Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system

Neural Computing and Applications - In this research article, a novel approach is proposed by considering the sine augmented scaled sine cosine (SAS-SCA) Algorithm for the load frequency control of...     

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.     

Design and analysis of SSSC-based supplementary damping controller

Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this paper. The design problem of the proposed controller is formulated as an optimization problem, and real coded genetic algorithm (RCGA) is employed to search for the optimal controller parameters. Both local and remote signals with associated time delays are considered in the present study and a comparison has been made between the two signals. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite-bus power system and multi-machine power system. Simulation results are presented and compared with a recently published modern heuristic optimization technique under various disturbances to show the effectiveness and robustness of the proposed approach.     

Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults.     

Smart SCADA system for urban air pollution monitoring

There are a number of different types of monitoring stations for checking air quality levels in urban environments. These monitoring stations usually just perform data acquisition of the measured values from the sensors and store them in the database. The processing of the measured results as well as the statistical analysis is mainly done in other places where the data come from various communication systems. Acquisition of the measured data is commonly done on-line while the processing and statistical analysis is performed off-line. As opposed to these measurement systems, this implemented device enables the acquisition and statistical processing of the measured data in real time and the results are instantly available to all users. The system indicates the air pollutants using the ARMA model. The transmission of information in the realized smart SCADA system is done by the Modbus protocol using shared variables which gives the whole system a stronger hierarchical structure.     

Application of Taguchi coupled Fuzzy Multi Attribute Decision Making (FMADM) for optimizing surface quality in turning austenitic and duplex stainless steels

In the present study, Taguchi approach is coupled with fuzzy-multiple attribute decision making methods for achieving better surface quality in constant cutting speed face turning of EN 1.4404 austenitic, EN 1.4462 standard duplex and EN 1.4410 super duplex stainless steels. Two typical multiple attribute decision making techniques were simultaneously adopted to determine multi-surface quality characteristics indices. The differences in rankings among derived indices are solved through converting each crisp values into trapezoidal fuzzy number and unifying them using fuzzy simple additive weight method. The fuzzy numbers are then deffuzified into crisp values employing techniques like; the spread, mode and area between centroid of centroids. Through this procedure, the decision maker is provided with necessary decision tools to optimize the cutting conditions with less sensitivity to the change of weights and no difference in ranking among the deffuzification techniques. Additionally, results of analyses of means and the validation experiments confirm that the optimum cutting conditions derived by this method produce far better surface finish than the best finish obtained during the course of experimentation. Analyses of variance results have shown the predominant effect of feed rate on surface quality. Finally, the collected chip at constant cutting speed and varying feed rates and depth of cuts has shown that friendlier-to-machine chips are obtained when machining austenitic stainless steels than duplex stainless steel grades.     

RF fingerprint measurements for the identification of devices in wireless communication networks based on feature reduction and subspace transformation

This document proposes a radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks. The proposed identification methods are based on the extraction of the preamble RF fingerprint of a device and its comparison with a set of already known device RF fingerprints. The identification method combines techniques for feature reduction such as Principal Component Analysis (PCA) and Partial Least Squares regression (PLS), both based on subspace transformation, along with a similarity-based analysis. In this work, a complete procedure for RF fingerprint data extraction and analysis is provided. In addition, some experimentation with commercial Wi-Fi devices is carried out for the methods validation.     

A novel adaptive deadbeat- based control for load frequency control of low inertia system in interconnected zones north and south of Scotland

An adaptive deadbeat (ADB) controller was developed to investigate its capability in providing a fast frequency response to an electrical power system. This controller was developed to meet the requirements of the National Grid System Operability Framework (SOF), which requires frequency to be accelerated in line with a fast rate of change of frequency (RoCoF) when a high rate of nonsynchronous machines are presented. The controller’s parameters were optimized using particle swarm optimization (PSO) to ensure a robust operation and to maintain the proper operation of the power system. The design of the ADB controller was then integrated with the multiarea model of the north and south zones of Scotland. This model was developed in order to conform to the future energy requirements scenario stated by National Grid whereby regional control can be provided in both the north and south of Scotland. In comparison with the standard PI and Fuzzy-PI controllers used in the four highlighted case studies, it was shown that the ADB controller was able to significantly reduce the RoCoF and deviation of frequency when a sudden loss of generation occurred in a low inertia zone. The ADB also showed high robustness against a wide range of operating conditions.     

Nonlinear adaptive noise-induced algorithm and its application in penetration signal

A nonlinear adaptive noise induced algorithm with nonlinear weights was proposed to extract rigid body deceleration during penetration events; it has 3rd-order nonlinear weight, which ensures deceleration curve is smooth everywhere (not only continuous) and avoids sharp points (crucial for targets detection). In addition, an autocorrelation algorithm was improved by applying moving window method to be compared with the proposed nonlinear adaptive algorithm. By calculating penetration depth and Power Spectrum Density (PSD) of 4 deceleration time series, we show that the nonlinear adaptive algorithm more effectively reduces noise in deceleration for striking velocities between 538 and 800 m/s compared with Adaptive Paйta Criterion, moving window autocorrelation and wavelet algorithms. It is further shown that the proposed adaptive algorithm is of the same order as the other 3 methods in terms of computational complexity.