Active damping of a DC network with a constant power load: an adaptive passivity-based control approach

This paper proposes a nonlinear, adaptive controller to increase the stability margin of a direct-current (DC) small-scale electrical network containing a constant power load, whose value is unknown. Due to their negative incremental impedance, constant power loads are known to reduce the effective damping of a network, leading to voltage oscillations and even to network collapse. To tackle this problem, we consider the incorporation of a controlled DC-DC power converter between the feeder and the constant power load. The design of the control law for the converter is based on the use of standard Passivity-Based Control and Immersion and Invariance theories. The good performance of the controller is evaluated with numerical simulations.Postprint (author's final draft

Collective Current Rectification

We consider a network of coupled underdamped ac-driven dynamical units exposed to a heat bath. The topology of connections defines the absence/presence of certain spatial symmetries, which in turn cause nonzero/zero value of a mean dc-output. We discuss dynamical mechanisms of the rectification and identify dc-current reversals with synchronization/desynchronization transitions in the network dynamics.Comment: 3 fig

Development of a dc-ac power conditioner for wind generator by using neural network

This project present of development single phase DC-AC converter for wind generator application. The mathematical model of the wind generator and Artificial Neural Network control for DC-AC converter is derived. The controller is designed to stabilize the output voltage of DC-AC converter. To verify the effectiveness of the proposal controller, both simulation and experimental are developed. The simulation and experimental result show that the amplitude of output voltage of the DC-AC converter can be controlled

A Cycle-Based Formulation and Valid Inequalities for DC Power Transmission Problems with Switching

It is well-known that optimizing network topology by switching on and off transmission lines improves the efficiency of power delivery in electrical networks. In fact, the USA Energy Policy Act of 2005 (Section 1223) states that the U.S. should "encourage, as appropriate, the deployment of advanced transmission technologies" including "optimized transmission line configurations". As such, many authors have studied the problem of determining an optimal set of transmission lines to switch off to minimize the cost of meeting a given power demand under the direct current (DC) model of power flow. This problem is known in the literature as the Direct-Current Optimal Transmission Switching Problem (DC-OTS). Most research on DC-OTS has focused on heuristic algorithms for generating quality solutions or on the application of DC-OTS to crucial operational and strategic problems such as contingency correction, real-time dispatch, and transmission expansion. The mathematical theory of the DC-OTS problem is less well-developed. In this work, we formally establish that DC-OTS is NP-Hard, even if the power network is a series-parallel graph with at most one load/demand pair. Inspired by Kirchoff's Voltage Law, we give a cycle-based formulation for DC-OTS, and we use the new formulation to build a cycle-induced relaxation. We characterize the convex hull of the cycle-induced relaxation, and the characterization provides strong valid inequalities that can be used in a cutting-plane approach to solve the DC-OTS. We give details of a practical implementation, and we show promising computational results on standard benchmark instances

DC motor speed control with the presence of input disturbance using neural network based model reference and predictive controllers

In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller

DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

Speed control of separately excited dc motor using artificial intelligent approach

This paper presents the ability of Artificial Intelligent Neural Network ANNs for the separately excited dc motor drives. The mathematical model of the motor and neural network algorithm is derived. The controller consists two parts which is designed to estimate of motor speed and the other is which to generate a control signal for a converter. The separately excited dc motor has some advantages compare to the others type of motors and there are some special qualities that have in ANNs and because of that, ANNs can be trained to display the nonlinear relationship that the conventional tools could not implemented such as proportional-integral-differential (PID) controller. A neural network controller with learning technique based on back propagation algorithm is developed. These two neural are training by Levenberg�Marquardt. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-Simulink program. The simulation results are presented to demonstrate the effectiveness and the proposed of this neural network controller produce significant improvement control performance and advantages of the control system DC motor with ANNs in comparison to the conventional controller without using ANNs

Protection of large partitioned MTDC networks using DC-DC converters and circuit breakers

This paper proposes a DC fault protection strategy for large multi-terminal HVDC (MTDC) network where MMC based DC-DC converter is configured at strategic locations to allow the large MTDC network to be operated interconnected but partitioned into islanded DC network zones following faults. Each DC network zone is protected using either AC circuit breakers coordinated with DC switches or slow mechanical type DC circuit breakers to minimize the capital cost. In case of a DC fault event, DC-DC converters which have inherent DC fault isolation capability provide ‘firewall’ between the faulty and healthy zones such that the faulty DC network zone can be quickly isolated from the remaining of the MTDC network to allow the healthy DC network zones to remain operational. The validity of the proposed protection arrangement is confirmed using MATLAB/SIMULINK simulations

Power converter

A dc-to-dc converter employs four transistor switches in a bridge to chop dc power from a source, and a voltage multiplying diode rectifying ladder network to rectify and filter the chopped dc power for delivery to a load. The bridge switches are cross coupled in order for diagonally opposite pairs to turn on and off together using RC networks for the cross coupling to achieve the mode of operation of a free running multivibrator, and the diode rectifying ladder is configured to operate in a push-pull mode driven from opposite sides of the multivibrator outputs of the ridge switches. The four transistor switches provide a square-wave output voltage which as a peak-to-peak amplitude that is twice the input dc voltage, and is thus useful as a dc-to-ac inverter