State Estimation Program Research Articles

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.

A rapid state estimation method for calculating transmission capacity despite cyber security concerns

AbstractThe present paper simulates cyberattacks in DIgSILENT for energy management and state estimation. Assuming hackers have adequate information to alter input data, they can alter the power market and the available transmission capability (ATC). Detection programs may fail if false information is coordinated in the presence of error information. The minimum number of network measurements before the state estimation program converges is 54% of the total measurements. In the new method, weighted least squares improve the state estimation speed. The new method can also improve the speed of ATC. In addition to identifying the cyberattack, the algorithm in the present paper can be used to identify the target of the attack. It also uses complete and correct information and even incorrect measurements to report state estimator errors. The present paper shows that ATC values follow the inverse pattern of load and loss. A real sample network is presented at the end of the paper to test the suggested method.

Quality of the State Estimation of an Electric Power System and Its Numerical Measurement

The quality of the state estimation (SE) of an electric power system (EPS) is essentially a subjective measure in established practice. A generally-accepted definition of the term “SE quality” and a method of measuring it are lacking. Automated analysis of a change in SE quality after a model correction when adjusting the model and performing calculations of the electrical modes during operation is hindered because of this. Comparison of the numerical values of SE quality obtained from use of programs with various algorithms would serve as a constructive stimulus for the improvement of these algorithms. A definition of the term “SE quality” and a method of measuring it are proposed. Materials from the experience of preparing and conducting calculations for SE programs for a significant number of power systems of UES of Russia have been used.

Iraqi Super Grid Network State Estimation Using PSO Technique

Power System State Estimation (PSSE) became a main subject in the operation of power systems through its important role in ensuring the secure and economical operation of the power system. In this work, two approaches were proposed and implemented in order to search for the optimal solution of state estimation in power systems, the first approach used a conventional state estimation program based on Weighted Least Square (WLS) method, and the second one used an intelligent technique based on Particle Swarm Optimization (PSO). All programs were implemented using MATLAB and developed to solve the state estimation problem of the Iraqi Super Grid network (400kV). The results showed that the PSO is more accurate and it convergence close to the optimal solution

Technical and non‐technical losses calculation in distribution grids using a defined equivalent operational impedance

This study presents an approach to calculate average technical losses (TLoss) and non-technical losses (NTLoss) in distribution grids using an equivalent operational impedance (EOI) calculated from a load flow solution for the transformers’ average loads, obtained from customers’ electric bills. If transformers are supervised, i.e. have available active and reactive power injections measurements or estimates from a state estimator program, the transformers’ average technical and non-technical losses can be calculated by running a load flow for the grid supplied by the transformer and using the definition of EOI presented in this study. For unsupervised transformers, i.e. no measurements or estimates are available, pseudo measurements of average active and reactive power injections can be obtained by a procedure that uses active and reactive power measurements at the feeder substation. The use of this methodology by electric utilities allows the calculation of technical and non-technical losses more accurately, as well as the inclusion of non-technical losses in the distribution networks operation planning. Results using the IEEE 13-bus test system and an urban real distribution grid are presented to demonstrate the effectiveness of the proposed methodology.

Forecasting the Total Transfer Capability of Intersystem Lines for On-line Control of Electric Power System Operation

Abstract: The paper develops a technique of total transfer capability forecasting in the controlled lines. The technique involves using dynamic state estimation, modified state estimation, and artificial neural networks. Dynamic state estimation yields the predicted values of the state variables. Modified state estimation calculates the total transfer capability on the basis of the forecast data. The obtained state is called the resultant steady state. Depending on operation constraints, the modified state estimation program parameters to be adjusted to provide optimal result of the modified state estimation are taken from the database by using ANN, and the resultant state variables of the interconnected power system are calculated. Artificial neural networks are used to quickly adjust the modified state estimator in real time.

Identifying Parameter Errors via Multiple Measurement Scans

This paper investigates the problem of network parameter error detection and identification in power systems. Recently, a parameter error identification method which is based on Lagrange multipliers corresponding to a single measurement scan has been developed. This paper provides an improvement of this method via the use of multiple measurement scans which increases the local redundancy at no additional cost. This leads to identification of errors which could otherwise not be identified. The paper presents a detailed analysis of the limitations of the single scan method and also shows the relationship between the number of scans, the normalized Lagrange multipliers and the normalized residuals. The presented method is easy to implement since estimation of different scans can be executed independent of each other by using an existing state estimation program. Simulations on IEEE 14-, 30-, and 118-bus systems are provided to illustrate the proposed approach of parameter error identification.

Wind power system state estimation with automatic differentiation technique

A wind power system state estimation method based on automatic differentiation technique is presented in this paper. A simplified RX model of wind turbine is introduced and used to be equivalent to wind power generator. As one of the state variables, the slip of wind power generator is introduced into state estimation process and the corresponding mathematical model is established. The proposed method takes into account the own characters of asynchronous generator and can easily combine with the original state estimation program. Furthermore, unlike the traditional method which needs to deduce all the formulas of derivatives and code them by hand, the Jacobian matrix is built automatically using automatic differentiation with sparse technique in the proposed method, the truncation error is avoided effectively, and the computational efficiency, speed and precision can be significantly improved. This paper demonstrates that the burdens of software developers are relieved greatly by automatic differentiation technique instead of hand-coded derivatives and the simplified RX model instead of the traditional RX model. Finally, it follows from the simulation results that the proposed method possesses good performance and can be well applied to state estimation for power system containing wind power generators.

Co-Optimal Placement of Measurement Devices and Their Related Communication Infrastructure in Wide Area Measurement Systems

The term wide area measurement system (WAMS) implies a system including new digital metering devices (e.g., phasor measurement unit) together with communication system which is designed for monitoring, operating, and controlling power systems. Generally, a WAMS process includes three main functions: data acquisition, data transmitting, and data processing performed by measurement devices, communication systems, and energy management systems, respectively. While these three functions are seriously interdependent, most researches carried out on this topic investigate these functions independently. In this paper, meters placement and their required communication infrastructure for state estimation program are co-optimized simultaneously. To perform this, these two planning issues are jointly formulated in a single genetic algorithm (GA) problem. Then, three IEEE test networks without any conventional measurements and communications are used to investigate the advantages of considering dependability of optimization of these two sections. The results confirm that the proposed method is advantageous over the techniques optimizing each section independently. The results indicate that while the total number of measurement devices for system observability may increase (and therefore, the observability is improved), the total cost for the plan is reduced.

Real-Time Monitoring and Analysis of Power Systems with Synchronized Phasor Measurements

State estimators are used to monitor the operating states of power systems in modem EMS. It iteratively calculates the voltage profile of the currently operating power system with voltage, current, and power measurements gathered from the entire system All the measurements are usually assumed to be obtained simultaneously. It is practically impossible, however, to maintain the synchronism of the measurement data Recently, phasor measurements synchronized via satellite are used for the operation of these power systems. This paper describes the modified state estimator used to support the processing of synchronized phasor measurements. Synchronized phasor measurements are found to provide synchronism of measurement data and improve the accuracy/redundancy of the measurement data for state estimation. The details of the developed state estimation program and some numerical results of operation are presented.

A partitioned state estimator for external network modeling

This paper presents a partitioned state estimation (SE) method for real-time external network modeling. The method utilizes an unreduced model of the external network and it is based on the SE approach. Since the external network model must not contaminate the internal system state and power mismatches at boundary buses should be minimal, the boundary measurements (tie-line flows and boundary injections) should be critical. Any type of real-time measurements (power flows and injections) can be used. The entire system state is computed by a two-pass method based on measurement model partitioning. The method is based on the fast decoupled model and it can be easily implemented by modifying an existing SE program. Partitioned observability and bad data processing schemes have been developed for the internal and external networks. The external system model can be dynamically updated when changes on topological or analog measurements happen. The method is tested on the IEEE 14-bus system and a 2040-bus system. Contingency analysis is performed and the results are compared with those of load flow (LF) studies which consider the same contingencies.

A Partitioned State Estimator for External Network Modeling

This paper presents a partitioned state estimation (SE) method for real-time external network modeling. The method utilizes an unreduced model of the external network and it is based on the SE approach. Since the external network model must not contaminate the internal system state and power mismatches at boundary buses should be minimal, the boundary measurements (tie-line flows and boundary injections) should be critical. Any type of real-time measurements (power flows and injections) can be used. The entire system state is computed by a two-pass method based on measurement model partitioning. The method is based on the fast decoupled model and it can be easily implemented by modifying an existing SE program. Partitioned observability and bad data processing schemes have been developed for the internal and external networks. The external system model can be dynamically updated when changes on topological or analog measurements happen. The method is tested on the IEEE 14-bus system and a 2040-bus system. Contingency analysis is performed and the results are compared with those of load flow (LF) studies which consider the same contingencies.

Detecting multiple solutions in state estimation in the presence of current magnitude measurements

This paper investigates the observability problem when the network contains current magnitude measurements. Use of these measurements may lead to multiple solutions and therefore to the loss of unique observability for certain measurement configurations. The paper proposes a simple method based on the commonly used measurement residual covariance matrix, in order to detect the possibility of multiple solutions for a given measurement set. The proposed test is simple to implement and uses the already available matrices and data structures of the commonly used weighted least squares state estimation programs. It can be utilized as a planning tool during the initial stages of measurement system design as well as an online observability analysis tool to enhance the capability of the existing observability programs which can not properly process the current magnitude measurements. Numerical examples are provided to demonstrate the performance of the proposed method on sample power systems.

Computational method for remote meter calibration in power systems

Measuring instruments in a power system are numbered in thousands and are dispersed geographically. The readings of those instruments are telemetered to the control centre and then used in a power system state estimation program for further processing. Field technicians calibrate these instruments routinely at great cost. The purpose of remote meter calibration is to determine, using analytical methods in the control centre, which instrument needs calibration and to estimate the calibration error. Results of many runs of the power system state estimation program are used to demonstrate the feasibility of remote meter calibration in a power system. The IEEE 14-bus test system is used for testing. It is shown that the simulated fixed (calibration) errors in all types of power system measurements can be advantageously estimated.

Clamped state solution of artificial neural network for real-time economic dispatch

This paper presents a novel method for the real-time economic dispatch using clamped state variable formulation of the Kennedy, Chua and Lin (1988) artificial neural network (ANN). An efficient economic power dispatch algorithm must use real-time load conditions and the loss penalty-factor for representation of transmission losses in power system. The approach described in this paper assumes that an interface program will calculate the penalty factors for the current power flow state, as calculated by a state estimation program. The proposed method employs an ANN to enhance the speed and capability of algorithms which may use heuristics for online use. The ability of processing feedbacks in a collective parallel analog mode enables a neural network to simulate the dynamics that represent the optimization of an objective function subjected to its constraints for a given optimization model. Different techniques may be used to simulate the neural dynamic system. In this study, the authors propose a new method of simulation called the clamped state variable (CSV) technique. The new approach is very simple and it takes smaller computer time for the algorithm to converge than the complete circuit simulation. The results obtained by the CSV method are very close to that of numerical methods and are reported in this paper. >

Effects on state estimation of time-skew errors in inter-utility data exchange

Inter-utility data exchange poses several unresolved problems. One is the error caused by time skewing of exchanged data. Three manifestations of time skewing, namely, load changes, inter-area interchange changes, and external topology changes, are addressed. The system under study is the IEEE 118-bus system. The system is modified incrementally and solved using a power flow program to simulate different operating states in time. For each operating state the system is divided into three parts, an internal system and two external systems. Selected modified external operating states are combined with the unmodified internal system and solved using a state estimator program, thus simulating exchanging data with various amounts of time skew. The results of load changes and inter-area interchange changes are predictable relationships between errors and changes. Although the actual amount of error would be difficult to determine in actual operation, it could be reliably minimized. The results of topology changes are much less predictable but are considerably easier to detect and may possibly be avoided completely.

Observability analysis and bad data processing for state estimation using Hachtel's augmented matrix method

Triangular-factorization-based observability analysis and normalized-residual-based bad-data processing are extended to state estimation using Hachtel's augmented matrix method, which is numerically robust, computationally efficient, and reasonable in extra storage requirement. It is shown that the observability analysis can be carried out in the course of triangular factorization of the augmented coefficient matrix used in Hachtel's method. The normalized residuals can be obtained using the sparse inverse of this augmented matrix. The algorithms have been successfully incorporated in the state estimation program developed at the Norwegian State Power Board. Test results on an IEE-14 bus system and a 99-bus system consisting of the main grid of southern Norway are presented. The results confirm that Hachtel's approach to state estimation provides an attractive alternative to the standard normal equations approach. >

A Hybrid State Estimator: Solving Normal Equations By Orthogonal Transformations

The standard approach to the solution of the weighted least square (WLS) state estimation in power system is the iterative normal equations method. Occasional ill-conditioning has been experienced with this method. Recently alternative solution approaches based on orthogonal transformations have been proposed. Sparsity generally suffers in these methods. In this paper, the network condition which causes ill- conditioning is studied. A hybrid approach which enjoys the same numerical robustness of the orthogonal methods with sparsity close to the standard WLS method is presented. The modification needed to implement the hybrid method on an existing standard WLS state estimation program is quite small.

Network Observability: Identification of Observable Islands and Measurement Placement

Two algorithms are presented; one for (i) testing the observability of a network and (ii) identifying the observable islands when the network is unobservable, and the other for selecting a minimal set of additional measurements to make the network observable. The two algorithms are based on triangular factorization of the gain matrix and are characterized by (i) being extremely simple, (ii) using, subroutines already in a state estimation program, and (iii) incurring very little extra computation. The design and testing of the algorithms are presented in this paper.

A Method That Combines Internal State Estimation and External Network Modeling

A method that combines internal state estimation and external network modeling is developed. The external system is represented by an unreduced load flow model. One state estimation covering both the internal system and the external system is used. The external system operating data on power injections and bus voltages are entered as pseudo-measurements. At each iteration the set of active pseudo- measurements are selected to conform with the specified variables in a load flow program. Because such a set of non-redundant measurements is used, the internal state estimation is not affected by the external system pseudo-measurements. External generation MVAR and controlled bus voltage limits are enforced. A technique is developed to make a pseudo-measurement dormant. Using the dormant-measurement technique, it is possible to maintain the same external system state estimation formulation while the PV-PQ switching takes place from iteration to iteration. The method can easily be implemented by modifying an existing state estimation program. It has been implemented in a fast model-decoupled estimation program. Because of the dormant measurement technique, the constant gain matrix evaluated at flat voltage is used in every iteration. The method has been tested on the IEEE 14- bus, 30-bus, and Brazilian 66-bus systems. Excellent results are obtained. The number of iterations for the method to converge is usually the same as the regular state estimation runs. Thus the method presented here achieves simultaneously the internal state estimation and the external network modeling without adversely affecting the internal state estimation.