Control of DSTATCOM Using ANN-BP Algorithm for the Grid Connected Wind Energy System

Irfan, Mohammad Mujahid; Malaji, Sushama; Patsa, Chandrashekhar; Rangarajan, Shriram S.; Hussain, Shoeb

doi:10.3390/en15196988

Cited by 41 publications

(5 citation statements)

References 22 publications

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“…Thresholds exist within neurons; only when external stimuli reach a certain threshold will the next neuron be stimulated to work [32]. Improper selection of weights and thresholds can easily lead to slow convergence and becoming trapped in local optima [33]. Therefore, to address the aforementioned issues, the subsequent sections consider the utilization of intelligent optimization algorithms to optimize both weights and thresholds.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Duan,

Cen,

et al. 2024

Energies

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To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.

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Section: Bp Neural Networkmentioning

confidence: 99%

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Duan,

Cen,

et al. 2024

Energies

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“…It is apparent that, for all samples, the preset parameters and the associated output spectrum coefficients have a mapping and form a dataset. Then, a typical BP neural network [27,28], which usually has a two-layer feed-forward framework, sigmoid hidden neurons, and linear output neurons, is established, as shown in Figure 3. For the training process of the BP neural network, the dataset is split into training, validation, and testing sets in a ratio of 7:1.5:1.5, and the Levenberg-Marquardt algorithm is implemented [29,30].…”

Section: Establishment Of the Pod-bp Prediction Modelmentioning

confidence: 99%

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

Yan,

Jiao,

Nie

et al. 2023

Processes

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The heat transfer assessment of a buried hot oil pipe is essential for the economical and safe transportation of the pipeline, where the basis is to determine the temperature field surrounding the pipe quickly. This work proposes a novel method to efficiently predict the temperature field surrounding a hot oil pipe, which combines the proper orthogonal decomposition (POD) method and the backpropagation (BP) neural network, named the POD-BP model. Specifically, the BP neural network is used to establish the mapping relationship between spectrum coefficients and the preset parameters of the sample. Compared with the classical POD reduced-order model, the POD-BP model avoids solving the system of reduced-order governing equations with spectrum coefficients as variables, thus improving the prediction speed. Another advantage is that it is easy to implement and does not require tremendous mathematical derivation of reduced-order governing equations. The POD-BP model is then used to predict the temperature field surrounding the hot oil pipe, and the sample matrix is obtained from the numerical results using the finite volume method (FVM). In validation cases, both steady and unsteady states are investigated, and multiple boundary conditions, thermal properties, and even geometry parameters (different buried depths and pipe diameters) are tested. The mean errors of steady and unsteady cases are 0.845~3.052% and 0.133~1.439%, respectively. Appealingly, almost no time, around 0.008 s, is consumed in predicting unsteady situations using the proposed POD-BP model, while the FVM requires a computational time of 70 s.

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“…In this configuration, the load considered is a non-linear DBR with a Resistive-Inductance (RL) load. Additionally, a common DC-link capacitor Cdc is used to drive the dual parallel-VSIs of DSTATCOM for regulating PCC voltages [11,12]. The proposed combined DC-Link fed parallel-VSI topology requires a single DC-link capacitor and offers merits such as reduced sensing elements, reliable operation and low-cost compensation compared to the isolated DC Link Fed parallel-VSIs topology.…”

Section: Proposed Combined Dc-link Fed Parallel Vsi-based Dstatcom Co...mentioning

confidence: 99%

Combined DC-Link Fed Parallel-VSI-Based DSTATCOM for Power Quality Improvement of a Solar DG Integrated System

Satyanarayana

Radhika²,

Reddy³

et al. 2023

Electronics

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In present day power systems, Power Quality (PQ) issues are causing great concern owing to the increased use of power electronic controlled drives and fluctuating and other non-linear loads. This problem is further aggravated by a steady increase in the integration of renewable energy-based Distribution Generation (DG), employing power electronic converters to distribution systems. Custom power devices with suitable control strategies provide an effective solution to these power quality issues. In this work, a typical three-phase distribution system supplying non-linear load and with DG integration is considered. A shunt connected DSTATCOM at PCC of the system is employed to mitigate power quality concerns. Initially, a parallel-VSI based DSTATCOM configuration, employing individual DC-Link and working basically on the principle of current sharing, has been proposed. The analysis is carried out for variable load conditions for PQ enhancement making use of a more effective control theory viz. Instantaneous Real-Reactive Power (IRP) theory for the generation of suitable switching patterns to the individual VSIs of the parallel DSTATCOM. Further, an improvement over the above configuration viz. combined/common DC-Link-fed parallel DSTATCOM is proposed. This configuration has the advantages of minimized sensing elements, reliable operation and low-cost compensation. A similar analysis is carried out for PQ improvement, making use of the same IRP theory with some modifications (known as MIRP theory). The effectiveness of this configuration is established from the simulation results. In all the above cases, the analyses are carried out using MATLAB/Simulink platform and the simulation results are presented in detail. Thus, the proposed parallel VSIs-based DSTATCOM configurations employing suitable control strategies provide effective solutions for power quality issues under varying load conditions in conventional distribution systems.

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Control of DSTATCOM Using ANN-BP Algorithm for the Grid Connected Wind Energy System

Cited by 41 publications

References 22 publications

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

Combined DC-Link Fed Parallel-VSI-Based DSTATCOM for Power Quality Improvement of a Solar DG Integrated System

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