Performance Assessment of Synchronized Phasor Measurement-Based Parameter Estimation for Distribution Networks

Various new energy, energy storage, and adjustable loads are incorporated into the distribution network to participate in the adjustment of the distribution network side, making the operation of the distribution network side more flexible. As an important part of the distribution network, the power lines of the distribution network are directly affected by this change. Therefore, the online detection of distribution network parameters in the distribution network becomes more and more important. Currently, smart devices can perfor15wm parameter estimation and topology recognition in a data-driven manner. However, many current methods require the use of phasor measurement units to measure the phase angle of the node voltage, which is not feasible for current relatively traditional power distribution networks. Based on the more accurate power detection of the current distribution network, this paper proposes a method for estimation of the line parameters of the distribution network without considering the voltage phase angle information. Based on the limited measurement data of the distribution network, the measured electricity is all amplitude. By modeling the transmission line of the distribution network, the problem of solving the transmission line parameters is transformed into a binary nonlinear regression problem. The binary nonlinear least-squares regression method is used to identify the transmission line parameters, and the residual analysis is used to eliminate anomalies data. After that, the reliability and accuracy of the method are analyzed for the identification results under different data volumes and different error levels. Finally, the line parameters within a day were identified under the condition of line temperature changes. By comparing the results of electrothermal coupling, the changes of line parameters in a day can be more accurately identified.

State estimator is an important tool for online monitoring, analysis and control of power systems. With the increased use of phasor measurement units in a power grid, utilization of synchro phasor measurements in State Estimation (SE) becomes essential. Conventional State Estimation (CSE) has been done from measurements obtained by Supervisory Control And Data Acquisition (SCADA) systems. Linear State Estimation (LSE) is done using phasor measurements alone. The paper presents a hybrid method called Two Stage State Estimation (TSSE), wherein the states of the system are estimated by incorporating both the SCADA measurements and phasor measurements. Simulations are done on IEEE 14 bus, IEEE 30 bus and IEEE 118 bus systems and results are compared.

Especially influenced by the rising number of renewable and distributed power generation plants and so-called unbundling, the controllability and protection of power supply systems are becoming ever more complex as these systems are developed further. To securely meet these challenges a reliable observability of the power system is needed. Novel digital measurement equipment such as phasor measurement units (PMU) in transmission and distribution networks and smart meters in low voltage grids deliver the system parameters needed to perform a system state estimation analysis. This paper discusses the use of PMUs to support the operation of virtual power plants (VPP). It additionally presents methods, which have already been tested in different simulations, to optimally select the locations of measurement units to ensure system observability.

In practice, the performance of distribution feeder parameter estimation is limited by the measurement conditions in distribution networks. An accurate mathematical model that considers limited phasor measurements in distribution networks is necessary to estimate feeder parameters. This paper presents a set of modified parameter estimation models for unbalanced three-phase distribution feeders that only require the measurements of voltage amplitudes and power flows. To simplify the calculation process and improve the estimated results, a method combined with a radial basis function neural network (RBFNN) and multi-run optimization method (MRO), namely RBFNN-MRO, is proposed to calculate the parameters of distribution feeders. The relationship between the feeder parameters and the measurement data from the two terminals of the feeder can be mapped perfectly using the RBFNN. Furthermore, the random errors in the measurement device were eliminated using the proposed RBFNN-MRO algorithm. The RBFNN-MRO algorithm can limit the number of neurons in the hidden layer and substantially reduce the training time for each RBFNN. The feasibility of the proposed method was verified using four IEEE test systems. The proposed RBFNN-MRO and RBFNN methods were compared using the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN-MRO method has excellent potential for improving the accuracy of feeder parameter estimation even without synchronized phasor measurement.

Efficient fault treatment of active distribution network is an important guarantee to ensure the steady-state reliability of the system. In order to improve the accuracy of distribution network fault identification and analysis, a fault processing method based on deep learning is proposed in this paper. This method collects massive heterogeneous data sets using patrol robot to realize real-time perception and accurate acquisition of distribution network status. Relying on the processing mode of distribution network cloud edge collaboration, the principal component analysis method is used at the edge to effectively remove redundant data, providing a complete and reliable data support for the deep network model. Meanwhile, the attention mechanism is added to the cloud to improve the depth confidence network, further realizing the extraction of useful feature information for complex data sets and avoiding the interference of irrelevant information on the recognition results. The simulation experiment is based on the actual active distribution network model. The experimental results show that the fault identification accuracy of the proposed method can reach 0.9255, indicating an excellent distribution network fault identification and analysis ability to support safe operation of active distribution network.

The traditional power flow and fault analysis methods fail to meet the requirements in both performance and accuracy aspects in the distribution network applications. This is due to the unbalanced multiphase nature of the distribution network and also due to the emerging penetration of renewable generation at the distribution level. This paper proposes a comprehensive method for power flow solution and fault analysis of multiphase unbalanced distribution networks with high penetration of inverter-interfaced DERs. Also, the self-protection scheme employed for the inverter of the DERs is also formulated in the proposed method. Performance of the proposed method has been assessed by simulations on a sample distribution network.

Power systems measurements are often performed with current and voltage signals derived from instrument transformer secondary circuits. Errors introduced by these transformers are a recognized component of input data errors in supervisory control and data acquisition and phasor measurements. Correcting these errors by {in situ} calibration is an expensive option; hence, it is seldom employed except in a few special cases. This article proposes a new method for calibrating instrument transformers utilizing phasor measurements by estimating instrument transformers' ratio correction factors and phase angle correction factors. The method focuses on the fundamental frequency component of phasor measurements, and, in contrast with some of the techniques proposed in the literature, it does not require accurate instrument transformer models and can be executed as often as needed to keep the calibration up to date. Two alternative calibration techniques are introduced along with the simulation results to prove the feasibility.

Scope of this paper is to investigate the effect of different load and distributed generation (photovoltaic systems and wind generators) modelling approaches for the steady-state analysis of distribution networks and the development of ancillary services by system operators. Specifically, five different modelling approaches are considered, based on different assumptions. The power flow problem is solved by applying the examined models to the 33-bus benchmark distribution network and the derived results are statistically analyzed. The accuracy, reliability and performance of the models is evaluated in terms of the calculated voltages, power losses and computational burden.

EVs are recognized as distributed loads and power sources with random motion characteristics in the power distribution networks (PDN); therefore, they serve as connecting points for the distribution network and transportation network (PDN-TN), and have significant impact on the stability and reliability of the routine operation of the coupled networks. In order to guarantee the safe operation of PDN-TN, it is necessary to evaluate the reliability of this coupled network. In this paper, the trip chaining theory is applied to describe the travel plan of EVs, and semi-dynamic flow allocation (SDTA) method is adopted to propose a simulation calculation method of energy flow between two networks based on user equalization (UE) criterion. The new reliability evaluation index for energy flow between the coupled networks is further proposed to better evaluate the reliability. Taking into account the daily trip characteristics of EVs, vehicles types, and charging modes, a coupled network reliability evaluation method based on sequential Monte Carlo simulation is put forward. Finally, the practicability of the proposed method is verified by an IEEE-RBTS BUS-6 test system and a 12-node transportation network.

Electric vehicles (EVs) are receiving increasing attention because of the development of renewable energy. Operators are promoting the use of EVs as virtual power plants (VPP) to improve the reliability of existing power grids. However, the integration of EVs will have a significant impact on the stability of the distribution network. In view of this, a dispatching cost model for EVs is proposed based on active power and reactive power, which is aggregated into a comprehensive cost model for VPP. In addition, corresponding hierarchical control strategies and dispatching optimization models for VPP are proposed to minimize the total generation cost of generators and VPP in the distribution network based on a robust optimization approach. The simulation tests the proposed hierarchical control strategy and dispatching optimization model on an IEEE9 node system as a test distribution network. The results show that the EV cluster as a VPP effectively reduces the output of other generators and the loss of active power for the whole distribution network, improving the power and voltage stability of distribution network nodes.

Review on vulnerability analysis of power distribution network

Power Flow solutions based on Fortescue's Method of Symmetrical Components have been so far limited only to three-phase transmission networks. Complete network solution could be reached only by applying standard methods based on phase coordinate system. This paper shows that it is possible to apply Fortescue's General Transformations for power flow solution on complete network. Special matrices for handling distribution network configurations are derived. Presented approach offers greater computational performances when solving power flow problem in distribution networks.

A modified Gauss–Seidel algorithm of three-phase power flow analysis in distribution networks

Analysis of transient stability in distribution systems with distributed generation

The uptake of distribution generation on electricity distribution networks imposes the operators to install new measurement devices such as phasor measurement units to achieve network observability. In this paper, we propose a framework for estimating synchronized phasor measurements for a virtual node using the measurements from the other nodes in the network. This system uses a machine learning method, in particular supervised regression models, to provide estimates. We show the performance of the proposed framework comparing two widely used regression methods i.e., Generalized Linear Models and Artificial Neural Networks. We extensively evaluate the proposed approach utilizing a real-world dataset collected from a medium voltage ring feeder. Our results indicate very low error rates; the average error for voltage magnitude was approx. 0.2V while for phase angle was 0.7mrad. Such low errors indicate the potential for reducing the scale of the measuring infrastructure required on distribution networks and increasing their reliability.