686 research outputs found
Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory
High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples
Fuzzy second order sliding mode control of a unified power flow controller
Purpose. This paper presents an advanced control scheme based on fuzzy logic and second order sliding mode of a unified power flow controller. This controller offers advantages in terms of static and dynamic operation of the power system such as the control law is synthesized using three types of controllers: proportional integral, and sliding mode controller and Fuzzy logic second order sliding mode controller. Their respective performances are compared in terms of reference tracking, sensitivity to perturbations and robustness. We have to study the problem of controlling power in electric system by UPFC. The simulation results show the effectiveness of the proposed method especiallyin chattering-free behavior, response to sudden load variations and robustness. All the simulations for the above work have been carried out using MATLAB / Simulink. Various simulations have given very satisfactory results and we have successfully improved the real and reactive power flows on a transmission lineas well as to regulate voltage at the bus where it is connected, the studies and illustrate the effectiveness and capability of UPFC in improving power.Π Π½Π°ΡΡΠΎΡΡΠ΅ΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΡΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½Π½Π°Ρ ΡΡ
Π΅ΠΌΠ° ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠ΅ ΠΈ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΡΠΊΠΎΠ»ΡΠΆΠ΅Π½ΠΈΡ Π²ΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ° ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΠΏΠΎΡΠΎΠΊΠ° ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠ°Π½Π½ΡΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅Ρ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π°ΠΌΠΈ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΡ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, Π·Π°ΠΊΠΎΠ½ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΡΠ΅ΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΡΠ΅Ρ
ΡΠΈΠΏΠΎΠ² ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠΎΠ²: ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΈΠ½ΡΠ΅Π³ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ, ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΡΠΊΠΎΠ»ΡΠ·ΡΡΠ΅Π³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ° ΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΡΠΊΠΎΠ»ΡΠ·ΡΡΠ΅Π³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ° Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠΈ Π²ΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ°. ΠΡ
ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΡΠ°Π²Π½ΠΈΠ²Π°ΡΡΡΡ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΡ ΡΡΠ°Π»ΠΎΠ½ΠΎΠ², ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΊ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡΠΌ ΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΈΠ·ΡΡΠΈΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΌΠΎΡΠ½ΠΎΡΡΡΡ Π² ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΠΏΠΎΡΠΎΠΊΠ° ΠΌΠΎΡΠ½ΠΎΡΡΠΈ (UPFC). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΡ Π²ΠΈΠ±ΡΠ°ΡΠΈΠΈ, ΡΠ΅Π°ΠΊΡΠΈΠΈ Π½Π° Π²Π½Π΅Π·Π°ΠΏΠ½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ. ΠΡΠ΅ ΡΠ°ΡΡΠ΅ΡΡ Π΄Π»Ρ Π²ΡΡΠ΅ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»ΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Ρ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ MATLAB/Simulink. Π Π°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΠ°ΡΡΠ΅ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π»ΠΈ Π²Π΅ΡΡΠΌΠ° ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΈ ΠΌΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΠ»ΡΡΡΠΈΠ»ΠΈ ΠΏΠΎΡΠΎΠΊΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π½Π° Π»ΠΈΠ½ΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ Π½Π° ΡΠΈΠ½Π΅, ΠΊ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΎΠ½Π° ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½Π°, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΠ·ΡΡΠΈΡΡ ΠΈ ΠΏΡΠΎΠΈΠ»Π»ΡΡΡΡΠΈΡΠΎΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ UPFC Π΄Π»Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ
Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review
This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented
Literature Review of PID Controller based on Various Soft Computing Techniques
This paper profound the various soft computing techniques like fuzzy logic, genetic algorithm, ant colony optimization, particle swarm optimization used in controlling the parameters of PID Controller. Its widespread use and universal acceptability is allocated to its elementary operating algorithm, the relative ease with the controller effects can be adjusted, the broad range of applications where it has truly developed excellent control performances, and the familiarity with which it is deduced among researchers. In spite of its wide spread use, one of its short-comings is that there is no efficient tuning method for PID controller. Given this background, the main objective of this is to develop a tuning methodology that would be universally applicable to a range of well-liked process that occurs in the process control industry
Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators
The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5βMW wind turbines connected to a 25βkV distribution system exporting power to a 120βkV grid through a 30βkm 25βkV feeder is carried out by simulation
A Prosthetic Limb Managed by Sensors-Based Electronic System: Experimental Results on Amputees
Taking the advantages offered by smart high-performance electronic devices, transradial prosthesis for upper-limb amputees was developed and tested. It is equipped with sensing devices and actuators allowing hand movements; myoelectric signals are detected by Myo armband with 8 ElectroMyoGraphic (EMG) electrodes, a 9-axis Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE) module. All data are received through HM-11 BLE transceiver by Arduino board which processes them and drives actuators. Raspberry Pi board controls a touchscreen display, providing user a feedback related to prosthesis functioning and sends EMG and IMU data, gathered via the armband, to cloud platform thus allowing orthopedic during rehabilitation period, to monitor usersβ improvements in real time. A GUI software integrating a machine learning algorithm was implemented for recognizing flexion/extension/rest gestures of user fingers. The algorithm performances were tested on 9 male subjects (8 able-bodied and 1 subject affected by upper-limb amelia), demonstrating high accuracy and fast responses
Low-cost and Efficient Fault Detection and Protection System for Distribution Transformer
Distribution transformers are a vital component of electrical power transmission and distribution system. Frequent Monitoring transformers faults before it occurs can help prevent transformer faults which are expensive to repair and result in a loss of energy and services. The present method of the routine manual check of transformer parameters by the electricity board has proven to be less effective. This research aims to develop a low-cost protection system for the distribution transformer making it safer with improved reliability of service to the users. Therefore, this research work investigated transformer fault types and developed a microcontroller-based system for transformer fault detection and protection system using GSM (the Global System of Mobile Communication) technology for fault reporting. The developed prototype system was tested using voltage, current and temperature, which gave a threshold voltage higher than 220 volts to be overvoltage, a load higher than 200 watts to be overload and temperature greater than 39 degrees Celsius to be over temperature was measured. From the results, there was timely detection of transformer faults of the system, the transformer protection circuits were fully functional, and fault reporting was achieved using the GSM device. Overall, 99% accuracy was achieved. The system can thus be recommended for use by the Electricity Distribution Companies to protect distribution transformers for optimal performance, as the developed system makes the transformers more robust, and intelligent. Hence, a real-time distribution transformer fault monitoring and prevention system is achieved and the cost of transformer maintenance is reduced to an extent
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