Measurement placement method for power system state estimation: part I

Decoupled State-Estimation in Energy Control Centres

Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.

Maintaining reliability during power system operation relies heavily on the operator’s knowledge of the system and its current state. With the increasing complexity of power systems, full system monitoring is needed. Due to the costs to install and maintain measurement devices, a cost-effective optimal placement is normally employed, and as such, state estimation is used to complete the picture. However, in order to provide accurate state estimates in the current power system climate, the models must be fully expanded to include probabilistic uncertainties and non-linear assets. Recognizing its analogous relationship with state estimation, machine learning and its ability to summarily model unseen and complex relationships between input data is used. Thus, a power system state estimator was developed using modified long short-term (LSTM) neural networks to provide quicker and more accurate state estimates over the conventional weighted least squares-based state estimator (WLS-SE). The networks are then subject to standard polynomial scheduled weight pruning to further optimize the size and memory consumption of the neural networks. The state estimators were tested on a hybrid AC/DC distribution system composed of the IEEE 34-bus AC test system and a 9-bus DC microgrid. The conventional WLS-SE has achieved a root mean square error (RMSE) of 0.0151 p.u. for voltage magnitude estimates, while the LSTM’s were able to achieve RMSE’s between 0.0019 p.u. and 0.0087 p.u., with the latter having 75% weight sparsity, estimates about ten times faster, and half of its full memory requirement occupied.

This paper presents a review on major blackouts occurred in power grid across the world and importance for the need of state estimation (SE) solution. This work introduces the applications of phasor measurement units (PMU) to reduce the occurrence of blackouts in power system. The blackouts in power system can occur due to overload, light load conditions, heavy storms or due to line outages. The cascade failure due to line outages, i.e., measured as N-1 outages can lead to power system blackout. The necessity of power system state estimation (PSSE) solution for protection in view of blackouts is discussed in this paper. A Generalized Linear State Estimation (GLSE) method is proposed to obtain robust and accurate states of power system network. A matrix integrating phasor and conventional measurements are formulated to obtain accurate states. IEEE transmission bus network such as 14-bus network is considered as test case for proposed GLSE programming in MATLAB. The accurate states are computed and compared with standard weighted least squares (WLS) method to show its effectiveness.

In allusion to the defect that conventional weight least squares (WLS), when applied to the power system state estimation, generally takes the same weight. It causes large state estimation error. A state estimation method combining optimal weight setting with weighted least squares is proposed. On the basis of weighted least squares state estimation models, through the theoretical analysis of different kinds of voltage measurements, power injection measurements and power flow measurements data, which impact differently on the results, different weights are set corresponding to their measurement sensitivity in order to reduce the estimation errors. Finally, the algorithm is tested on IEEE14 bus system by MATLAB. Compared with the traditional weighted least squares, this method is similar with regard to iteration time. However, the voltage and phase angle state estimation accuracy can be improved by about 0.16 and 4.66 percentage points. Therefore, the algorithm has certain advantages and application value.

This chapter deals with the power flow problem. The power flow algorithms include the Newton-Raphson method in both polar and rectangular forms, the Gauss-Seidel method, the DC power flow method, and all kinds of decoupled power flow methods such as fast decoupled power flow, simplified BX and XB methods, as well as decoupled power flow without major approximation. Power system state estimation derives a real-time model through the received data from a redundant measurement set. Different kinds of methods about state estimation are introduced in the chapter. Among them, the weighted least squares (WLS) state estimation methods are widely used. There are three most commonly used measurement types in power system state estimation. They are the bus power injections, the line power flows, and the bus voltage magnitudes.

Measurements from the electrical network are generally transmitted towards the control centres using special communication links. These measurements allow determining the state of the network in real time. However, these measurements often contain uncertainties due to the meter and communication errors, incomplete metering or unavailability of some of these measurements, etc. This paper presents the application of the Particle Swarm Optimization (PSO) algorithm in minimizing the raw measurement errors in order to identify or estimate the optimal operating state of the power system. Two different objective function formulations are assessed by PSO. The first formulation is the Weighted Least Square (WLS) and the second one is the Iteratively Reweighted Least Squares (IRLS) implementation of the Weighted Least Absolute Value (WLAV). Both solutions are compared with a Newton-Raphson (NR) power flow solution using an IEEE 6-bus test system.

State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

False data injection (FDI) attacks have recently been introduced as an important class of cyber-attacks against power system state estimation. Utilizing vulnerabilities in information systems, attackers can inject well-constructed false data and stealthily misguide results of state estimation. However, only measurements, such as power flows and bus injections, are coordinately modified in FDI attacks, which may result in a larger number of modified measurements in constructing such attacks. In this article, we propose a network parameter coordinated false data injection (NP-FDI) attack to reduce the number of attacked measurements, where expected changes of system states and modifications of network parameters are well coordinated. Analysis of minimal attack set at a single line gives feasible conditions in reducing the number of attacked measurements. A sparse attack strategy is designed to obtain the minimal attack set of the whole grid, which can also be applied to cases with incomplete topology information. An extension to NP-FDI attacks with incomplete line impedance is presented, where the required line impedance can be estimated from local measurements adjacent to the targeted branch. Based on simulations in the IEEE 14-bus and IEEE 118-bus test systems, performance of the proposed NP-FDI attacks on sparsity and stealth are evaluated.

State estimation in power systems is utilized to provide an ongoing data set for controlling and observing power systems' performance. There are a few traditional methods like Weighted Least Square (WLS), Weighted Least Average Value (WLAV), and settled utilizing iterative techniques, for example, Gauss-Newton methods. However, these techniques are sensitive to the operating conditions of power systems and uncertainties associated with Renewable Energy Systems (RESs). These techniques yield poor results in the presence of missing data arising out of unreliable communication networks and false data injection, as in the case of a cyber-attack. This paper proposes Bi-directional LSTM (BiLSTM) based Machine Learning method to forecast the state values of power systems. The BiLSTM model utilizes forward and backward layers, having bidirectional memory, making it capable of estimating both past and future hidden layers data. This paper investigates the performance of the proposed method studied on different test system datasets. The efficacy of the proposed method is investigated by comparing the obtained results with other Machine Learning techniques results in the literature. Further, the BiLSTM model's robustness for state estimation is analyzed in the presence of Gaussian noise and missing data points.

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios.

This paper aims to assess the performance of linear state estimation (SE) processes of power systems relying on synchrophasor measurements. The performance assessment is conducted with respect to two different families of SE algorithms, i.e., static ones represented by weighted least squares (WLS) and recursive ones represented by Kalman filter (KF). To this end, this paper firstly recalls the analytical formulation of linearWLS state estimator (LWLS-SE) and Discrete KF state estimator (DKF-SE). We formally quantify the differences in the performance of the two algorithms. The validation of this result, together with the comprehensive performance evaluation of the considered state estimators, is carried out using two case studies, representing distribution (IEEE 123-bus test feeder) and transmission (IEEE 39-bus test system) networks. As a further contribution, this paper validates the correctness of the most common process model adopted in DKF-SE of power systems.

Growth in power systems has led to an increase in operational complexity, highlighting the importance of maintaining them in optimal conditions. State estimation in electric power systems is a crucial tool for determining the state of transmission networks through the use of sensors and topology information. This information is then utilized for contingency analysis and error detection/identification to ensure system reliability. To maintain optimal power system operations, state estimation and its associated techniques are critical components. This work focuses on analyzing DC state estimation using a statistical method and weighted least squares methodology. Anomalous measurements are filtered and corrected using Chi-Square. The algorithm was developed in MATLAB and verified in DIgSILENT PowerFactory. The IEEE 14-bus system, with added wind turbines, was used as a test system to determine security in the power grid through estimator confidence parameters. The results provide valuable insights into the efficacy of the DC state estimation process.

Guest Editorial: Situational awareness of integrated energy systems

Recently a new class of security problem in a power system state estimation was defined by false data injection (cyber-attack). The deliberate injection by an adversary would not be expected to follow the same patterns as random bad data. False data injection can be launched in measurements set, topology and parameters network data. Identify clearly its injection point is very important to provide a suitable correction of the output states. In this paper is presented an identification strategy for false data injection in power system state estimation input data based on a deep learning. Evaluation of the presented solution is done through Monte Carlo simulation considering different test scenarios in the IEEE 14-bus test system. The results confirm that the proposed algorithm is a potential tool to identify accurately the false data injection point in power system state estimation.