A fast Broyden's approximation‐based weighted least square state estimation for power systems

Subramaniyan, Shanmugapriya; Duraisamy, Maharajan

doi:10.1002/jnm.2831

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…In [46], SCADA, µPMU, and Smart Meter (SM) data were used for unbalanced DNs, where Kalman smoother and Expectation Maximization (EM) methods were used to manage the missing measurements and combine measurements in different time samples, respectively. In [47], a static state estimator was used to perform DSSE calculations using both SCADA and PMU measurements. When SCADA measurements were not available, a fast state reconstruction algorithm was applied using only PMU and pseudo-measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

et al. 2023

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Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.

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Section: Literature Reviewmentioning

confidence: 99%

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

et al. 2023

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“…In Weighted least square estimation, the difference between the estimated and actual value can be minimized by sum of squares approach. WLS faces issues difficulty error in state estimation, alternative methods are also there to solve this problem [12]- [13]. Dynamic State estimation is the approach to change their behavior with respect to time.…”

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confidence: 99%

Power Flow Parameter Estimation in Power System Using Machine Learning Techniques Under Varying Load Conditions

Ramasamy¹,

Ganesan²,

Arunachalam³

2022

IJEER

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In power transmission network state estimation is more complex and the measurements are critical in nature. Estimation of power flow parameters such as voltage magnitude and phasor angle in a power system is challenging when the loads are varying. The objective of the work is to estimate the voltage magnitude and phase angle using machine learning techniques. Some of the Machine learning techniques are decision trees (DT), support vector machines (SVM), ensemble boost (E-Boost), ensemble bags (E-bag), and artificial neural networks (ANN) are proposed in this work. Among these methods, the best machine learning techniques are selected for this study based on performance metrics. Performance metrics are Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Neural network produces minimum error when compared to other ML Techniques. Among three Performance Metrics MSE provides minimum error and is used to predict the exact model in this work. Therefore, it is concluded that the neural network can predict voltage and angle accurately under various load conditions in the power system effectively. Neural Network (NN) is applied to different load condition, and performance metrics are computed. To validate the proposed work, IEEE 14 and IEEE 30 bus systems are considered. The predicted value is compared to the actual value for all the load variation and residues are measured. The regression learner software in MATLAB is used to implement ML approaches in this work. The Outcome of this proposed work is used in phasor measurement units. The predicted value of voltage and angle using a neural network can be used to minimize the voltage magnitude and phase angle error in phasor Measurement Units (PMU).

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A GAN-based Adversarial Attack Method for Data-driven State Estimation

Zhang

Miao

et al. 2023

2023 IEEE 6th International Electrical and Energy Conference (CIEEC)

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A fast Broyden's approximation‐based weighted least square state estimation for power systems

Cited by 5 publications

References 36 publications

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

Power Flow Parameter Estimation in Power System Using Machine Learning Techniques Under Varying Load Conditions

A GAN-based Adversarial Attack Method for Data-driven State Estimation

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