Method For Power System State Estimation Research Articles

A distributed multi-area power system state estimation method based on generalized loss function

For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution, and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true, and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise and bad data. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system.

New power system operational state estimation with cluster of electric vehicles

The access of distributed generation (DG) and a large number of electric vehicles (EVs) have changed the operation mode of power system. Its reliability and stability are facing more and more challenges. Therefore, it is very important to accurately estimate the state of the power system. This paper discusses a new power system state estimation method that is based on the shuffled frog leaping pigeon-inspired optimization algorithm (SFL-PIOA). Firstly, establish EV charging load model and distributed generation probability model (including photovoltaic power generation and wind power generation). Then, considering EVs and DG, the state estimation model of the new power system is built. The objective function and constraint conditions are established, and then the improved SFL-PIOA is used to solve the model. Finally, a simulation example is given to compare the improved algorithms (SFL-PIOA) to initial algorithm (PIOA). The results verify the feasibility and effectiveness of the improved method.

A SCADA/PMU hybrid measurement state estimation method considering load uncertainty

This paper proposes a power system state estimation method combining the Monte Carlo method and the SCADA/PMU hybrid measurement state estimation algorithm. Quantify the impact of load uncertainty on the state estimation results to reduce the influence of power system measurement error. The results show that the method takes into account the uncertainty of load measurement and the influence of flexible load, and accurately estimate the operating state of the power system.

Power System State Estimation Based on PLS-ELM

In order to effectively improve the computational accuracy and robustness of state estimation, a power system state estimation method based on partial least square (PLS) and extreme learning machine (ELM) is proposed in this paper, which combines artificial intelligence technology with grid data. In order to solve the problem of strong correlation between measurements, PLS is used to extract important information and select variables for each measurement, and the optimal variables are input into the ELM model, thus the PLS-ELM model of state quantities is established. Finally, this method is compared with other methods. Experimental results show that the proposed method reduces the complexity of the model, and has high estimation accuracy and strong robustness.

Multi-model power system state estimation based on linear transition models

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.

A New Power System State Estimation Method Based on External Student zed Residuals

Power system state estimation is the core of power system online monitoring, analysis and control function, playing an important role in the intelligent analysis and decision-making of power grid dispatching. The estimation results directly affect the correctness and effectiveness of the operation analysis and decision system, how to improve the accuracy of state estimation is an important content of the research. Detection and identification of bad data is one of the important function of state estimation in power system, its purpose is to eliminate a few bad data by the measurement data and improve the reliability of state estimation, which is of great significance to the safe operation of power system. External studentized residuals are used as criterion to detect and identify bad data in power system state estimation, improving the shortcomings of standardized residuals in traditional bad data detection and identification. Then the exponential function weighted least squares method based on external studentized residuals is proposed. The form of the original algorithm is modified using the principle of equivalent weight. Simulation is carried out according to the different situation of bad data to verify its performance. The performance is compared with the traditional robust estimation method and basic weighted least squares method.

Distributed State Estimation for Interconnected Synchronous Generators

Abstract In contrast to the traditional centralised power system state estimation methods, this paper investigates the optimal filtering problem for distributed dynamic systems. Specifically, the interconnected synchronous generators are modelled as a state-space linear equation where sensors are deployed to obtain measurements. As the synchronous generator states are unknown, the estimation is required to know the overall operating conditions of large-scale complex power networks. Basically, the proposed algorithm is based on the minimization of the mean squared estimation error, and the optimal gain is computed by exchanging information with their neighboring estimators. Afterwards, the convergence of the developed algorithm is proved so that it can be applied to real-time applications in modern smart grids. Finally, simulation results demonstrate the efficacy of the developed approach.

Distributed State Estimation Over Unreliable Communication Networks With an Application to Smart Grids

In contrast to the traditional centralized power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modeled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Second, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers with an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to losing network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighboring gains are computed to reach a consensus estimation after exchanging their information with the neighboring estimators. Then, the convergence of the developed algorithm is theoretically proved. Afterward, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

Hierarchical Multi-Area State Estimation via Sensitivity Function Exchanges

A new hierarchical multi-area power system state estimation method is proposed in this paper. Instead of exchanging boundary measurements or state estimates, the proposed technique is based on exchanging the sensitivity functions of local state estimators. The main benefit of the proposed scheme is the improved convergence speed, which also reduces the amount of information exchange required. Extensive numerical results involving IEEE standard systems and a utility scale system are presented.

RETRACTED: Jin et al. A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology. Energies 2015, 8, 2769–2787

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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.

Hierarchical state estimation using a fast rectangular-coordinate method (power system analysis computing)

A hierarchical computing scheme for power system state estimation is developed by carefully considering mismatches arising from the system decomposition. The method makes use of an extension of a fast second-order load-flow method that allows a fixed Jacobian matrix to be used in the hierarchical algorithm. In the problem formulation, the power network is decomposed into two or more subsystems; the interaction among them is taken into account through the tie-line bus voltages. The hierarchical structure of the method consists of two levels: the upper level, where the optimal tie-line bus voltages are evaluated; and the lower level, where the optimal states of each subsystem are determined by minimizing a cost function that involves the entire system. Three test cases using the method are reported here and the test results are compared with those obtained by another hierarchical power system state estimation method. >