Topology identification method of distribution network based on branch active power

Abstract Because of low measurement redundancy and frequent switch changes, it is difficult to identify the correct topology structure. In this paper, a topology recognition method of distribution network based on branch active power is proposed. Firstly, branch active power residual algorithm is used to identify the topological structure. The topology obtained by this method has the highest matching degree with the real-time measured data. Then genetic algorithm is used to optimize the inverse recognition of power grid topology. The numerical example shows that the method is reasonable, effective, rapid and simple. It also has good adaptability with a large number of measurement errors.

Download Full-text

Decision letter for "Optimization of DC‐link voltage regulator using Bat algorithm for proportional resonant controller‐based current control of shunt active power filter in distribution network"

10.1002/2050-7038.12369/v2/decision1 ◽

2020 ◽

Keyword(s):

Distribution Network ◽

Active Power Filter ◽

Bat Algorithm ◽

Active Power ◽

Current Control ◽

Voltage Regulator ◽

Shunt Active Power Filter ◽

Power Filter ◽

Resonant Controller

Download Full-text

Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

Energies ◽

10.3390/en14113242 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3242

Author(s):

Hamid Mirshekali ◽

Rahman Dashti ◽

Karsten Handrup ◽

Hamid Reza Shaker

Keyword(s):

Fault Location ◽

Network Reliability ◽

Distribution Network ◽

Estimation Method ◽

Electrical Energy ◽

Distribution Networks ◽

Active Power ◽

Financial Loss ◽

Smart Meters ◽

The Real

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

Download Full-text

A Harmonic Source Location Method Based on PMU Measurements in Distribution Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.775.409 ◽

2015 ◽

Vol 775 ◽

pp. 409-414

Author(s):

Bing Jun Li ◽

Su Quan Zhou ◽

Xiao Xiang Lun

Keyword(s):

Power System ◽

State Estimation ◽

Distribution Network ◽

Measured Data ◽

Optimal Measurement ◽

Location Method ◽

Measurement Placement ◽

Minimum Number ◽

Harmonic Source ◽

The Cost

It is of great importance to identify the location of the harmonic sources for the harmonic governance in the power system. Applied with optimal measurement placement (OMP) and harmonic state estimation (HSE), this paper presents a novel process based on PMU measurements to locate the harmonic sources in the distribution network. Considering the cost and the observability, the OMP can provide a scheme of the measurement placement with the minimum number of PMU measurements. In order to simplify the HSE equation, the measured data are converted to the form of voltage by the method proposed in this paper.By solving the HSE equation, the location and magnitude of the harmonic source are evaluated. The methodology is applied to the IEEE 33-bus system, and the obtained results are properly analyzed.

Download Full-text

A novel fault section locating method based on distance matching degree in distribution network

Protection and Control of Modern Power Systems ◽

10.1186/s41601-021-00194-y ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Zhenxing Li ◽

Jialing Wan ◽

Pengfei Wang ◽

Hanli Weng ◽

Zhenhua Li

Keyword(s):

Single Phase ◽

Distribution Network ◽

Matlab Simulink ◽

Grounding Resistance ◽

Blind Zone ◽

System Transition ◽

Transition Resistance ◽

Zero Sequence ◽

Matching Degree ◽

Amplitude Characteristics

AbstractFault section location of a single-phase grounding fault is affected by the neutral grounding mode of the system, transition resistance, and the blind zone. A fault section locating method based on an amplitude feature and an intelligent distance algorithm is proposed to eliminate the influence of the above factors. By analyzing and comparing the amplitude characteristics of the zero-sequence current transient components at both ends of the healthy section and the faulty section, a distance algorithm with strong abnormal data immune capability is introduced in this paper. The matching degree of the amplitude characteristics at both ends of the feeder section are used as the criterion and by comparing with the set threshold, the faulty section is effectively determined. Finally, simulations using Matlab/Simulink and PSCAD/EMTDC show that the proposed section locating method can locate the faulty section accurately, and is not affected by grounding mode, grounding resistance, or the blind zone.

Download Full-text

Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models

Journal of Turbomachinery ◽

10.1115/1.2183315 ◽

2005 ◽

Vol 128 (3) ◽

pp. 444-454 ◽

Cited By ~ 9

Author(s):

M. Venturini

Keyword(s):

Measurement Errors ◽

Learning Algorithm ◽

Back Propagation ◽

Network Models ◽

Transient Behavior ◽

Optimal Number ◽

Measured Data ◽

Outlet Temperature ◽

Neural Network Models ◽

Hidden Layer

In the paper, self-adapting models capable of reproducing time-dependent data with high computational speed are investigated. The considered models are recurrent feed-forward neural networks (RNNs) with one feedback loop in a recursive computational structure, trained by using a back-propagation learning algorithm. The data used for both training and testing the RNNs have been generated by means of a nonlinear physics-based model for compressor dynamic simulation, which was calibrated on a multistage axial-centrifugal small size compressor. The first step of the analysis is the selection of the compressor maneuver to be used for optimizing RNN training. The subsequent step consists in evaluating the most appropriate RNN structure (optimal number of neurons in the hidden layer and number of outputs) and RNN proper delay time. Then, the robustness of the model response towards measurement uncertainty is ascertained, by comparing the performance of RNNs trained on data uncorrupted or corrupted with measurement errors with respect to the simulation of data corrupted with measurement errors. Finally, the best RNN model is tested on field data taken on the axial-centrifugal compressor on which the physics-based model was calibrated, by comparing physics-based model and RNN predictions against measured data. The comparison between RNN predictions and measured data shows that the agreement can be considered acceptable for inlet pressure, outlet pressure and outlet temperature, while errors are significant for inlet mass flow rate.

Download Full-text