Improvement in power system transient stability by using STATCOM and neural networks

Karami, Ali; Galougahi, K. Mahmoodi

doi:10.1007/s00202-019-00753-5

Cited by 29 publications

(9 citation statements)

References 51 publications

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“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

Section: A Database Buildingmentioning

confidence: 99%

“…Supervised learning techniques can also be used. For instance, in [47], the authors train a decision tree classifier to evaluate transient stability and thanks to the Fisher linear discriminant, they evaluate the sensitivity of each generator and load to stability and then [16], [103], [104] Adaptive programming [105], [106] Unsupervised learning [107] Tree-based algorithms [47], [108]- [110] Neural networks [111]- [113] define emergency control actions accordingly. In [111], the authors use a neural network to assess the generators that need to be re-dispatched and the loads that need to be shed.…”

Section: Learning a Modelmentioning

confidence: 99%

“…In the context of transient stability, in [113] a neural network is used to estimate the gain for a static synchronous compensator auxiliary controller, that needs to be adjusted according to the system operation point to obtain the desired CCT. The gain computation is heavy and thus a neural network model is proposed to reduce the computational burden.…”

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: A Database Buildingmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…In Equations (4) and (5), u sd and u sq are the voltage components of the d-axis and q-axis on the grid side after coordinate transformation; U Ld and U Lq are the voltage components of the D-STATCOM system output voltage; i d and i q are the d-axis and q-axis components of the D-STATCOM injection grid side current, respectively. The δ between the grid voltage and the output voltage of the static var compensator can be used to determine whether the system absorbs or emits reactive power.…”

Section: Control Signalmentioning

confidence: 99%

“…Various static reactive power compensators and their control strategies are constantly improving. The control strategies proposed by experts at home and abroad are generally divided into three categories: (1) Traditional proportional integral control [2], (2) intelligent control technologies such as neural networks and sliding mode control [3][4][5] and (3) control methods based on traditional modern control theory [6]. All kinds of control methods have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Research on D-STATCOM Double Closed-Loop Control Method Based on Improved First-Order Linear Active Disturbance Rejection Technology

Sun

Zhou

2020

Energies

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The application of a distribution static synchronous compensator (D-STATCOM) is the best technical means to solve the problem of reactive power compensation and harmonics. The D-STATCOM system has the characteristics of variable parameters, strong coupling, nonlinearity and multi-variability. In order to improve the speed of the dynamic tracking response and anti-disturbance capability of the D-STATCOM, the article proposes an improved Linear Active Disturbance Rejection Controller (LADRC) based on the total disturbance deviation control method. Combined with double closed-loop control, the inner loop takes the current as the state variable, and the outer loop uses the DC side output voltage as the state variable to achieve robust stability of the D-STATCOM and controller output. The simulation results show that the improved LADRC is more stable than the traditional LADRC in controlling the DC voltage waveform of the compensator, reactive current tracking and reactive power compensation waveform when it is disturbed, which verifies the superiority and feasibility of the improved LADRC.

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A XGBOOST‐MRFO control scheme for power quality improvement in grid integrated hybrid renewable energy sources using STATCOM

Datar¹,

Kulkarni²

2021

Int Trans Electr Energ Syst

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A hybrid control system is presented for the improvement of power quality in grid-integrated hybrid renewable energy sources (HRES) like hybrid photovoltaic-wind solar energy system using STATCOM. The proposed hybrid control system is the hybrid wrapper of both the extreme gradient boosting (XGBOOST) and Manta Ray Foraging Optimization (MRFO) and hence it is said to be as XGBOOST-MRFO control scheme. The innovation of the proposed hybrid system is the improved predictability through the participation of MRFO, reliability, adaptation for the entire parameter variations, and shorter implementation time. Depending on the minimal error objective function, MRFO improves the XGBOOST learning process. The proposed approach relies on XGBOOST-MRFO which is serving control algorithm to generate STATCOM reference signals. Here, the XGBOOST system is qualified with inputs like instantaneous previous power of the obtainable sources and the current time necessary load demand and the equivalent target reference power of the sources. Depending on the load variation, the XGBOOST-MRFO control scheme makes the gain parameters of the PI controller give the optimal control

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Improvement in power system transient stability by using STATCOM and neural networks

Cited by 29 publications

References 51 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Research on D-STATCOM Double Closed-Loop Control Method Based on Improved First-Order Linear Active Disturbance Rejection Technology

A XGBOOST‐MRFO control scheme for power quality improvement in grid integrated hybrid renewable energy sources using STATCOM

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