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This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system's load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer's operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods. [ABSTRACT FROM AUTHOR]

A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset. [ABSTRACT FROM AUTHOR]

Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed. [ABSTRACT FROM AUTHOR]

In this paper, the modeling of storage elements and a unified theory liberated of the I–V characteristics of a specific technology is developed. First, the definition of an ideal storage element with limited energy capacity is presented, then the semiideal storage properties are discussed. The addition of power conservative two-port network denoted POPI (Pout = Pin) is added in order to define a flexible storage element in terms of characteristics. The paper demonstrates the theory by examples of storage elements integrated in power electronics systems. The ideas and methodology presented in this work can be applied to power electronics applications as well as power systems, smart grids, and microgrids, where storage is an important factor. [ABSTRACT FROM AUTHOR]

This study presents a family of non-isolated bidirectional DC-DC converters with extended DC gain. Increasing of the DC gain range is achieved due to splitting the base inductor and also by using magnetically coupled inductors. All the variants of the proposed schemes are based on the use of the dual-half-bridge DC-DC converter. As a result, a number of improved schemes of buck, boost and buck-boost bidirectional converters were obtained with the possibility of increasing the voltageboosting ratio in boost converter mode and simultaneously reducing the voltage gain in the buck converter mode. The converters have a symmetrical structure, and this ensures the operation of each of them in four modes, which gives greater flexibility in matching the voltages of the power supply with storage devices. The formulas for the DC gain, maximum voltage stresses on the switches and a comparative evaluation of these parameters are presented. The modes of operation and equivalent schemes are considered in detail. For each of the schemes, the corresponding tables for the operation of the transistors are given. The analyses of the dynamic modes of the proposed converters is shown. The experimental verification completely confirms the theoretical results. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring is an algorithm or process that disaggregates the total power in a facility to identify consumption of individual appliances. In this paper, a new algorithm is proposed to classify events of appliance states based on modification of the cross-entropy (CE) method. The main contribution is a formulation and solution of the problem with the CE method as a constrained optimization problem. This new technique is called the modified CE method. The proposed algorithm is simple, as it operates in real time, using low rate sampling of the active power. In addition, there is no need to train the system or to use complex hardware. The algorithm is tested using the REDD and AMPds datasets and the results are compared to several state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this study a new scheme of a step-up converter with very high voltage gain is proposed. The scheme is based on a natural combination of the switched-coupled-inductor boost converter and the diode-capacitor multiplier. The study proposes a special scheme of their mutual use for attaining very high voltage gain. An important advantage of the proposed circuit is the avoidance of the current spikes through the transistor and diodes because of the leakage inductance of the coupled inductors. The scheme provides soft commutation of the switch and the diodes. The study analyses the modes of operation and obtains the basic fundamental relations in steady state; an expression for voltage stress across the switch is derived. A new method for dynamic analysis is proposed. The corresponding analytical expressions and curves of the transient behaviour are also obtained. Modelling of the proposed structure and the experimental results are in full agreement regarding the expected efficiency and correctness of the theoretical analysis. A 100W laboratory prototype was built and verified. [ABSTRACT FROM AUTHOR]

The realization of capacitive converters based on a modular approach is presented and analyzed in this paper. The converters are based on topologies with identical capacitors. First, the switching network (SN) of a basic series–parallel topology is presented. The analysis reveals the assembly and growth rules for expanding the topology to any number of capacitors. Subsequently, the SN for the more versatile and complex general transposed series–parallel (GTSP) topology is presented. The topology is expanded by defining a basic cell comprising a capacitor and five peripheral switches. The expansion of this converter improves its performance by ameliorating accuracy and regulation in the dc/dc voltage ratio. The control schemes of these converters are also presented with emphasis on the GTSP topology, whose switches are not assigned in advance. The control scheme for achieving the desired voltage ratio is described. A five-capacitor converter with its control is built to illustrate the theory in open-loop mode, which provides experimental results that are in full agreement with the analytical predictions. [ABSTRACT FROM PUBLISHER]

Energy storage may improve power management in microgrids that include renewable energy sources. The storage devices match energy generation to consumption, facilitating a smooth and robust energy balance within the microgrid. This paper addresses the optimal control of the microgrid's energy storage devices. Stored energy is controlled to balance power generation of renewable sources to optimize overall power consumption at the microgrid point of common coupling. Recent works emphasize constraints imposed by the storage device itself, such as limited capacity and internal losses. However, these works assume flat, highly simplified network models, which overlook the physical connectivity. This work proposes an optimal power flow solution that considers the entire system: the storage device limits, voltages limits, currents limits, and power limits. The power network may be arbitrarily complex, and the proposed solver obtains a globally optimal solution. [ABSTRACT FROM PUBLISHER]

Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heatmapping and improves city energy distribution. This paper describes a novel algorithm of energy fraud detection, utilizing energy and energy consumption specialized knowledge poured into AI front-end. The proposed algorithm improves fraud detection's accuracy and reduces the false positive rate, as well as reducing the preliminary required training dataset. The paper also introduces a holistic algorithm, specifying the major phenomena that disguises as energy fraud or affects it. Consequently, a mathematical foundation for energy fraud detection for the proposed algorithm is presented. The results show that a unique pattern is obtained during fraud, which is independent of a reference non-fraud pattern of the same customer. The theory is implemented on real data taken from smart metering systems and validated in real life scenarios. [ABSTRACT FROM AUTHOR]

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder. [ABSTRACT FROM AUTHOR]

Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents' physical components-based power theory decomposition. Using currents' physical components ensures a constant number of features, which maintains the signal's characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. In this work a new method for multi-dimensional NILM signals is proposed—the Multi-objective NILM (MO-NILM). While classical NILM algorithms are based on a single objective function, MO-NILM classifies NILM events by solving a multi-objective optimization problem. The main idea is to model each NILM feature as an objective function, and to mutually minimize these objectives based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The presented algorithms can operate in real time using low sampling rates (0.25 Hz and lower) without training the system. In addition, the proposed algorithm is simple, and requires information on the average power signatures of each appliance. The method shows good performance in terms of standard measures when tested on the popular REDD and AMPds datasets. [ABSTRACT FROM AUTHOR]