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

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.

Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India)

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.

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.

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.

This study developed a rational approach to the consideration of solar photovoltaic (PV) power plants applied to electric utility systems, then performed a requirements assessment and preliminary impact and penetration analyses by studying PV generation in three actual utility systems. Conventional utility loss-of-load probability and production simulation methods were used, along with a PV plant performance model developed for the study.Evaluations were based on comparison of total utility generation system costs with and without PV plants. The study is reported under the following sections: 1) geographic areas for photovoltaic power plants; 2) siting of photovoltaic power plants; 3) electric utility characteristics; 4) photovoltaic power plant characteristics; 5) methods of study; 6) study inputs; 7) results; 8) cost and performance goals for viability; 9) reference PV selection; 10) reference PV plant definitions; 11) comparison with conventional generation; 12) requirements for PV applications; 13) preliminary impact analysis; and 14) preliminary penetration analysis. (WHK)

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.

Detection and isolation of faulty line in an active distribution network using intelligent numerical relay

To adapt to the distribution network unobservable problem from the scarcity of real-time measurements, the distribution system state estimation (DSSE) method based on hybrid measurements of the supervisory control and data acquisition (SCADA) and the advanced metering infrastructure (AMI) is proposed. Firstly, the ratio of energy data from AMI is used to construct pseudo-measurements, which satisfies the observability of DSSE. Secondly, multiple SCADA acquisition snapshots joint state estimation model is constructed by the SCADA measurements and the energy data of AMI with multiple snapshots. In a rectangular coordinate system, the joint state estimation model and the weighted least squares (WLS) method are described in quadratic polynomial form, leading to a quadratic constraint quadratic estimation (QCQE) model. The QCQE model can decouple the modeling and algorithm implementation of state estimation and enhance the efficiency of algorithm implementation significantly. Otherwise, more state estimation mature algorithms can be adopted for the joint state estimation model in quadratic polynomial form. Simulations on the IEEE 33-bus system verify the accuracy and effectiveness of the proposed method.

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.

Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

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.

Large-scale integration of Wind Generators (WGs) with distribution systems is underway right across the globe in a drive to harness green energy. The Doubly Fed Induction Generator (DFIG) is an important type of WG due to its robustness and versatility. Its accurate and efficient modeling is very important in distribution systems planning and analysis studies, as the older approximate representation method (the constant PQ model) is no longer sufficient given the scale of integration of WGs. This thesis proposes a new three-phase model for the DFIG, compatible with unbalanced three-phase distribution systems, by deriving an analytical representation of its three major components, namely the wind turbine, the voltage source converter, and the wound-rotor induction machine. The proposed model has a set of nonlinear equations that yields the total three-phase active and reactive powers injected into the grid by the DFIG as a function of the grid voltage and wind turbine parameters. This proposed model is integrated with a three-phased unbalanced power flow method and reported in this thesis. The proposed method opens up a new way to conduct power flow studies on unbalanced distribution systems with WGs. The proposed DFIG model is verified using Matlab-Simulink. IEEE 37-bus test system data from the IEEE Distribution System sub-committee is used to benchmark the results of the power flow method.

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

Global state estimation for whole transmission and distribution networks