scholarly journals Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

2021 ◽  
Vol 9 ◽  
Author(s):  
Xingzhen Bai ◽  
Xinlei Zheng ◽  
Leijiao Ge ◽  
Feiyu Qin ◽  
Yuanliang Li
Keyword(s):  
State Estimation ◽  
Data Transmission ◽  
Distribution System ◽  
Unscented Kalman Filter ◽  
Test System ◽  
The State ◽  
Distributed Generations ◽  
Error Covariance ◽  
Active Distribution System ◽  
Event Triggered

In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to the estimator. It can efficiently reduce the amount of data transmission while guaranteeing the performance of system state estimation. Second, an event-triggered unscented Kalman filter (ET-UKF) algorithm is proposed to conduct the state estimation of the ADS with mixed measurements. To this end, the unscented transform (UT) technique is employed to approximate the probability distribution of the state variable after nonlinear transformation, which can reach more than second order, and then, an upper bound of the filtering error covariance is derived and subsequently minimized at each iteration. The gain of the desired filter is obtained recursively by following a certain set of recursions. Finally, the effectiveness of the proposed method is demonstrated by using the IEEE-34 distribution test system.

scholarly journals A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1967
Author(s):  
Gaurav Kumar Roy ◽  
Marco Pau ◽  
Ferdinanda Ponci ◽  
Antonello Monti
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Estimation Algorithm ◽  
The State ◽  
Second Step ◽  
Distribution Grid ◽  
Dc Distribution ◽  
Attractive Option ◽  
Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

scholarly journals Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2301
Author(s):  
Yun-Sung Cho ◽  
Yun-Hyuk Choi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
State Estimation ◽  
Measurement Errors ◽  
Large Scale ◽  
Test System ◽  
The State ◽  
Inner Function ◽  
Outer Function ◽  
Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

scholarly journals State Estimation and Event-Triggered Control for Cyber-Physical Systems under Malicious Attack

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yongzhen Guo ◽  
Baijing Han ◽  
Weiping Wang ◽  
Manman Yuan
Keyword(s):  
State Estimation ◽  
Finite Time ◽  
Cyber Physical Systems ◽  
The State ◽  
State Information ◽  
Physical Systems ◽  
Malicious Attack ◽  
Event Triggered

This paper is concerned with the security state estimation and event-triggered control of cyber-physical systems (CPSs) under malicious attack. Aiming at this problem, a finite-time observer is designed to estimate the state of the system successfully. Then, according to the state information, the event-triggered controller is designed through the event-triggered communication. It is proved that the system is uniformly and finally bounded. Finally, the effectiveness of the proposed method is verified by a simulation example.

scholarly journals Smart Grid State Estimation with PMUs Time Synchronization Errors

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5148
Author(s):  
Marco Todescato ◽  
Ruggero Carli ◽  
Luca Schenato ◽  
Grazia Barchi
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Time Synchronization ◽  
The State ◽  
Estimation Accuracy ◽  
Real Field ◽  
State Variables ◽  
Smart Power ◽  
Actual Performance ◽  
Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.

scholarly journals Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

2019 ◽  
Vol 8 (3) ◽  
pp. 978-984
Author(s):  
Nur Ainna Shakinah Abas ◽  
Ismail Musirin ◽  
Shahrizal Jelani ◽  
Mohd Helmi Mansor ◽  
Naeem M. S. Honnoon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Distributed Generation ◽  
Distribution System ◽  
Optimization Technique ◽  
Evolutionary Programming ◽  
Test System ◽  
Voltage Profile ◽  
Power Losses ◽  
Programming Technique ◽  
Distributed Generations

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

scholarly journals The Improvement of Weighted Least Square State Estimation Accuracy Using Optimal PMU Placement

2020 ◽  
Vol 15 ◽  
Keyword(s):  
State Estimation ◽  
System Analysis ◽  
Test System ◽  
The State ◽  
Least Square ◽  
Optimal Placement ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Measurement Units ◽  
A New Technique

This paper proposed a new technique to improve the state estimation performance by optimal placement of phasor measurement units (OPP), the proposed technique is based on Simulating Annealing (SA) algorithm for OPP by comparing between the SA solution sets and choosing the optimal location of PMUs to enhance the state estimation performance. The proposed technique has been tested through IEEE 24 bus test system using power system analysis toolbox in MATLAB program. In this paper the root mean squared deviation (RMSD) has been used to determine the state estimation performance, the simulation result demonstrates that the proposed method proved its effectiveness to be one of the best methods used to improve the state estimation performance

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