State Estimation In Distribution Networks Research Articles

State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary. The efficiency of deep learning neural networks such as multi-layer perceptron (MLP) and Legendre neural network (LeNN) depends on the position of the measurements and the number of neural networks applied. Determining the applicable number of neural networks to ensure high estimation accuracy plays an important role in the estimation process. This aspect is addressed in this study, where multiple neural networks are used to improve performance compared to a single neural network. The results obtained indicate that determining the applicable number of neural networks depends on several factors such as the position of the measurements and the diversity of the data. The application of LeNN on state estimation of a 69-bus radial distribution network is used as an illustrative example to explain the distinctive feature of the proposed technique.

Dynamic state estimation of distribution network under Markov DOS attack

AbstractEnergy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack.

AM-HNN based real-time voltage estimation method of distribution network using joint PMU and AMI data

A large amount of distributed energy access increases the risk of voltage fluctuation and off-limit voltage. Accurate voltage estimation is more and more important to ensure the safety of distribution networks. With the introduction of phasor measurement unit (PMU), multi-source measuring data with different time scales and measuring precision exist in distribution networks. How to fully integrate multi-source measurement data to achieve fast and accurate state estimation of distribution networks is an important prerequisite for efficient operation decision making. In this paper, a distribution network voltage state estimation method based on AM-HNN model is proposed. The attentional mechanism (AM) is introduced to mine the influence of PMU measurement data on voltage state estimation, and the feature weight is modified adaptively. The highway neural network (HNN) was used to fit the mapping relationship between the injected power of grid nodes and the voltage of key nodes. Taking the distribution network data of different scenes as an example, the superiority of the proposed optimal measurement model is verified by comparing with various voltage estimation methods.

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Dynamic state estimation of distribution networks with mixed measurements based on Cubature Kalman Filter

The integration of synchronized phasor measurement units (μPMU) has introduced diverse data sources into the distribution network. Each has distinct time scales and precision levels. To enable effective decision-making, it is crucial to amalgamate these measurements efficiently, ensuring both speed and accuracy in distribution network state estimation. This study explores a dynamic state estimation approach for distribution networks, employing Cubature Kalman Filtering (CKF) as the central methodology. By deeply fusing μPMU, AMI and SCADA data and incorporating CKF techniques, it improves accuracy and stability, which is validated on the IEEE 37-node system. This research supports advanced distribution network apps and large-scale data analysis.

Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm

To address the insufficient configuration of real-time measurement devices in distribution networks, a state estimation method for distribution networks is proposed. First, both historical data and real-time data are used as the input of the convolutional neural networks-bi-directional long short-term memory (CNN-BiLSTM) network to obtain the pseudo measurements of the branch power and load node injected power with high accuracy at the current moment. Second, the pseudo measurement set of the input state estimation is determined using the customized hybrid greedy genetic algorithm (HGGA) algorithm. Finally, the real-time measurement, pseudo measurement, virtual measurement, and weight set are input into the state estimation program to complete the real-time state estimation of the distribution network based on the weighted least squares method. Additionally, the proposed method is applied to the data fusion of different measurement systems. Theoretical analysis and example verification confirm that the proposed method can effectively improve the accuracy of pseudo measurement modelling and state estimation results.

Photovoltaic injection power estimation method based on variable voltage sensitivity matrix

The operation of PVs has a significant impact on the safe and economic operation of the distribution networks. Nevertheless, there are many cases where the distribution system operators cannot obtain the accurate operation information of PVs. To solve the above problems, this paper proposes a PV injection power estimation method based on the variable voltage sensitivity matrix. First, the voltage sensitivity matrix is constructed. Second, all the node voltage magnitudes are obtained indirectly through distribution network state estimation owing to the limited number of voltage measurements. Then, combine the estimated voltages with the voltage sensitivity matrix to realize the estimation of PV injection power. Finally, the validity of the proposed method is verified in CIGRE distribution network, IEEE 34-node and IEEE 123-node distribution systems. The results show that PV on-grid state and output can be effectively identified in several different scenarios: PV continuous change, PV injection power plunge, and PV injection power plunge during continuous change. Furthermore, the performance of the proposed method is unaffected by the on/off-grid action of the impact load. The average error of PV injection power estimation results is less than 4 %, which has strong adaptability to operating conditions and good estimation accuracy.

Multi-area state estimation for active distribution networks under multiple uncertainties: An affine approach

An enormous challenge for the state estimation (SE) process in large-scale distribution networks lies in how to deal with multiple uncertainties and the increasing computational tasks simultaneously. This paper aims to address the issue and propose a multi-area architecture for perceiving system status with uncertain inputs. The first step involves the introduction of a novel Louvain-flow-based partition structure, which effectively divides large-scale unbalanced distribution networks into several sub-areas. Subsequently, an affine state estimation (ASE) model, employing the Neumann series method, is formulated for each sub-area, taking into account uncertainties in system measurements and line parameters. Finally, the interior-point filter line-search algorithm combined with consensus theory is utilized to solve all local ASE models, and the interaction of boundary state between adjacent sub-areas is focused and completed, to output the global state results of the distribution network precisely. Case studies are presented to verify that the proposed distributed ASE model yields obvious advantages in tracking system uncertainties by contrast with the existing SE model.

A False Data Injection Attack Detection Strategy for Unbalanced Distribution Networks State Estimation

With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.

Spatio-Temporal Generative Adversarial Network Based Power Distribution Network State Estimation With Multiple Time-Scale Measurements

The increasing penetration of distributed renewable generation has introduced significant uncertainties and randomness to the power distribution network operation. Accurate and timely awareness of the network operation is of paramount importance to ensure system safety and reliability and is considered non-trivial and costly as substantial network reinforcement with advanced measurement devices is generally required. Also, the existing state estimation methods, e.g., weighted least square (WLS), may not converge in the presence of incomplete and inaccurate measurements. This paper proposes a spatio-temporal estimation generative adversarial network (ST-EGAN) consisting of feature extraction, information completion, data reconstruction and fake data discrimination to generate high-resolution pseudo-measurements to promote the accuracy and robustness of state estimation. The task of high-resolution power distribution network state estimation is carried out based on the mixed dataset of multiple time-scale measurements obtained from Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU). The proposed solution is extensively assessed using the IEEE 33-bus test network compared with the existing solutions for a range of scenarios with different resolutions and noise intensities. The numerical results demonstrated that the proposed ST-EGAN can reduce the mean RMSE by 4.78% compared to interpolation algorithms, and reduce the RMSE by 0.14% and 0.21% compared with deep convolutional generative adversarial networks (DCGAN) and super-resolution convolutional networks (SRCNN) in the presence of noises with different intensities. The proposed method can be generalized to cases with different topological structures and measurement assembly conditions.

Harmonic State Estimation for Distribution Networks Based on Multi-Measurement Data

Accurate harmonic state estimation (HSE) is necessary to mitigate harmonic issues in power distribution networks. However, existing HSE methods face difficulties in solution of state equation that the measured data is insufficient with limited monitors. Therefore, this study proposes a HSE algorithm for distribution networks based on multi-measurement data. Firstly, a harmonic source location method based on mutual information and distribution network partition is proposed to reduce the number of state variables and to solve the state equation. Thus, HSE with low resolution (15 min) is realized. Secondly, a HSE method with enhanced resolution is developed. This method establishes maximum entropy principle models for harmonic currents injected into buses, calculates the probability density function (PDF) of the harmonic current, and realizes optimal interpolation of the harmonic data to enhance the resolution of the HSE (3 min) according to the PDF. Finally, the applicability and effectiveness of the proposed method are verified using an IEEE-33 test system simulation and a real power system in South China.

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

State estimation of active distribution network (ADN) plays an important role in distribution energy management system. The increase penetration of distributed generations, especially the distributed photovoltaic (PV), in ADN leads to high uncertainty of ADN’s operation and the state of the ADN varies with the variation of the PV output power. For the uncertainty of PV power output, an interval dynamic state estimation (IDSE) method, which estimates the interval of ADN state variables is proposed in this paper. Firstly, considering the slow computation speed of the Unscented Kalman Filter (UKF), the square root UKF is used to predict the real-time operating level of the state variables. Secondly, since the power output of PV has features of the variation randomly, the neural network-based prediction intervals is employed to predict the power output interval of PV. Finally, the normal fluctuation range of ADN state is modelled as a bilevel non-linear programming problem to perform IDSE, which in turn monitors the operating state of ADN. The proposed method is evaluated on the IEEE 33-node and IEEE 123-node systems, respectively. The test results demonstrate that the dynamic status of the ADN can be tracked accurately using the proposed method.

Robust State Estimation of Active Distribution Networks with Multi-source Measurements

Robust State Estimation of Active Distribution Networks with Multi-source Measurements

Forecasting Aided Distribution Network State Estimation Using Mixed μPMU-RTU Measurements

Distribution network state estimation is the backbone of energy management systems, whose accuracy and adaptability are very important for the advanced application software in distribution networks. This article proposes a novel forecasting-aided state estimation method for distribution networks with the mixed measurements of microphasor measurement unit (μPMU) and remote terminal unit (RTU). First of all, the data imputation techniques for RTU with a longer update period are proposed to handle asynchronous characteristics and improve computational accuracy using historical and current measurements. Then, the measurement equations are built to process the different types of measurement data from μPMU and RTU. The cubature Kalman filter is adopted to ensure the numerical stability of state forecasting, measurement forecasting, and filter correction. Finally, the IEEE 33- and 37-node systems are applied to verify the effectiveness of the proposed method, which can get an accurate state under mixed measurement.

State Estimation of Asymmetrical Distribution Networks by μ-PMU Allocation: A Novel Stochastic Two-stage Programming

• The WLS-based state estimation algorithm developed in this research can simulate large-scale asymmetric distribution networks. • An effective DSSE model is implemented based on a limited number of μ-PMUs and ZINs for determining the state variables. • Using the ZIN characteristics to minimize the μ-PMUs number in the system and keep the system fully observable. • The linear approximations are applied to model the linear DSSE. Distribution system state estimation provides essential data for the system monitoring and control, which some uncertain parameters such as the intermittent and varying output of distributed generation (DG), random meter errors, and inaccurate network parameters make situational awareness (SA) of distribution systems a challenging issue. To address these issues, this paper develops an innovative two-stage stochastic programming model, where in the first stage, optimal μ-PMU placement is implemented aiming at minimizing the installation cost of μ-PMU and maximizing the number of measurement redundancy and the system observability in the presence of partially zero injection nodes (PZIN), while in the second stage state estimation of three-phase asymmetric DG-integrated distribution systems is performed to enhance SA. By applying the proposed model, the optimal locations of μ-PMUs in the presence of PZINs and various contingencies were achieved, and the distribution system state estimation was obtained with high accuracy and low error percentage. The feasibility of the proposed methodology is verified on the modified IEEE 85-bus distribution system.

A topology identification method based on one-dimensional convolutional neural network for distribution network

Distribution network (DN) topology identification (TI) is the basis of distribution network state estimation. However, the connection of high-penetration renewable energy makes TI of DN more challenging. Thus, a TI method of active distribution network (ADN) based on one-dimensional convolutional neural network is proposed in this manuscript. Based on the sensitivity of node voltage to topology changing of DN, the characteristics of nodes are analyzed to select the key nodes to place the distribution network phasor measurement unit (DPMU), which can save investment and reduce the redundancy of model training. Several tests are carried out with the modified IEEE-33 bus DN with photovoltaic (PV) units. The results show that the proposed distribution network topology identification method can realize the high accuracy TI in ADN under limited DPMU measurement.

State Estimation and Leakage Detection in Water Distribution Networks Using Constrained Zonotopes

State estimation in water distribution networks (WDNs) is a challenging task due to the scarcity of measurements and the presence of several modeling uncertainties. In the literature, pseudo-measurements are often required to obtain an observable model for state estimation, which are highly uncertain and mostly represented by bounded sets. This article proposes two new methods for set-based state estimation in WDNs, considering unknown-but-bounded measurement and model uncertainties to calculate bounds on the system states. A new interval method is proposed based on iterative computation of tight enclosures of the nonlinear head-loss functions, and bounding the solution of the algebraic equations using rescaling. This method, referred to as iterative slope approximation and rescaling (ISAR), is mildly more conservative than the iterative hydraulic interval state estimation (IHISE) method previously proposed by the authors, but significantly more computationally efficient. However, since intervals are not capable of capturing the dependencies between state variables, we propose the use of constrained zonotopes (CZs) as an additional step to both IHISE and ISAR. This yields two new algorithms for set-based state estimation of WDNs, capable of capturing the dependencies between hydraulic states, which result in sets with significantly smaller volumes than intervals. The benefits of CZs are also highlighted when the new enclosures are used for leakage detection. These provide higher leakage detection rates compared to intervals, as demonstrated in two case studies using benchmark WDNs.

DSSE in European-Type Networks Using PLC-Based Advanced Metering Infrastructure

This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders.

Real-Time Topology Detection and State Estimation in Distribution Systems Using Micro-PMU and Smart Meter Data

Distribution network topology detection and state estimation in real-time are critical for modern distribution systems management and control. However, number of sensors in distribution networks are limited and communication links between switch devices and distribution management system are not well-established. In this regard, this paper proposes mixed-integer quadratic programming (MIQP) formulations to determine the topology of distribution network and estimate distribution system states simultaneously using micro-phasor measurement units (micro-PMUs) and smart meter data. Two approaches based on AC optimal power flow are proposed and analyzed: (i) polar power-voltage (PPV) formulation, and (ii) rectangular current-voltage (RIV) formulation. The proposed models include convex objective function while constraints are linearized using first-order approximation of Taylor series and Big M method. The proposed models can identify multiple simultaneous switching actions at each time instant and different topology configurations including radial and meshed networks. Only measurement data at each time interval is needed to identify topology and states of the system correctly. The proposed models are tested on a modified IEEE-33 bus system with realistic load data from Pecan Street Inc. database. The results confirm that both models can identify system topology and states with remarkable accuracy in real-time, while RIV model outperforms PPV model.

State Estimation in Unbalanced Distribution Networks by Symmetrical Components

State Estimation in Unbalanced Distribution Networks by Symmetrical Components