Showing papers in "IEEE Transactions on Industrial Informatics in 2015"

A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

Abstract: The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Abstract: In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called “health factors,” or quantitative indicators, of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management, and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance tradeoffs in terms of frequency of unexpected breaks and unexploited lifetime, and then employing this information in an operating cost-based maintenance decision system to minimize expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies

Abstract: In this paper, a detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.

Model-Based Predictive Direct Control Strategies for Electrical Drives: An Experimental Evaluation of PTC and PCC Methods

Abstract: Model-based predictive direct control methods are advanced control strategies in the field of power electronics. To control an induction machine (IM), the predictive torque control (PTC) method evaluates the electromagnetic torque and stator flux in the cost function. The switching vector selected for the use in the insulated gate bipolar transistors (IGBTs) minimizes the error between references and the predicted values. The system constraints can be easily included. The predictive current control (PCC) strategy assesses the stator current in the cost function. The weighting factor is not necessary. Both the PTC and PCC methods are very useful direct control methods that do not require the use of a modulator. In this paper, the PTC and PCC methods are carried out experimentally for an IM on the same test bench. The behaviors and the robustness in steady state and the performances in transient state are evaluated.

Stochastic Scheduling of Renewable and CHP-Based Microgrids

Abstract: Microgrids (MGs) are considered as a key solution for integrating renewable and distributed energy resources, combined heat and power (CHP) systems, as well as distributed energy-storage systems This paper presents a stochastic programming framework for conducting optimal 24-h scheduling of CHP-based MGs consisting of wind turbine, fuel cell, boiler, a typical power-only unit, and energy storage devices The objective of scheduling is to find the optimal set points of energy resources for profit maximization considering demand response programs and uncertainties The impact of the wind speed, market, and MG load uncertainties on the MG scheduling problem is characterized through a stochastic programming formulation This paper studies three cases to confirm the performance of the proposed model The effect of CHP-based MG scheduling in the islanded and grid-connected modes, as well as the effectiveness of applying the proposed DR program is investigated in the case studies

Opportunistic Routing Algorithm for Relay Node Selection in Wireless Sensor Networks

Abstract: Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.

Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering

Abstract: The need for accurate, fast, and reliable indoor localization using wireless sensor networks (WSNs) has recently grown in diverse areas of industry Accurate localization in cluttered and noisy environments is commonly provided by means of a mathematical algorithm referred to as a state estimator or filter The particle filter (PF), which is the most commonly used filter in localization, suffers from the sample impoverishment problem under typical conditions of real-time localization based on WSNs This paper proposes a novel hybrid particle/finite impulse response (FIR) filtering algorithm for improving reliability of PF-based localization schemes under harsh conditions causing sample impoverishment The hybrid particle/FIR filter detects the PF failures and recovers the failed PF by resetting the PF using the output of an auxiliary FIR filter Combining the regularized particle filter (RPF) and the extended unbiased FIR (EFIR) filter, the hybrid RP/EFIR filter is constructed in this paper Through simulations, the hybrid RP/EFIR filter demonstrates its improved reliability and ability to recover the RPF from failures

A Fast-Converging MPPT Technique for Photovoltaic System Under Fast-Varying Solar Irradiation and Load Resistance

Abstract: Under fast varying solar irradiation and load resistance, a fast-converging maximum power point tracking system is required to ensure the photovoltaic system response rapidly with minimum power losses. Traditionally, maximum power point locus was used to provide such a fast response. However, the algorithm requires extra control loop or intermittent disconnection of the PV module. Hence, this paper proposes a simpler fast-converging maximum power point tracking technique, which excludes the extra control loop and intermittent disconnection. In the proposed algorithm, the relationship between the load line and the I-V curve is used with trigonometry rule to obtain the fast response. Results of the simulation and experiment using single-ended primary-inductor converter showed that the response of the proposed algorithm is four times faster than the conventional incremental conductance algorithm during the load and solar irradiation variation. Consequently, the proposed algorithm has higher efficiency.

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

Abstract: This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.

Predictive Control of Power Converters: Designs With Guaranteed Performance

Abstract: In this work, a cost function design based on Lyapunov stability concepts for finite control set model predictive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal.

Quaternion-Based Robust Attitude Control for Uncertain Robotic Quadrotors

Abstract: A robust nonlinear attitude control method is proposed for uncertain robotic quadrotors. The proposed controller is developed based on a nonlinear model with the quaternion representation and subject to parameter uncertainties, nonlinearities, and external disturbances. A new state feedback controller is proposed to restrain the effects of nonlinearities and uncertainties on the closed-loop control system. These uncertainties are considered as input equivalent disturbances and their effects are guaranteed to be attenuated. Experimental results are given to show good steady-state and dynamic tracking performance of the closed-loop system by the proposed robust control method compared with other nonlinear control methods.

Heuristic-based shiftable loads optimal management in smart micro-grids

Abstract: In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.

Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS

Abstract: Partial least squares (PLS) is an efficient tool widely used in multivariate statistical process monitoring. Since standard PLS performs oblique projection to input space $\mathbf{X}$ , it has limitations in distinguishing quality-related and quality-unrelated faults. Several postprocessing modifications of PLS, such as total projection to latent structures (T-PLS), have been proposed to solve this issue. Further studies have found that these modifications fail to reduce false alarm rates (FARs) of quality-unrelated faults when fault amplitude increases. To cope with this problem, this paper proposes an enhanced quality-related fault detection approach based on orthogonal signal correction (OSC) and modified-PLS (M-PLS). The proposed approach removes variation orthogonal to output space $\mathbf{Y}$ from input space $\mathbf{X}$ before PLS modeling, and further decomposes $\mathbf{X}$ into two orthogonal subspaces in which quality-related and quality-unrelated statistical indicators are designed separately. Compared with T-PLS, the proposed approach has a more robust performance and a lower computational load. Two case studies, including a numerical example and the Tennessee Eastman (TE) process, show the effeteness of the proposed approach.

Spatial-Temporal Solar Power Forecasting for Smart Grids

Abstract: The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting This paper presents a new spatial-temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels This framework has been tested in the smart grid pilot of Evora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations A benchmark comparison was made with the autoregressive forecasting model (AR-univariate model) leading to an improvement on average between 8% and 10%

Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach

Abstract: The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Abstract: Industrial wireless sensor networks (IWSNs) have to contend with environments that are usually harsh and time-varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilized by many other wireless technologies. With wireless applications rapidly growing, it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and coexisting with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio (CR) methods offer a potential solution to improve resistance of IWSNs to interference. Integrating CR principles into the lower layers of IWSNs can enable devices to detect and avoid interference, and potentially opens the possibility of utilizing free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarize CR methods relevant to industrial applications, covering CR architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access (DSA), game theory, and CR network (CRN) security.

Advanced Features and Industrial Applications of FPGAs—A Review

Abstract: Field programmable gate arrays (FPGAs) have established themselves as one of the preferred digital implementation platforms in a plethora of current industrial applications, and extensions and improvements are still continuously being included in the devices. This paper reviews recent advancements in FPGA technology, emphasizing the novel features that may significantly contribute to the development of more efficient digital systems for industrial applications. Special attention is paid to the design paradigm shift caused by the availability of increasingly powerful embedded (and soft) processors, which transformed FPGAs from hardware accelerators to very powerful system-on-chip (SoC) platforms. New analog resources, floating-point operators, and hard memory controllers are also described, because of the great advantages they provide to designers. Software tools are being strongly influenced by the design paradigm shift, which requires from them a much better support for software developers. Focusing mainly on this issue, recent advancements in software resources [intellectual property (IP) cores and design tools] are also reviewed. The impact of new FPGA features in industrial applications is analyzed in detail in three main areas, namely digital real-time simulation, advanced control techniques, and electronic instrumentation, with focus on mechatronics, robotics, and power systems design. The way digital systems are being currently designed in these areas is comprehensively reviewed, and a critical analysis of how they could significantly benefit from new FPGA features is presented.

Expected Cost Minimization of Smart Grids With Plug-In Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration

Abstract: Stochastic charging behavior of plug-in hybrid electric vehicles (PHEVs) under different charging strategies brings new challenges for distribution networks such as feeder overloading and loss increase. In this way, the augmented penetration of these vehicles mandates employing new operative tools to inspect their impacts on electrical grids. Therefore, this paper proposes a novel optimal stochastic reconfiguration methodology to moderate the charging effect of PHEVs by changing the topology of grid using some remote controlled switches. Uncertainties associated with network demand, energy price, and PHEV charging behavior in different charging frameworks are handled with Monte Carlo simulation and the proposed stochastic problem is solved with krill herd optimization algorithm. Numerical studies on Tai-power distribution system verify the efficacy of proposed reconfiguration to improve the system performance considering PHEV charging loads.

Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data

Abstract: The very short-term load forecasting (VSTLF) problem is of particular interest for use in smart grid and automated demand response applications. An effective solution for VSTLF can facilitate real-time electricity deployment and improve its quality. In this paper, a novel approach to model the very short-term load of individual households based on context information and daily schedule pattern analysis is proposed. Several daily behavior pattern types were obtained by analyzing the time series of daily electricity consumption, and context features from various sources were collected and used to establish a rule set for use in anticipating the likely behavior pattern type of a specific day. Meanwhile, an electricity consumption volume prediction model was developed for each behavior pattern type to predict the load at a specific time point in a day. This study was concerned with solving the VSTLF for individual households in Taiwan. The proposed approach obtained an average mean absolute percentage error (MAPE) of 3.23% and 2.44% for forecasting individual household load and aggregation load 30-min ahead, respectively, which is more favorable than other methods.

Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines

Abstract: Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania–New Jersey–Maryland (PJM) interconnection day-ahead and real-time markets.

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Abstract: Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids

Abstract: Although considerable advances have been made in single microgrid (MG) systems, the problem of cooperation among MGs and the macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage power trading, storage, and distribution in a smart power grid with a macrogrid and cooperative MGs. We first formulate the problem as a convex optimization problem and then decompose it into a two-tier formulation. The first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the second-tier problem minimizes the power generation and transmission cost, and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, as well as a distributed optimal algorithm for solving the second-tier problem. The proposed algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors

Abstract: Recognition of human gestures plays an important role in a number of human-interactive applications, such as mobile phones, health monitoring systems, and human-assistive robots. Electromyography (EMG) is one of the most common and intuitive methods used for detecting gestures based on muscle activities. The EMG, however, is in general, too sensitive to environmental disturbances, such as electrical noise, electromagnetic signals, humidity, and so on. In this paper, a new method for recognizing the muscular activities is proposed based on air-pressure sensors and air-bladders. The muscular activity is detected by measuring the change of the air pressure in an air-bladder contacting the interested muscle(s). Since the change of the air pressure can be more robustly measured compared with the change of electric signals appeared on the skin, the proposed sensing method is useful for mobile devices due to its great signal-to-noise ratio (SNR) and fast response time. The principle and applications of the proposed sensing method are introduced in this paper. The performance of the proposed method is evaluated in terms of linearity, repeatability, wear-comfort, etc., and is also verified by comparing it with an EMG signal and a motion sensor.

Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

Abstract: Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm’s effectiveness at identifying unique paths for each type of event and the accompanying classifier’s ability to accurately discern each type of event.

Energy Flow Optimization in Multicarrier Systems

Abstract: In this paper, a generalized heuristic approach is proposed to solve the optimal power flow problem in multicarrier energy systems. This technique omits the use of any extra variable, such as dispatch factors or dummy variables required for conventional techniques. The unified proposed approach can be utilized with all evolutionary algorithms. Modeling hub devices with constant efficiency may produce a considerable error in finding the actual optimal operating point of the whole network. However, using variable efficiency model adds complexity to the conventional methods while increasing the computation–demand of these techniques, but this target can be simply implemented by the proposed scheme. A multicarrier energy system consists of an electrical, a natural gas, and a district heating network is analyzed by the proposed algorithm using the modified teaching–learning-based optimization method. Results validate the utilized approach and show that it can successfully reach the global optimal solution of the problem.

Optimal Pricing and Energy Scheduling for Hybrid Energy Trading Market in Future Smart Grid

Abstract: Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC’s pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC’s optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

Trajectory Optimization With Particle Swarm Optimization for Manipulator Motion Planning

Abstract: Optimization-based methods have been recently proposed to solve motion planning problems with complex constraints. Previous methods have used optimization methods that may converge to a local minimum. In this study, particle swarm optimization (PSO) is proposed for trajectory optimization. PSO is a population-based stochastic global optimization method inspired by group behaviors in wildlife, and has the advantages of simplicity and fast convergence. Trajectory modifications are encoded in particles that are optimized with PSO. The normalized step cost (NSC) concept is used for the initialization of the particles in PSO. A method for reusing previously optimized parameters is also developed. The optimized parameters are stored together with corresponding NSC vectors, and when a constraint violation occurs, the parameters associated with an NSC vector that is similar to the query NSC vector are selected. The selected vector is used for initializing the particles. The reuse of the previously optimized parameters improves the convergence of the PSO in motion planning. The effectiveness of these methods is shown with simulations and an experiment using a three-dimensional problem with constraints. The proposed algorithm successfully optimized a trajectory while satisfying the constraints and is less likely to converge to a local minimum.

Experimental Link Quality Characterization of Wireless Sensor Networks for Underground Monitoring

Abstract: Wireless underground sensor networks (WUSNs) are a category of wireless sensor networks (WSNs) with buried nodes, which communicate wirelessly through soil with sensor nodes located aboveground. As the communication medium (i.e., soil) between traditional over-the-air WSNs and WUSNs differs, communication characteristics have to be fully characterized for WUSNs, specifically to enable development of efficient communication protocols. Characterization of link quality is a fundamental building block for various communication protocols. The aim of this paper is to experimentally investigate the link quality characteristics of the three communication channels available in WUSNs for underground pipeline monitoring to gain further insight into protocol development for WUSNs. To this end, received signal strength (RSS), link quality indicator (LQI), and packet reception ratio (PRR) are characterized for the three communication channels in WUSNs. The RSS and PRR results show that the underground-to-underground channel is highly symmetric and temporally stable, but its range is severely limited, and that the aboveground-to-underground/underground-to-underground channels are asymmetric and exhibit similar temporal properties to over-the-air communication channels. Interestingly, the results show that RSS is a better indicator of PRR than LQI for all three channels under consideration.

Distributed Dynamic Programming-Based Approach for Economic Dispatch in Smart Grids

Abstract: In this paper, the discrete economic dispatch problem is formulated as a knapsack problem. An effective distributed strategy based on distributed dynamic programming algorithm is proposed to optimally allocate the total power demand among different generation units considering the generation limits and ramping rate limits. The proposed distributed strategy is implemented based on a multiagent system framework which only requires local computation and communication among neighboring agents. Thus, it enables the sharing of computational and communication burden among distributed agents. In addition, the proposed strategy can be implemented with asynchronous communication, which may lead to simpler implementation and faster convergence speed. Simulation results with a four-generator system and the IEEE 162-bus system are presented to demonstrate the effectiveness of the proposed distributed strategy.

Harmonic Estimation Using Symmetrical Interpolation FFT Based on Triangular Self-Convolution Window

Abstract: Harmonic estimation is an important topic in power system signal processing. Windowed interpolation fast Fourier transformation (WIFFT) is an efficient algorithm for power system harmonic estimation, which can eliminate the errors caused by spectral leakage and picket fence effect. However, the fitting polynomial in the interpolation procedure contains both even and odd terms, and this increases the computational burden. This paper proposes a simple symmetrical interpolation FFT algorithm, where the even terms are removed from the fitting polynomial based on the triangular self-convolution windows (TSCW). The polynomials for frequency and amplitude computations are provided. Considerable leakage errors and harmonic interferences can be suppressed by the TSCW. Accurate estimations of harmonic parameters can be obtained via the fitting polynomial and the TSCW, both with adjustable order to fulfill different accuracy and speed requirements of practical power harmonic measurement. Simulation results and measurements have validated the proposed method.