Genetic algorithm based artificial neural network model for voltage stability monitoring

An electric power system sometimes fails because of disturbances that occur unexpectedly, such as the uncontrolled loss of load that developed from cascading blackout. Which make stability through a little of under voltage load shedding should work. The development of phasor measurement unit(PMU) makes network supervision possible. The information obtained from PMU is synchronized by global positioning system(GPS). There are many real-time algorithms which are monitoring the voltage stability. This paper presents the study on the VILS(Voltage Instability Load Shedding) using PMU data. This algorithm computes Voltage Stability Margin Index(VSMI) continuously to track the voltage stability margin at local bus level. The VSMI is expressed as active and reactive power. The VSMI is used as an criterion for load shedding. In order to examine the algorithm is effective, applied to KEPCO system.

In this paper, an artificial neural network (ANN) based method is developed for quickly estimating the long-term voltage stability margin. The investigation presented in the paper showed that node voltage magnitudes and the phase angles are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. If the voltage magnitudes and phase angles are obtained in real-time from phasor measurement units (PMUs) using the proposed method, the voltage stability margin can be estimated in real time and used for initiating stability control actions. Finally, a suboptimal approach to determine the best locations for PMUs is presented. Numerical examples of the proposed techniques are presented using the New England 39-bus test system and a practical power system which consists of 1844 buses, 746 load buses, and 302 generator buses.

Accurate real-time assessment of power systems voltage stability has been an active area of research in the past few decades. In the past decade, after the development of phasor measurement units (PMU), a lot of discussions has been going on phasor measurement techniques for real-time voltage stability. The fundamental idea behind these methods is to find the Thevenin equivalents of the system, and then determine the voltage stability margin based on the equivalent circuits. Some approaches also include the use of Artificial Neural Networks (ANN), for online monitoring of voltage stability margins. These methods are really fast as compared to the other methods. It has been shown that if we can obtain the phase angles and voltage magnitude in real-time from the phasor measurement units (PMU), then the voltage stability margins can be obtained in real-time and we can initiate voltage stability control methods. We are going to discuss Thevenin's equivalent methods and Artificial Intelligence methods in detail in this paper. We will also introduce the traditional methods which were earlier used for power systems stability assessment such as Time Domain methods, Static Methods, and Sensitivity methods. We are going to finally compare these methods and try to give general guidance on choosing a power stability method.

Deep neural network model with Bayesian hyperparameter optimization for prediction of NO[formula omitted] at transient conditions in a diesel engine

Objective: The main objective of this paper is to improve the intrusion detection accuracy of neural networks by hybridized with Jordan network, applying novel Lyapunov function and changing input values as fuzzy values using fuzzy logic. Methods: In this research, the intrusion detection of NSL KDD dataset is carry out by neural network first . The performance of neural networks mainly depends on the parameters like number of hidden layer, no of node in the hidden layer and the no of epoch. Selecting the proper parameters values and weight initialization are the main difficulty in neural network. To overcome this issue a hybrid network by combining neural network with Jordan network is proposed which is highly sensitive to weight convergence. In hybrid network Lyapunov function is used to achieve the stability of equilibrium between two networks. But in this work a novel Lyapunov function is proposed to achieve global robust stability in hybrid network. A novel Lyapunov function uses a class of general activation functions which are not to be differentiable, bounded or monotonically nondecreasing. A set of criteria are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of hybrid networks with time delays. Then the learning ability of hybrid network is improved by using fuzzy logic. The hybrid network improved by a novel Lyapunov and fuzzy logic is called as Improved Fuzzy Hybrid Jordan network and Artificial Neural Network (IHFJANN). Findings: Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJ ANN) are applied on NSL KDD training dataset to lean the type of available attacks. NSL training dataset contains 2500 instances and 38 attributes where as testing dataset contains 995 instances and 38 attributes. The learned model of three classifiers is used to predict the classes in test dataset. The performance measures are evaluated in terms of accuracy, precision, recall and f-measure values. Improvement: The classification accuracy of Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJANN) are 72%, 80% and 84% respectively. Accuracy is increased by 12% in IHFJANN than the ANN and 4% than the HJANN. The precision value of ANN, HJANN and IHFJANN are 0.46, 0.54 and 0.57 respectively. Precision value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The recall value of ANN, HJANN and IHFJANN are 0.73, 0.81 and 0.84 respectively. Recall value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The F-measure value of ANN, HJANN and IHFJANN are 0.58, 0.65 and 0.68 respectively. F-measure value is increased by 0.10 in IHFJANN than the ANN and 0.3 than the HJANN. The results proved that the proposed IHFJANN provides better performance than ANN and HJANN.

The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite images such as Sentinel-2, it is imperative that accurate retrieval methods are adopted towards monitoring LAI at irrigated rice fields. Here, we evaluate the potential of a hybrid radiative transfer model (i.e., PROSAIL - Gaussian Process Regression (GPR), for estimating the phenological dynamics of irrigated rice LAI using imager derived from the Sentinel-2 multispectral instrument. LAI field measurements were obtained from an experimental rice field in Nasarawa state, Nigeria during the dry season. We used the PROSAIL radiative transfer model to create a look up table (LUT) that was subsequently used to train a GPR model. Afterwards, we evaluated the potential of the hybrid modelling approach by assessing the overall model accuracy and the extent to which LAI was able to accurately predict LAI during key rice phenological phases. We compared the predicted hybrid GPR LAI values with LAI values generated from the SNAP toolbox, based on a hybrid Artificial Neural Network (ANN) modelling approach. Our results show that the overall predictive accuracy of the hybrid GPR model (R2 = 0.82, RMSE = 1.65) was more accurate than that of the hybrid ANN model (R2 = 0.66, RMSE = 3.89) for retrieving LAI values from Sentinel-2 imagery. Both models underestimated LAI values during the reproductive and ripening phases . However, the accuracy during the phenological phases were more significant when using the hybrid GPR model (P < 0.05). During the different phenological phases, the hybrid GPR model predicted LAI more accurately during the reproductive (R2 = 0.7) and ripening (R2 = 0.59) phases compared to the hybrid ANN reproductive and ripening phases. When monitoring LAI phenological profiles of both hybrid models, the hybrid GPR and ANN models underestimated LAI during the reproductive and ripening phases. However, the ANN model underestimations were statistically significantly greater than those for the hybrid GPR model (P < 0.05). Our results highlight the potential of hybrid GPR models for estimating the phenological dynamics of irrigated rice LAI from Sentinel-2 data. They provided more accurate estimation of LAI patterns from varying nitrogen and water applications than hybrid ANN models.

In this paper, a multi-layered perceptron (MLP) neural network (NN) is proposed to nullify the effects of the measured data errors associated with phasor measurement units (PMUs). With this MLP NN, power system voltage stability is estimated by calculating voltage collapse indices (VCIs). Several VCIs are compared with two evaluation criteria to determine those appropriate indices that should be trained using NN as the target output. In other words, the data obtained from the PMU are exploited as the MLP NN inputs, and the exact informative VCIs are considered as its output. The capability of the proposed method is demonstrated by applying it to the New England 39-bus test system. The obtained results confirm the validity of the proposed technique.

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

Optimal placement of voltage measurements for wide area fault location observability considering the uncertainty of network parameters

A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

Continuous assessment of voltage stability (VS) is an important aspect to safeguard electrical power system (EPS) operation. The conservative methods for online assessment in terms of stability check for voltage are extremely time consuming and also absurd for supervising any application online. In agreement with this, a model which is an amalgamation of the Salp swarm algorithm (SSA) and support vector machine (SVM) is aimed at supervising the VS in the paper. The method anticipated for the aforesaid problem utilises the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the ML model and the respective output are considered to be the voltage stability margin index (VSMI). The proficiency of the anticipated model (SSA – SVM) is verified by means of various test cases under the real-time scenario and compared with the same data set to attest its pre-eminence.

A reactive power planning method based on improved particle swarm optimization (IPSO) is presented. This method can improve the quality of initial particles and eliminate the boundary oscillation in traditional particle swarm optimization. The voltage stability margin index (VSI) is used to rank the load buses. Then reactive power compensators are installed on the buses with weak voltage stability. The maximization of voltage stability margin is included in the objective function of reactive power planning to improve system voltage stability. Its application in IEEE 30‐bus system illustrates that the real power loss is depressed with voltage stability margins increased simultaneously. Copyright © 2009 John Wiley &amp; Sons, Ltd.

This paper presents the assessment methodology for voltage stability using Phasor Measurement Unit (PMU) with complete system observability. For full power system observability, the PMU placement is considered with and without conventional power flow as well as injection measurement such that minimum number of PMU's is used. Data obtained by PMU's are used for voltage stability assessment with the help of L-Index. As the PMU gives real time voltage and current phasors and L-index is dependent on voltage and admittance values, thus the L-index so obtained can be used as real time voltage stability indicator. The case study has been carried out on IEEE-14 bus system.

&lt;div data-canvas-width="397.3623059866966"&gt;This paper presents an approach to place the phasor measurement unit (PMU)&lt;/div&gt;&lt;div data-canvas-width="397.3623059866962"&gt;optimally, which minimizes the setup cost of PMU. This methodology attains complete state estimation of the interconnected power networks. An integer linear programming (ILP) method is explored for the optimal PMU placement problem. It is used to determine the optimal location and minimum number of PMUs necessary to make the interconnected power network completely observable. ILP may provide many solutions if acquainting buses to zero injection buses are unhandled. In the case of more than one solution, a bus observability redundancy index and total system observability redundancy index is proposed to find the most promising solutions set for redundancy measurement. The proposed algorithm is applied to benchmark the optimal PMU placement solutions for the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 118-bus, and NRPG 246-bus test systems. The obtained results of the proposed approach are compared with the existing standard algorithm, and it is observed that the proposed approach achieves complete observability of the interconnected power network under base-load conditions.&lt;/div&gt;

Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex parameters. In this study, ANN was hybridized with Genetic algorithm (GA) in order to optimize the performance of ANN for spam detection. GA was used to determine some ANN parameters and suggest optimum weights to efficiently enhance the ANN learning. Experimental results show that the hybrid ANN and GA has superior performance when compared to conventional ANN.