This paper presents the design and the development of an EOD robot, with MVP characteristics. The design is based on a solid base structure with an arm manipulator attached to the base. The overall dimensions of the robot are 590x860x340 mm and it weighs 55kg. The robot is capable of towing heavy objects as well as lifting sensitive objects. The robot has a maximum horizontal reach of 1400 mm and a vertical reach of 1200 mm. The robot is tested according to guidelines developed in the U.S.A., as much as the conditions allowed. Briefly, the results can be summarized as follows: the setup time for the robot is 10 minutes, it can reach speeds up to 8 km/h, it has a towing capacity of 40kg and the maximum communication reach is 20 meters. Among successful tests, the weaknesses were also found which act as a guide for future designs and developments. These weaknesses are what MVP concepts are actually developed for.

Concurrently with the electrification of many segments of modern life, such as transport and heating, the problem of short term load forecasting becomes more actual and important. For each power system management, it is crucial to have parameters that display how much electricity is consumed and generated, so the estimation of future consumption can be developed. During the last years, short term load forecast is obtained by various methodologies; each presenting their advantages compared to the others. In this paper we investigate the feasibility of short term load forecasting using dynamic mode decomposition (DMD). The main idea is to model the consumption space as a linear dynamic system using the measured data. A test data-set is collected from 2009 to 2011 in Ireland, consumption of power was recorded every half hour, the experiments performed by manipulating data, and arranging it for easier application of the algorithm. It is shown that using timestamps with DMD provides easy and fast computation. The obtained results are proven to be promising and comparable with others from the literature.

This paper describes the usage of voltage VAr control (VVC) in closed loop mode (CLVVC). The focus is the application in single feeder radial distribution systems, where the CLVVC is executed in combination with short term load forecast (STLF) and distribution system state estimation (DSSE). Although well-known benefits, this paper especially deals with pitfalls and drawbacks of implementing and using closed loop.

Fault location is one of the most common aspects of power system analysis and protection. In order to maintain system's stability and avoid long lasting blackouts, it is important to localize a fault in shortest possible time. This paper analyzes already published impedance-based fault location methods and suggests new algorithm based on orthogonal components. Proposed method is applied on Simulink created three phase model, considering various system specifications including different fault resistance values, having more than one fault on a line at different locations, grounded and ungrounded generators effects, sampling rate changes, line length effects, etc. The results are indicating good accuracy of the algorithm itself.

This paper focuses on closed loop operational mode of Volt-VAr control (VVC) algorithm, as well as the power flow (PF) methods that can successfully be used to implement VVC. Two methods to implement efficient PF are Newton-Raphson approach and current iteration approach. The paper compares both approaches to find the one most suitable for the implementation of closed loop VVC algorithm.

Ungrounded and high resistance grounded lines are utilized in distribution networks, and in special transmission networks. The fault current of a line to ground fault is not high enough to activate protective equipment, hence, the power delivery is not interrupted. Pre-fault and fault currents are available after fault occurs. Thus, used for compensation of pre-fault signals, then filtered using a low pass filter. This allows the introduction of a robust and accurate algorithm for locating single line to ground faults in such ungrounded networks. The algorithm uses symmetrical components extracted from filtered and compensated signals on both ends of a line for an initial estimate of fault location. The fine tuned location is determined by solving nonlinear least square problem that imposes equality constraints related to physical attributes of the fault. The details of the algorithm are already published. In this paper we provide an in depth overview of the algorithm and some numerical results.

The continuity of service in power systems has a vital economical and social impact on all shareholders; generation, transmission and distribution, and end users. Fault classification within transmission and distribution networks plays an important role in power restoration for guaranteed service continuity. With advances in digital signal processing in terms of speed and algorithms, the use of wavelets transform is made easy and feasible for real-time applications in power systems. In this paper we present two methods of fault classification using Discrete Wavelet Transforms (DWT). The coefficients of the wavelet decomposition of fault signals are correlated with the coefficients of signals in normal working conditions to deduce fault information. Haar wavelets and multi-resolution analysis are used for detecting the faulty phase while Daubechies wavelet is used to determine if the fault to ground or not. Both suggested methods succeeded in all types of faults simulated using Simulink.

This paper presents a combined application of fast Fourier transform (FFT) and the Mexican hat wavelet for fault detection within distribution networks. The short time fast Fourier transform (STFFT) can replace FFT as well. One of the two Fourier transforms is used to determine the high frequency harmonics due to abrupt changes in line currents, those harmonics take place when various faults occur. The Mexican hat wavelet is used with a scaling factor which is found based on the frequency of the harmonics and the sampling rate of the measurement device. The results show good performance of the suggested approach, and the next step would be the classification of the fault type.

Due to the parallel features of hardware devices multi-threading became a trend in applications that require intensive computations. However, it is not always the best [1]. In this ongoing work an overview of real-life Power Distribution Management System (PDMS) is given and its performance in single-threaded and multi-threaded environment is tested. PDMS implements parallelism through sub-division of networks based on their natural features. Each sub-network is run in parallel on separate processor cores using a single thread processing, and with such setup it outperforms the multi-threaded BLAS by the factor of 20. With multi-threading implemented, the performance dreadfully goes down, and processing time increases. Reasons for this are the structure of power distribution network matrices (indefinite and very sparse) and synchronization overhead involved in multi-thread operations.

Moving or stationary target Synthetic Aperture Radar (SAR) signatures can be analyzed and used to identify and classify target type against known stored target data base, consisting of full circle raw target data. Public target data base is used as data source, and additionally derived target signature characteristics are generated suitable for target identification. Pose, angle between position and velocity, can be derived in this process and it can be used to reduce search space and hence increase likelihood of Automatic Target Identification (ATR). Our goal in this paper is to define new methodology to analyze target data, using stored signatures as well as real time target signature, and generating variety of spatial statistics and correlations. Besides applications in defense area, there are numerous commercial applications in machine learning, augmented reality, traffic control, and facial recognition.