considered as a key component of future smart grids, there would be enough metering data in the distribution networks (voltage, current, active and reactive power consumption and generation) available at every customer connection point. This paper aims to develop a method to solve the problem of meter placement for low voltage system state estimation through deciding which measurement data from the installed smart meters should be considered in the state estimation algorithms in order to improve on the uncertainty of the estimated voltage and its phase angle at every node in the network. (4 pages)

Due to the increasing participation of alternative and intermittent energy sources, which induce changes in the structure and operation of distribution systems, algorithms for state estimation have been developed in recent years in response to the need for real-time monitoring for these systems. In this scenario, this paper proposes the development of a methodology for state estimation for distribution systems. The proposed formulation combines decoupling approaches, recognized for reducing computational effort, with the Sparse Tableau Method and a priori information, with decoupling in distribution networks enabled by the use of the complex per unit normalization technique. To demonstrate the performance of the proposed methodology, tests were conducted on an illustrative test system and on a 69-bar test system.

State estimation plays a critical role in power dis-tribution systems. However, the conventional state estimation commonly used in transmission systems cannot be applied to the distribution systems because of insufficient measurements. Even though machine learning methods have begun to demonstrate their capabilities to overcome this challenge, they are not capa-ble of explicitly incorporating distribution systems' topological information. This paper proposes EleGNN, an electrical-model-guided graph neural network (GNN), to perform power distri-bution system state estimation (DSSE). By explicitly considering physical topology, EleGNN enhances GNN with original node and edge feature propagation methods, allowing us to obtain accurate estimation results despite insufficient measurements and various topologies. Evaluations of six different power systems demonstrate significant improvement in state estimation accuracy than the method relying on general neural networks. Specifically, node-level mean square errors introduced by EleGNN are at least one order of magnitude smaller, even with up to 50% of missing measurements.

Learning-based state estimation in distribution systems with limited real-time measurements

Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Modern-day electric distribution grid is experiencing rapid and fundamental changes. The share of distributed generation units such as small-scale wind turbines, photovoltaic panels, electric vehicle charging stations and heat pumps is growing at low voltage distribution grids. The increase in the integration of renewable units transforms the traditional centralised uni-directional chain of energy production, to a distributed bidirectional power flow. This ongoing transition is challenging the Distribution System Operators as they can no longer rely on traditional methods to ensure reliable operation of the distribution grid. The first step towards the reliable operation of Low Voltage (LV) grids is to increase observability via real-time monitoring and enable data-driven novel applications such as Distribution System State Estimation, Demand Management, and Voltage Control.

Efficient and accurate state estimation is essential for the optimal management of the future smart grid. However, to meet the requirements of deploying the future grid at a large scale, the state estimation algorithm must be able to accomplish two major tasks: 1) combining measurement data with different qualities to attain an optimal state estimate and 2) dealing with the large number of measurement data rendered by meter devices. To address these two tasks, we first propose a practical solution using a very short word length to represent a partial measurement of the system state in the meter device to reduce the amount of data. We then develop a unified probabilistic framework based on a Bayesian belief inference to incorporate measurements of different qualities to obtain an optimal state estimate. Simulation results demonstrate that the proposed scheme significantly outperforms other linear estimators in different test scenarios. These findings indicate that the proposed scheme not only has the ability to integrate data with different qualities but can also decrease the amount of data that needs to be transmitted and processed.

Robust three-phase state estimation for PV-Integrated unbalanced distribution systems

This study presents a Distribution System State Estimation (DSSE) algorithm for unbalanced smart grids and evaluates its performance with special focus on the effect of the unavailability of measurements, which is a common problem on smart grids. The proposed DSSE algorithm is based on the Weighted Least Squares (WLS) method and the unbalanced power flow analysis and addresses several challenges such as unbalanced consumption, various configurations and types of loads, line couplings and lack of measurements. To validate the proposed algorithm’s performance, the IEEE 4-node test feeder and the IEEE European Low Voltage test feeder, comprising 907 nodes, are used as benchmarks. A detailed analysis of the DSSE state results, their deviations from the IEEE test feeders, and their sensitivity regarding measurements’ availability, demonstrates the accuracy and robustness of the proposed solution.

The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks.

The State Estimation (SE) is one of the key elements of the monitoring activity of an active distribution network and is the basis for every control and management application. Distribution networks present their own features, requiring specific estimation methodologies. The most widespread algorithms for Distribution System State Estimation (DSSE) are based on a weighted least squares (WLS) minimization of the residuals of the measurements and usually compute the state in terms ofeither node voltages or branch currents. The DSSE relies on real measurements collected by the distributed measurement system and on other available information, mainly obtained from historical data, that help obtaining observability. The prior information (the so called pseudo-measurements) derived from expected power profiles of both loads and generators is crucial for the accuracy of the estimation process and has to be fully exploited. A degree of correlation can often be assumed among power consumptions or generations of some particular nodes. The influence of the correlation on the quality of the estimation is investigated in this paper using a branch current DSSE algorithm. The importance of including correlation in the WLS is discussed using both traditional and synchronized measurements. Results obtained on a 95-bus distribution network are presented and analyzed.

While various industries heavily rely on electrochemical sensors for air and liquid quality monitoring and control, many electrochemical sensors are cross-sensitive to one or more interfering gases in addition to the target component, which may cause incorrect readings and lead to improper control measures. In this study, we proposed a direct algebraic approach-based state estimation and output decomposition algorithm which is suitable for state estimation for a class of nonlinear dynamic systems with output measurements that are highly cross-sensitive to multiple interfering gases. Using the cross-sensitive sensor measurement and its successive time derivatives, the proposed methodology can provide a closed-form solution for the actual system state estimation in a systematic way. The established state estimation algorithm was successfully validated in a numeric example. The proposed state estimation and output decomposition algorithm is applicable to a broad class of nonlinear dynamic systems which use sensors that are cross-sensitive to multiple interfering gases in order to improve the performance of the systems.

State estimation is often a challenging task in distribution systems due to deploying a limited number of measurement devices. Moreover, the integration of precise distribution-level phasor measurement unites, a.k.a micro-PMUs, along with inaccurate pseudo-measurements in state estimation introduces another challenge. These issues might decrease the efficiency of traditional standard weighted least squares (WLS) for distribution system state estimation. This paper proposes a novel linear distribution system state estimation based on limited number of synchrophasor measurements and pseudo-measurements. To involve pseudo-measurements into the linear state estimation, Taylor's approximation is adopted to reformulate pseudo-measurement functions in a linear form. The results obtained via numerical simulations demonstrate that the proposed linear state estimation method performs as good as the standard nonlinear WLS estimator. Moreover, the sensitivity analyses results show that our method has a better performance compared to WLS once a limited number of highly precise micro-PMUs are accompanied with inaccurate pseudo-measurements in state estimation.