Application of Supervised Learning for Voltage Stability Assessment

In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand for electricity, and voltage stability is therefore a worth consideration. The growing demand penetration of solar photovoltaic (PV) systems has some potential stability challenges due to their intermittent characteristics. Existing research evaluates the test systems of voltage stability and does not optimize using Intelligent models, which have variable power generation from Solar PV and Wind, as well as uncertainties in load demand and the initial step considering the evaluation of the model have to cover either inputs. A feed–forward back propagation artificial neural network (ANN) for voltage stability assessment to classify the operating scenario of a power system into one of two states, i.e. safe or unsafe. Neural network input data is generated from load flow analysis using Newton-Raphson (NR), and stability indices are computed over different scenarios. Line Stability Index (Lmn), counts the stability of a transmission line in power systems and provides an indication of its potential voltage collapse. The Fast Voltage Stability Index (FVSI), which also estimates line stability through reactive power and impedance characteristics. Voltage Collapse Proximity Index (VCPI) assesses system proximity to voltage collapse and identifies weak transmission lines. Values approaching 1 suggest voltage collapse conditions. The FVSI, VCPI, and Lmn are compared to a baseline NR stability index. Moreover, self-organizing map (SOM) neural network is used for stability results classification. Multi-objective grasshopper optimization algorithm (MOGOA) has been employed as an optimization network on the dual axis solar PV and wind turbine-based hybrid power generation system to analyze technical, economic, as well as environmental relations of energy conversion. The findings reveal that the adapted grid-tied solar-wind composite power system substantially mitigates the power deficiencies, reduces the real-power outages, diminishes the motion voltage small amendments, maximizes voltage prosperity, and improves power system resilience. The main findings show a significant reduction in real power loss, which went from 289,391.30 kW to only 5.43 kW, and a remarkable decrease in voltage deviation, which reduced from 0.56 p.u to 0.0493 p.u. Ensures Improved Service Quality and Enhanced Equipment Protection While gas generation costs increased by a small amount, it is important to note that this is an acceptable trade-off for a more robust and sustainable system, with costs per kWh of $0.142 vs $0.2881 per kWh, respectively. In addition, stability index was substantially decreased from 0.7666 to 0.0768, indicating better grid and resilience to disturbances. Findings emphasize the contribution of embedding MOGOA in conjunction with machine learning models including ANN and Self Organizing Maps (SOM) to fulfill the primal requisites of stability, efficiency and sustainability in modernized power systems, illustrating a coupled advancement in the realm of power systems engineering and optimization.

Power systems are being operated close to their voltage security limit which makes their reliable operation more challenging. Voltage stability has been a major problem faced by utilities now a days. Many blackout have been reported around the world related to voltage instability problem. Many voltage stability analysis methods have been presented up till now. Here artificial neural network based approach for voltage stability analysis of power system is presented. Voltage stability analysis of power system has been done in terms of voltage stability margin. Voltage stability margin is calculated by estimating distance from current operating point to maximum voltage stability point according to loading parameter. Principal Component Analysis is an efficient feature selection method used here to find few input variables required to estimate voltage stability margin with sufficient accuracy and execution speed. Major drawback of previously published work is they need to train new neural network whenever change in system topology occur which is very time consuming process. Therefor in this paper single ANN has been prepared for estimating VSM for several system configuration. Feed forward neural network and radial basis function neural network have been used for voltage stability analysis of power system. The final result indicate that proposed method provide an efficient and compact ANN model for different system configuration as well as base case operating condition with lowest possible input required.

Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability security assessment accounting uncertainties in the line parameters and control variables. Security index has been evaluated using Monte-Carlo simulation with & without consideration of unavailability of transmission lines, which is used to contingency selection and compared with well-established methods. Further, evaluation of probabilistic insecurity index (PISI) using cut-set method for single & double line outages at various loading conditions considering voltage stability limit. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. The applicability of PISI has been made on-line using radial basis function (RBF). Since voltage security assessment is possible on-line using RBFNN. Proposed algorithms have been implemented on IEEE 6-bus 7-line test systems.

This paper presents an application of Artificial Neural Network (ANN) algorithm for improving power system voltage stability. The training data is obtained by solving several normal and abnormal conditions using the Linear Programming technique. The selected objective function gives minimum deviation of the reactive power control variables, which leads to the maximization of minimum Eigen value of load flow Jacobian. The considered reactive power control variables are switchable VAR compensators, OLTC transformers and excitation of generators. The method has been implemented on a modified IEEE 30-bus test system. The results obtain from the test clearly show that the trained neural network is capable of improving the voltage stability in power system with a high level of precision and speed.

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Renewable energy generation has different static and transient characteristics than traditional synchronous power plants. The connection of renewable energy to the grid will have varying degrees of impact on the voltage stability of the local power grid. In a power grid with large renewable energy penetration, in addition to the voltage stability problem, renewable energy integration changes the original power flow distribution, line transmission power and inertia of the entire system, the power system transient stability has undergone tremendous changes. Renewable energy power generation technology and grid-connected scale are developing rapidly. The technical requirements for renewable energy and the research on grid-connected technology need to be continuously deepened and improved. However, some large-scale renewable energy off-grid accidents in recent years have brought serious threats to the security and stability of power systems, and have also raised higher research requirements for the stability of large-scale renewable energy grid-connected. This paper provides an introduction about the control and stability of renewable energy grid integration on both technologies and standardization aspects, and tries to explore potential the trend in active power and frequency control, reactive power and voltage control, security and stability control respectively caused by the integration of RE generation into power systems.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip

To prevent the maritime traffic accidents and make scientific decision, scientific and accurate prediction of the traffic flow is useful, which has often been made by neural network. The weight updating methods have played an important role in improving the performance of neural networks. To ameliorate the oscillating phenomenon in training radial basis function (RBF) neural network, a fractional order gradient descent (GD) with momentum method for updating the weights of RBF neural network (FOGDM-RBF) is proposed. Its convergence is proved. The new algorithm is used to predict vessel traffic flow at Xiamen Port. It performs stable and converges to zero as the iteration increases. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by fractional order GDM is smoother than the GD and GDM method. Error analysis shows that the algorithm can effectively accelerate the convergence speed of the GD method and improve its performance with high accuracy and validity. The influence of fractional order, number of hidden layer neurons, tide peak hours, and ship size is analyzed and compared. 1. Introduction As the world shipping becomes more and more busy, the large ship traffic flow leads to frequent maritime traffic accidents, resulting in huge economic losses. Ship traffic flow is a basic system in marine traffic engineering and an important index to measure the construction of marine traffic infrastructure. Its prediction results can provide basis for formulating scientific Port management planning and ship navigation management. Therefore, to ensure the accuracy and rationality of ship traffic flow forecasting is of great significance for improving port infrastructure construction and formulating scientific port management strategies. Many advanced artificial intelligence optimization algorithms have been used for ship traffic flow forecasting, such as artificial neural network (Zhai 2013; Zhang 2015). Neural network can deal with complex nonlinear problems and has achieved some results. However, the neural network itself has some shortcomings, such as slow learning speed, easy to fall into the local extremum, learning and memory instability, etc.

Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns, a neural model, suffered heavily in getting generalized learning, in result poor performances appeared over test data. To overcome this issue in this work, a decomposition-based approach has been applied to separate the component patterns of trend and cyclic patterns, and a dedicated model has been developed for predicting the individual data patterns. The linear characteristic of the trend data pattern has been modeled through a linear regression model while the nonlinearity behavior of cyclic pattern has been model by an adaptive radial basis function neural network. The final predicted outcome has been considered as the linear combination of individual model outcomes. The Gaussian function has been considered as the kernel function in the radial basis function neural network because of its wider and efficient applicability in function mapping. The performance of the neural model has been improved very much by providing the adaptive value of spreads and centers of basis function along with weights values. In this paper, two different applications of forecasting in the area of electric power demand by the individual house and month-wise annual power generation have been considered. Based on house characteristics parameters, the power demanded by a house have been considered which carried a moderate complexity of function mapping problem while in another case, total power generation needed to be predicted on the monthly basis for a year from just the previous year observation, which carried the mixed behavior of trend and cyclic pattern. For house power demand forecasting the adaptive kernel-based radial basis function has shown very satisfactory performances and much better against static kernel radial basis function and multilayer perceptron neural network. The integrated approach of neural model and linear regression has shown very efficient outcomes for the mixture pattern while individual neural models were failed to do so. The coefficient of determination (R2) has been applied to estimate the quality of predicted outcomes and comparison.

The load shedding plan for voltage and Frequency stability is an important means for operators to maintain a stable and reliable state of the power system when it suffers from severe disturbance. In a power system the load shedding based on critical undervoltage or underfrequency provides protection when the system frequency or voltage drop below a prespecified value. This paper presents a load shedding scheme that stabilizes the system during both undervoltage and underfrequency scenarios. The scheme is developed in MATLAB where artificial neural network is used with reference to under frequency whereas Fast voltage stability index is used for voltage stability analysis. The effectiveness of the scheme is verified in an IEEE 6 bus system which is developed in power world simulator.

This study presents an analysis of voltage stability of the electrical power system with the integration of renewable energy and the improvement of the conventional analysis using the artificial neural network (ANN), which is mainly focused on the effect of distributed generator-solar (DG-Solar) towards the electric power system. Real power and reactive power margins of both voltage stability are expressed as VSM (P) and VSM (Q), respectively. They are obtained from the actual power-voltage (PV) and reactive-power (QV) curve, which is created for each series by a series of load flows by incrementing P and Q load. Then, the system will integrate with 5MW, 50MW and 100MW of DG-Solar to compare the electric power system before and after the integration. The results are observed and compared for both situations. After that, an analysis improvement is made by using ANN-based model to predict the values of VSM (P) and VSM (Q) without having to perform PV QV Curve and calculate VSM (P) and VSM (Q). IEEE 30-bus was chosen as the electrical power system. The load flow analysis and ANN-based model are simulated and developed by using MATLAB software.

The Distribution Static Synchronous Compensator (DSTATCOM) has emerged as a vital solution for enhancing power quality and providing reactive power support in distribution networks. This study investigates the application of DSTATCOM in a multi-machine 9-bus system to achieve effective reactive power compensation and maintain a stable voltage profile under various operating conditions. Optimal placement of the DSTATCOM is determined through load flow analysis, targeting voltage regulation during normal operation as well as in the presence of voltage sag and swell scenarios. Further, this work extends into fault analysis by integrating Discrete Wavelet Transform (DWT) and Radial Basis Function Neural Networks (RBFNN) to detect and classify diverse fault events. Fault current signals are decomposed using the Daubechies-4 (Db4) mother wavelet at level 1, enabling the extraction of critical features. Fault Index (FI) and Fault Threshold (FTH) values are computed and compared to distinguish between normal and abnormal system conditions. The extracted DWT coefficients serve as inputs for training and testing the RBFNN, which is employed to accurately identify and categorize different fault types across various buses. To ensure the robustness and reliability of the proposed methodology, a comparative analysis is performed using Feedforward Neural Networks (FFNN), Probabilistic neural network (PNN), Recurrent current neural network (RNN) and Elman neural network (ENN). Additionally, RBFNN demonstrated faster convergence and better generalization capabilities, making it the most effective tool for fault detection in this study. Comprehensive simulations are performed in MATLAB Simulink under varying network conditions and neural network architectures to validate the approach. The results confirm that the proposed method offers a fast, accurate, and reliable solution for real-time fault detection and classification in power distribution systems.