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Active noise control (ANC) headphone is widely used to attenuate the noise around human' s ear. The microphone mounted on the conventional ANC headphones collected the mixed reference signals when more than one noise sources are often present in the surrounding. In this case, the uncorrelated noise sources involved in the mixed reference usually deteriorate the noise reduction performance of the ANC headphones. To solve this problem, wireless microphones are proposed to install close to each potential noise source in the environment. The microphones pick up the clean reference signals and transmit them to the ANC controller embedded in the headphones with time-advance wirelessly. Every reference signal selected by a coherence-based-selection algorithm is provided individual control filter in each ear. Each control filter updated by using a single clean reference offers better noise reduction performance for ANC headphones. Furthermore, numerical simulations and real-time experiment results in this paper demonstrate the improvement of the proposed method compared with conventional ANC headphones.

Hierarchical state estimation (HSE) is often deployed to evaluate the states of an interconnected power system from telemetered measurements. By HSE, each low-level control center (LCC) takes charge of the estimation of its internal states, whereas a trusted high-level control center (HCC) assumes the coordination of boundary states. However, a trusted HCC may not always exist in practice; a cloud server can take the role of an HCC in case no such facility is available. Since it is prohibited to release sensitive power grid data to untrustworthy cloud environments, considerations need to be given to avoid breaches of LCCs’ privacy when outsourcing the coordination tasks to the cloud server. To this end, this article proposes a privacy-preserving HSE framework, which rearranges the regular HSE procedure to integrate a degree-2 variant of the Thresholded Paillier Cryptosystem (D2TPC). Attributed to D2TPC, computations by the cloud-based HCC can be conducted entirely in the ciphertext space. Even if the HCC and some LCCs conspire together to share the information they have, the privacy of non-conspiring LCCs is still assured. Experiments on various scales of test systems demonstrate a high level of accuracy, efficiency, and scalability of the proposed framework.

Leveraging multiple heterogeneous measurements to predict wind power has long been a challenging task in the electrical community. In this paper, a deep architecture incorporated with multitask learning and multimodal learning for wind power prediction, termed predictive stacked autoencoder (PSAE), is presented. PSAE is a unified framework integrating multiple stacked autoencoders (SAEs), one feature fusion layer, and one prediction terminal layer, which expands the architecture from two spatial dimensions, including the depth and width, compared to conventional prediction models. Initially, the SAEs at the bottom of PSAE extracted features from multiple kinds of measurements respectively. Following, the feature fusion layer encodes the high-order features extracted by different SAEs into a unified feature that is more informative and representative for wind power prediction. Finally, the prediction terminal layer functions as a regression machine which generates the predicted targets based on the fusion features. Trained in an end-to-end (E2E) manner, PSAE is capable of learning heterogeneous features jointly and achieving the prediction task of sequence-to-sequence (S2S). Experiments for multi-step short-term predictions are conducted on real-world data, and the results demonstrate the superiority of PSAE to prior methods.

Multichannel active noise control (MCANC) is widely recognized as an effective and efficient solution for acoustic noise and vibration cancellation, such as in high-dimensional ventilation ducts, open windows, and mechanical structures. The feedforward multichannel filtered-x least mean square (FFMCFxLMS) algorithm is commonly used to dynamically adjust the transfer function of the multichannel controllers for different noise environments. The computational load incurred by the FFMCFxLMS algorithm, however, increases exponentially with increasing channel count, thus requiring high-end field-programmable gate array (FPGA) processors. Nevertheless, such processors still need specific configurations to cope with soaring computing loads as the channel count increases. To achieve a high-efficiency implementation of the FFMCFxLMS algorithm with floating-point arithmetic, a novel architecture based on multiple-parallel-branch with folding (MPBF) technique is proposed. This architecture parallelizes the branches and reuses the multiplier and adder in each folded branch so that the tradeoff between throughput and the usage of the hardware resources is balanced. The proposed architecture is validated in an experimental setup that implements the FFMCFxLMS algorithm for the MCANC system with 24 reference sensors, 24 secondary sources, and 24 error sensors, at a sampling and throughput rates of 25 kHz and 260 Mb/s, respectively. NRF (Natl Research Foundation, S’pore) Accepted version

Leveraging both temporal and spatial correlations to predict wind speed remains one of the most challenging and less studied areas of wind speed prediction. In this paper, the problem of predicting wind speeds for multiple sites is investigated by using the spatio-temporal correlation. We proposed a deep architecture termed predictive spatio-temporal network (PSTN), which is a unified framework integrating a convolutional neural network (CNN) and a long short-term memory (LSTM). Initially, the spatial features are extracted from the spatial wind speed matrices by the CNN at the bottom of the model. Then, the LSTM captures the temporal dependencies among the spatial features extracted from contiguous time points. Finally, the predicted wind speeds are given by the last state of the top layer of the LSTM, which are generated by using the spatial features and temporal dependencies. Though composed of two kinds of architectures, PSTN is trained with one loss function in an end-to-end manner, which can learn temporal and spatial correlations jointly. Experiments for short-term predictions are conducted on real-world data, whose results demonstrate that PSTN outperforms prior methods.

Feedforward active noise control (ANC) is widely utilized to attenuate the broadband noise picked up by the reference microphone. However, in some situations, it is impractical to obtain a clean reference signal when the noise source is far away from the controller. Hence, we adopt a wireless reference microphone to pick up the reference signals around the noise sources. Furthermore, a coherence-based-selection algorithm is proposed to select the reference signals with high coherence. The proposed method improves the quality of the reference signals and the noise reduction performance of the ANC system. Numerical simulations and real-time experiments are conducted to validate the effectiveness of the proposed algorithm.

The growing integration of information and communication technology exposes more exploitable cyber-attack vectors to electric power systems. Measurement manipulation is an impactive type of cyber-attacks that can be launched to cause massive damages by interfering decision-making of control centers. Existing data-driven measurement manipulation detection models often bear criticisms due to the deficient mining of spatial-temporal correlations. In this paper, based on the Gramian Angular Summation Field (GASF) algorithm, various types of phasor time-series measured at different locations are transformed into a series of generalized multichannel images. From these generalized images, Convolutional Neural Networks (CNNs) are trained to discover suspicious patterns. GASF can visualize the temporal features of a single time-series and facilitate collaborative CNNs to figure out spatial-temporal correlations between different measurements. The improvements of the proposed framework in the detection performance are demonstrated through case studies.

The goal of optimal dynamic topology evolution is to establish a distribution network which accommodates a high penetration of renewable energy with least cost. In this paper, the evolution process is formulated as a bi-level optimization model. The upper-level model optimizes the location and size of renewable distributed generation (RDG) as well as flexible resources and aims to minimize the total cost of evolution. Besides, out of simplification, the upper-level model discretizes the continues evolution process into several stages and becomes an integer programming solved by an improved genetic algorithm (GA). The lower-level model is a linear programming after the linearization of nonconvex constraints and minimizes the operation cost of the distribution system. In order to handle the uncertainty in distribution system, an hourly simulation is performed in the lower-level model which is solved by SeDuMi. Finally, the model is applied to a 13-bus distribution network to show its effectiveness.

Urban noise pollution is an omnipresent but often neglected threat to public health that must be addressed urgently. Passive noise control measures, which are less effective at reducing low-frequency noise and are often bulky and may impede airflow. As evidenced in automobiles, active control of cabin noise has resulted in lighter cars due to reduced passive insulation. Despite its long history and recent popularisation by consumer head- phones, the implementation of active noise control in the built environment is still rare. To date, active noise control (ANC) has been demonstrated, at source, in construction machines and, in the transmission path, in noise barriers. Recent demand for naturally-ventilated buildings has also spurred the development of active control solutions at the receiving end, such as on windows. The ten questions aim to demystify the principles of ANC and highlight areas in which environmental noise can be actively mitigated. Since the implementation of active control in the built environment usually involves multiple stakeholders, operational concerns are addressed. To conclude, research gaps are identified that would enable increased adoption of ANC in the built environment. There is also renewed interest in applying intelligent ANC to tackle environmentally complex applications, such as varying noise levels in the earcup of ANC headphones, particularly with the advent of the low-cost, low-power, highly-efficient embedded electronics; advancing speaker technology; and new impetus from digital signal processing and artificial intelligence Algorithms. Ministry of National Development (MND) National Research Foundation (NRF) Published version This research is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office under the Cities of Tomorrow (CoT) Research Programme (CoT Award No. COT-V4-2019-1). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore Ministry of National Development and National Research Foundation, Prime Minister’s Office, Singapore.

With the continuous growing of wind power grid connected capacity, the requirements for the accuracy of wind farm equivalent models in power system analysis are increasing. However, traditional equivalence methods have disadvantages such as irrational number of clusters and poor adaptability of clustering results, which makes the methods unable to meet the needs in certain fields. Therefore, this paper proposes an equivalence method for wind farm based on clustering of output power time series data. This method uses the DTW algorithm to measure the similarity between the time series output power from each wind turbine generator. Then the DBSCAN algorithm is used to achieve a reasonable number of clusters and to identify the outliers. Finally, simulation verification is carried out under the conditions of wind speed disturbance and three-phase short-circuit fault, and the results are compared with those of the single-machine equivalent model and the k-means multi-machine equivalent model. Simulation results show the effectiveness of the proposed equivalence method, and the established model can reflect the operating characteristics of wind farm more accurately.

Online dynamic security assessment is one of the important applications of online situation awareness for power systems, providing essential information for secure operation and preventive control. Different from the existing methods which are fault-dependent or requiring for transient-state data, this paper proposes a novel deep learning model for online power system multi-fault dynamic security assessment driven by hybrid information of anticipated faults and static operating points. The proposed model is universal and suitable for different fault situations. In addition, this model avoids the acquisition of transient information, thus reducing the time-consuming process of online evaluation. For the discrete nominal features in the hybrid information, one-hot encoding is introduced to preprocess the discrete nominal features. Both the performance of the dynamic security assessment model and the principal component analysis are promoted. Furthermore, an improved adaptive synthetic sampling algorithm is proposed and applied to alleviate the problem of power system data imbalance. Finally, the feasibility and effectiveness of the proposed method is verified by testing on the anticipated faults in the New England 39-bus system and the IEEE 54-machine 118-bus system.

Active noise control (ANC) technology is increasingly ubiquitous in wearable audio devices, or hearables. Owing to its low computational complexity, high robustness, and exemplary performance in dealing with dynamic noise, the fixed-coefficient control filter strategy plays a central role in portable ANC implementation. Unlike its traditional adaptive counterpart, the fixed-filter strategy is unable to attain optimal noise reduction for different types of noise. Hence, we propose a selective fixed-filter ANC method based on a simplified two-dimensional convolution neural network (2D CNN), which is implemented on a co-processor (e.g., in a mobile phone), to derive the most suitable control filter for different noise types. To further reduce classification complexity, we designed a lightweight one-dimensional CNN (1D CNN), which can directly classify noise types in time domain. A numerical simulation based on measured paths in headphones demonstrates the proposed algorithm’s efficacy in attenuating real-world non-stationary noise over conventional adaptive algorithms.

Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.

Although the ac fault current provided by photovoltaic (PV) power plant is limited as a weak-infeed power or a electric load, the voltage and current directions are different from conventional synchronous source, since the current is highly controlled by the PV inverter. This paper investigates the measured impedance and the distance directional element of PV side line protections. The impedance trajectory are drawn and compared with conventional power source. Simulation result shows that the directional comparison teleprotections may miss operate under a line internal fault, since the measured impedance drops into the negative direction area, even though the weak-infeed tripping function is activated.

With a large number of renewable energy sources and DC-type loads entering the distribution network, the cost of inverter investment and system operation become high. By introducing a DC part into the traditional distribution network, the cost can be reduced. Also, it will be more convenient for new energy access. Nowadays, the main body of the distribution network is in AC form. Thus, it is impractical for whole grid DC retrofit. To analyze the optimal proportion of the DC part to be employed into the traditional distribution network, firstly, a bilevel optimization model is established to optimize the topology of the AC-DC distribution network. Secondly, in order to solve the bi-level optimization model, an improved GA algorithm, in which the Warshell algorithm is used to improve the convergence performance of the traditional GA algorithm, has been adopted. Finally, based on the bi-level optimization model, a multi-stage planning method of the distribution network is proposed, which reveals the dynamic topology evolution of the distribution network.

One of the significant impacts of the growing penetration of intermittent renewable energy sources is upon the frequency response of power system. Compared with the dispersive electric vehicle energy storage, electric vehicle battery swapping station (BSS), as an emerging form of storage, can provide a more reliable supplementary regulation service for frequency control. This study has proposed a new supplementary automatic generation control (AGC) strategy using controllable energy storage in BSSs, referred to as station-to-grid (S2G). A Monte–Carlo stochastic simulation method is utilised to estimate the equivalent controllable capacity (CC) of BSSs, and then the lumped S2G equivalent model subject to SOC limits and CC constrains is presented. A filter-based AGC coordinated strategy is used to allocate the regulation power between generators and BSSs. The proposed AGC strategy is validated in a two-area interconnected power system with significant load and wind power fluctuations. Comparison analysis demonstrates the availability of the proposed models and control methods.

Application of demand-side resources to automatic generation control (AGC) has a great significance for improving the dynamic control performance of power system frequency regulation. This paper investigates the possibility of providing regulation services by demand-side energy storage in electric vehicle battery swapping stations (BSS). An interaction framework, namely station-to-grid (S2G), is presented to integrate BSS energy storage into power grid for giving benefits to frequency regulation. The BSS can be regarded as a lumped battery energy storage station through S2G framework. A supplementary AGC method using demand-side BSS energy storage is developed considering the vehicle user demand of battery swapping. The effects to the AGC performance are evaluated through simulations by using a two-area interconnected power grid model with step and random load disturbance. The results show that the demand-side BSS can significantly suppress the frequency deviation and tie-line power fluctuations.

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

In recent years, cyber-attacks have become an imminent danger threatening the stable power supply. Data-driven approaches based on machine learning techniques are frequently used to detect cyber-attacks. To get satisfactory detection performances, these approaches have to establish an in-depth understanding of power system operating behaviors, not only under normal conditions but also under contingencies like short-circuit faults. Contingency data appears far more rarely than normal data in the historical measurement database. If normal and contingency data are uniformly sampled to generate training datasets, the included contingency examples may be too few for machine learning models to recognize the operating patterns thoroughly. In this paper, a data preparation method combining Random Matrix Theory (RMT) and Adaptive Synthetic Sampling (ADASYN) algorithm is proposed. RMT is applied to extract rare contingency data from the historical operating database and ADASYN is used to synthetically generate new contingency examples according to the existing ones. Case studies show that this method can facilitate the learning of the intrinsic statistical characteristics of power system operating data, and then enhance the detection of cyber-attacks.

Selective active noise control (SANC) is a method to select a pre-trained control filter for different primary noises, instead of using conventional real-time computation of the control filter coefficients. SANC has the advantage of improving the robustness of control filter while reducing the computational complexity. This paper presents a practical strategy in choosing a suitable control filter based on the frequency-band-match mechanism implemented in a partitioned frequency domain filter structure. Both simulation and real-time experiment are carried out validate the noise reduction performance of the SANC compared to the conventional FxLMS algorithm. NRF (Natl Research Foundation, S’pore) Accepted version

In this paper, a new definition of short-circuit ratio concept for multi-converter HVDC systems is proposed. Analysis results of voltage interaction between converters show that the reactive power-voltage characteristic of a converter has a dominant effect on voltage interaction level compared with its active power-voltage characteristic. Such a relation between converter reactive power and voltage interaction level supports taking the former into account in the definition of short-circuit ratio concept for multi-converter systems. The proposed definition is verified by the method of maximum power curve for various system configurations. Furthermore, a formula to calculate transient overvoltage for multi-converter systems is derived based on the proposed definition, and the efficiency of the derived formula is verified.

Vehicular ad-hoc network (VANET) is an emerging technology which can offer a wide variety of promising applications, such as safety-related and infotainment applications. However, VANET also raises important security and privacy concerns that must be properly addressed for widespread deployment. In this paper, we first proposed a provable secure pseudonym-based cryptosystem with a trusted authority, including a pseudonym-based encryption scheme, a pseudonym-based multi-receiver encryption scheme, a pseudonym-based signature scheme, and a pseudonym-based one-pass key establishment protocol. We then presented a secure and efficient data access scheme for VANET based on cooperative caching technology and the proposed pseudonym-based cryptosystem. The efficiency of data access was greatly improved by allowing the sharing and coordination of cached data among multiple vehicles, and the anonymity of vehicles, data confidentiality, integrity and non-repudiation were guaranteed by employing the proposed pseudonym-based cryptosystem. Simulation results have shown that the proposed data access scheme is suitable to the VANET environment.

The active noise canceling window is an application of the multi-channel active noise control (ANC) system, which aims to provide a quiet living environment while preserving the ventilation of the room. In the implementation of the active noise canceling window, the standard multiple-error LMS algorithm demands too much computational power to be handled by a common digital signal processor (DSP). Hence, the minimax algorithm, which minimize the amplitude instead of the power of the noise field, is revisited. The minimax algorithm is compared with the multiple-error LMS algorithm based on numerical simulations of a case (1, 4, 4) ANC system and real-time experiments with floating-point implementations. The simulation and experiment results show that the minimax algorithm has a distinct advantage in reducing the computational complexity but its trade-off is the relatively slow convergence speed.

We demonstrate a portable lens-free cell imaging system activated by resistive pulses that are induced by biological cells in a microfluidic channel. This imaging on-chip flow cytometer integrates CMOS with microfluidics as a standalone on-chip analytical device. The preliminary version of this system is able to provide dual-parametric study (light contrast image and modulated electrical pulse) for each biological cell. Red blood cells and a tumor cell line (HepG2) are used to demonstrate the performance of this chip for flow analysis of biological samples.

Shortage of energy resources, rising power generation cost, and increasing electric energy demand make the dynamic economic dispatch (DED) increasingly necessary in today’s competitive electricity market. In this paper, an enhanced biogeography-based optimization (BBO) referred to as POLBBO is proposed to solve the DED problem with valve-point effects. BBO is a relatively new powerful population-based meta-heuristic algorithm inspired by biogeography and has been extensively applied to many scientific and engineering problems. However, its direct-copying-based migration and random mutation operators make BBO possess good local exploitation ability but lack enough global exploration ability. To remedy the defect, on one hand, an efficient operator named polyphyletic migration operator is proposed to enhance the search ability of POLBBO. This operator can not only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is presented. The OL strategy can quickly discover useful information from the search experiences and effectively utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. In addition, an effective simultaneous constraints handling technique without penalty factor settings is developed to handle various complicated constraints of the DED problem. Finally, four test cases with diverse complexities are employed to verify the feasibility and effectiveness of the proposed POLBBO method. The experimental results and comparisons with many other recently reported DED solution methods consistently demonstrate that POLBBO is able to obtain better economic dispatch schemes.

Due to the lack of standard information model and description, the configuration of the communication network is still out of the unified and standard engineering process of substation automation, which may result in incorrect configuration and long engineering time. The IEC 61850-based comprehensive information model of the communication network and its description in substation configuration description language (SCL) files are proposed for standard, correct, and convenient configuration of the communication network. The information model is established by means of information extraction with function decomposition, and mapping of the management information base (MIB). The detailed configuration description is achieved by extending the existing SCL communication model in IEC 61850-6. The case study of a typical 220-kV substation demonstrates the validation and availability of the proposed information model and configuration description method, which comply with the current edition of IEC 61850 only requiring minor revisions. Moreover, the proposed information model has the potential to be utilized in online network monitoring and management instead of MIB.

Biogeography-based optimization (BBO) is a powerful population-based algorithm inspired by biogeography and has been extensively applied to many science and engineering problems. However, its direct-copying-based migration and random mutation operators make BBO possess local exploitation ability but lack global exploration ability. To remedy the defect and enhance the performance of BBO, an enhanced BBO variant, called POLBBO, is developed in this paper. In POLBBO, a proposed efficient operator named polyphyletic migration operator can formally utilize as many as four individuals' features to construct a new solution vector. This operator cannot only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is employed. The OL strategy can quickly discover more useful information from the search experiences and efficiently utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. The proposed POLBBO is verified on a set of 24 benchmark functions with diverse complexities, and is compared with the basic BBO, five state-of-the-art BBO variants, five existing OL-based algorithms, and nine other evolutionary algorithms. The experimental results and comparisons demonstrate that the polyphyletic migration operator and the OL strategy can work together well and enhance the performance of BBO significantly in terms of the quality of the final solutions and the convergence rate.

The optimal placement of charging infrastructures owns fundamental importance to the popularization of pure electric vehicles (PEVs). This paper focuses on the optimal configuration of centralized charging stations (CCSs) under the condition of large-scale integration of PEVs into grid. A mathematical model to formulate the optimal CCS placement problem is firstly established. Then the distribution discipline of CCSs in the optimum CCS configuration with minimum total transportation distance (TTD) is shed light on according to the mathematical model, and it in turn helps to identify the candidate CCS locations which turn out to be discrete, finite, fit for numerical calculation and reliable. Finally a further optimization model within the searching space of these candidate CCS locations is proposed to identify the optimum CCS configuration, and solved by a modified binary particle swarm optimization (BPSO) based on Taboo mechanism (TM). The resultant optimization method, named TM-BPSO, can make up the defect of premature convergence of the original BPSO to a certain extent. A large number of numerical examples verify the correctness of the proposed strategy and the applicability of the modified BPSO in this study.

Economic dispatch (ED) is an important task in power system operation. It is able to decrease the operating cost, save energy resources, and reduce environmental load. In this paper, a multi-strategy ensemble biogeography-based optimization (MsEBBO) based method for ED problems is proposed. BBO is a population-based meta-heuristic algorithm inspired by the science of biogeography and mainly consists of three components: migration model, migration operator, and mutation operator. It has good local exploitation ability but lacks satisfactory global exploration ability. To keep a proper balance between exploration and exploitation, MsEBBO has three extensions to BBO’s three components according to the no free lunch theorem. First, a nonlinear migration model based on sinusoidal curve is employed. Second, a backup migration operator through adopting a backup strategy to combine perturb operator and blended operator is presented. This operator can make the entire population fully exchange or share information and thus further strengthen the exploitation ability. Finally, both differential mutation and Levy local search are embedded as mutation operator for MsEBBO using a similar backup strategy. Gaining from this mutation operator, MsEBBO can be accelerated to escape from local optima and perform efficient search within global range. Additionally, an effective repair technique is proposed to handle different constraints of ED problems. The performance of MsEBBO is tested on four ED problems with diverse complexities. Experimental results and comparisons with other recently reported ED solution methods confirm that MsEBBO is capable of yielding a good balance between exploration and exploitation, and obtaining competitive solution quality. Moreover, the sensitivity of MsEBBO to variations in population size is investigated as well.

Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

Active noise control (ANC) is an efficient technique to deal with low frequency noise that is difficult to be abated by noise barrier or sound absorbing material. Many successful ANC systems have adopted the feedforward filtered-x least mean squares (FxLMS) algorithm to reduce machinery noise. The noise canceling headset is another well known example, where the feedback control structure is favorable due to the small size. However, the feedback control structure is incapable of reducing broad band noise. Therefore, this paper investigates the feasibility of implementing the feedforward FxLMS algorithm in the noise canceling headset. Since the reference microphone has to be placed very close to the secondary source, we propose a systolic FxLMS structure that can be very efficiently implemented on the field programmable gate array (FPGA). Theoretical analysis and simulation results are presented to show the convergence behavior of the systolic FxLMS structure.

Transmission reliability margin (TRM) is the amount of transmission capability ensuring that the transmission network is secure from the uncertainty in system operating condition. The accurate evaluation of TRM is of great importance in the calculation of available transfer capability (ATC). This paper presents a fast assessment method of TRM using stochastic response surface method (SRSM). The statistics and probability distributions of TRM can be accurately estimated with less computational cost. The effectiveness of the proposed method is demonstrated by several case studies of the modified IEEE 30-bus standard test systems with wind farm integration.

With the availability of many low-cost programmable development kits in the market, real-time signal processing projects can now be readily introduced into today's signal processing and embedded system course curriculum. In this paper, we group these popular development kits in terms of their cost, hardware architecture, development methodology and software resource. We further illustrate the programming efforts in implementing a real-time digital signal processing algorithm using different types of programmable development kits.

Flexible intelligent electronic devices (IEDs) are highly desirable to support free allocation of function to IED by means of software reconfiguration without any change of hardware. The application of generic hardware platforms and component-based software technology seems to be a good solution. Due to the advent of IEC 61850, generic hardware platforms with a standard communication interface can be used to implement different kinds of functions with high flexibility. The remaining challenge is the unified function model that specifies various software components with appropriate granularity and provides a framework to integrate them efficiently. This paper proposes the function-block (FB)-based function model for flexible IEDs. The standard FBs are established by combining the IEC 61850 model and the IEC 61499 model. The design of a simplified distance protection IED using standard FBs is described and investigated. The testing results of the prototype system in MATLAB/Simulink demonstrate the feasibility and flexibility of FB-based IEDs.

This paper proposes a multiagent-based adaptive wide-area current differential protection system. A power grid is divided into primary and backup protection zones dynamically online with the help of a developed expert system, and the relay agents of these zones cooperate to perform a differential relaying function. Predictive self-healing strategies have been proposed to prevent misoperation of the relay agent, and the communication system has been simulated. The performance of the proposed adaptive wide-area current differential protection system based on computer simulation is presented in this paper.

The rapid growth of Internet applications has made communication anonymity an increasingly important or even indispensable security requirement. Onion routing has been employed as an infrastructure for anonymous communication over a public network, which provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. However, existing onion routing protocols usually exhibit poor performance due to repeated encryption operations. In this paper, we first present an improved anonymous multi-receiver identity-based encryption (AMRIBE) scheme, and an improved identity-based one-way anonymous key agreement (IBOWAKE) protocol. We then propose an efficient onion routing protocol named AIB-OR that provides provable security and strong anonymity. Our main approach is to use our improved AMRIBE scheme and improved IBOWAKE protocol in onion routing circuit construction. Compared with other onion routing protocols, AIB-OR provides high efficiency, scalability, strong anonymity and fault tolerance. Performance measurements from a prototype implementation show that our proposed AIB-OR can achieve high bandwidths and low latencies when deployed over the Internet.

The technology of wireless body area network (WBAN) has attracted intensive attention in recent years. For widespread deployment of WBANs, security and privacy issues must be addressed properly. Recently, Hu et al. proposed a fuzzy attribute-based signcryption scheme with the aim to provide security and privacy mechanisms in WBANs. In this paper, we first show Hu et al.’s scheme cannot achieve the claimed security properties. In particular, an adversary is capable of generating private keys for any set of attributes. Then we introduce a new cryptographic primitive named ciphertext-policy attribute-based ring signcryption (CP-ABRSC) by integrating the notion of ciphertext-policy attribute-based encryption with identity-based ring signature. We give formal syntax and security definitions for CP-ABRSC and present a provable secure CP-ABRSC scheme from bilinear pairings. Finally, we propose a novel access control framework for WBANs by exploiting CP-ABRSC scheme, which can not only provide semantic security, unforgeability and public authenticity, but also can provide participants privacy and fine-grained access control on encrypted health data.

The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping controllers is rarely involved. A simultaneous robust coordinated multiple damping controller design strategy for a power system incorporating power system stabilizer (PSS), static var compensator (SVC) POD and DFIG POD is presented in this paper. This coordinated damping control design strategy is addressed as an eigenvalue-based optimization problem to increase the damping ratios of oscillation modes. Both local and inter-area electromechanical oscillation modes are intended in the optimization design process. Wide-area phasor measurement unit (PMU) signals, selected by the joint modal controllability/ observability index, are utilized as SVC and DFIG POD feedback modulation signals to suppress inter-area oscillation modes. The robustness of the proposed coordinated design strategy is achieved by simultaneously considering multiple power flow situations and operating conditions. The recently proposed Grey Wolf optimizer (GWO) algorithm is adopted to efficiently optimize the parameter values of multiple damping controllers. The feasibility and effectiveness of the proposed coordinated design strategy are demonstrated through frequency-domain eigenvalue analysis and nonlinear time-domain simulation studies in two modified benchmark test systems. Moreover, the dynamic response simulation results also validate the robustness of the recommended coordinated multiple damping controllers under various system operating conditions.

As the cornerstone of the future intelligent transportation system, vehicular ad hoc networks (VANETs) have attracted intensive attention from the academic and industrial research communities in recent years. For widespread deployment of VANETs, security and privacy issues must be addressed properly. In this paper, we introduce the notion of pseudonym-based cryptography, and present a provable secure pseudonym-based cryptosystems with a trusted authority that includes a pseudonym-based multi-receiver encryption scheme, a pseudonym-based signature scheme, and a pseudonym-based key establishment protocol. We then propose a secure and efficient data access scheme for VANETs based on cooperative caching technology and our proposed pseudonym-based cryptosystems. On the one hand, the efficiency of data access are greatly improved by allowing the sharing and coordination of cached data among multiple vehicles. On the other hand, anonymity of the vehicles, data confidentiality, integrity and non-repudiation are guaranteed by employing our proposed pseudonym-based cryptosystems. Simulation results have shown that our proposed pseudonym-based cryptosystems are suitable to the VANETs environment.

As an emerging patient-centric model of health information exchange, cloud-based personal health record (PHR) system holds great promise for empowering patients and ensuring more effective delivery of health care. In this paper, we propose a novel patient-centric cloud-based secure PHR system, which allows patients to securely store their PHR data on the semi-trusted cloud service providers, and selectively share their PHR data with a wide range of users, including health care provider like doctors and nurses, family members or friends. To reduce the key management complexity for owners and users, we divide the users in the cloud-based PHR system into two security domains named public domain and personal domain. Different from previous cloud-based PHR system, PHR owners encrypt their PHR data for the public domain using cipher text-policy attribute-based encryption scheme, while they encrypt their PHR data for the personal domain using anonymous multi-receiver identity-based encryption scheme. Only authorized users whose credentials satisfy the specified cipher text-policy or whose identities belong to dedicated identities can decrypt the encrypted PHR data, where cipher text-policy or dedicated identities are embedded in the encrypted PHR data. Extensive analytical and experimental results are presented which show the patient-centric cloud-based secure PHR system is secure, scalable and efficient.

Mobile ad hoc networks have been extensively studied in recent years due to its potential applications in civilian and military environments. Cooperative caching could be employed to improve data accessibility and reduce data access cost in mobile ad hoc networks. In this paper, we present two secure and anonymous cooperative caching schemes based on onion message and pseudonym-based encryption, respectively. The proposed schemes can not only protect the privacy of nodes and routes, but also can resist both passive and active attacks.

Energy crisis and environmental pollution make the plug-in hybrid electric vehicle (PHEV) become a hot topic. This paper proposes a multiobjective network reconfiguration methodology based on quantum-inspired binary particle swarm algorithm that aims at alleviating the adverse impact of PHEV on distribution system. The methodology involves two steps: load level division and network reconfiguration for each load level. Two different charging patterns of PHEV are considered in this analysis: uncoordinated charging and coordinated charging. The simulation results show that the proposed methodology is an effective method to make the distribution network more flexible to accommodate PHEV.DOI: http://dx.doi.org/10.5755/j01.eee.19.5.1738

Fault diagnosis of power systems is of great significance in restoring the outage area as soon as possible. In this paper, fuzzy reasoning spiking neural P system (FRSN P system) is implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P system is able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationships between candidate faulted section and protective devices are represented by the FRSN P system, the faulty conclusion can be drawn by means of a simple parallel matrix-based operation. A 14-bus power system is used to demonstrate the effectiveness of the proposed diagnosis method. The simulations show that FRSN P system has notable characteristics of being easy to implement, rapid parallel reasoning, and handling uncertainty.

Integration of demand-side regulation resources into automatic generation control (AGC) has greater significance for improving the dynamic performance of grid frequency control. This paper investigates the possibility of providing regulation services by energy storage in electric vehicle battery swapping stations (BSS) in the demand-side. An interaction framework, namely Station-to-Grid (S2G), is presented to integrate the BSS into power grid. An aggregated model of S2G is developed, which accounts for both the users' demand of battery swapping and the available regulation reserve (ARR). A two-area interconnected system simulation has been studied to verify the contributions to AGC by BSS. The results show that the demand-side BSS can significantly suppress the frequency deviation and tie-line power fluctuations..

Mobile ad hoc network has been extensively studied in recent years due to its potential applications in civilian and military environments. Cooperative caching, which allows the sharing and coordination of cached data among multiple nodes, could be employed to improve data accessibility and reduce data access cost in mobile ad hoc networks. In this paper, we give anonymity requirements for cooperative cache-based data access in mobile ad hoc networks and present two efficient anonymous data access schemes based on onion message and pseudonym-based encryption, respectively. The proposed schemes can not only protect confidentiality of sensitive cache data but can also protect privacy of nodes and routes.

This paper presents a new firmware upgrade mechanism for Software Defined Radio (SDR) based systems. The mechanism solves many problems existing in firmware on-site upgrade, and makes the upgrade of SDR based systems to be much quicker and easier. A remote upgrade method is used in this mechanism. The software scheme and hardware structure of this mechanism are illustrated. A synchronization mechanism and alternating upgrade strategy are used in the software scheme. In the hardware structure, eight memory sections are allocated to satisfy the requirement of software scheme, and different operation attributes are set for these memory sections. With the implementation of these strategies and settings, this firmware upgrade mechanism becomes safe, secure and reliable.

Integration of electric vehicles (EVs) into power system ancillary services has great significance for promoting the development of EVs and improving the level of grid operation. The decentralized on-board batteries and user mobility behaviour may cause to increase the cost and complexity of grid control. This paper presents a new conceptual framework for the interaction of EVs and power grid, namely Station-to-Grid (S2G), based on the battery swapping business model. The control structure, communication and incentives for S2G participating in Automatic Generation Control (AGC) are also discussed. A two-area AGC simulation model has been proposed to verify the effect of S2G. The results show that S2G can significantly suppress the frequency deviation and tie line power fluctuations.

Available transfer capability (ATC) has become an important indicator in the power system operation and planning. In order to better guide dispatchers to make decisions, this paper focuses on improving integrity of the calculation model and robustness of the algorithm. Based on the analysis of power incremental directions, constraints checking and system instable risk on ATC, a fast calculation algorithm for dynamic ATC is presented. Through improving the repeated power flow (RPF) method, this calculation framework is applicable in bulk interconnected grid. The online application of developed software in practical power grid verifies the validity and applicability of the proposed approach.

Energy crisis and environmental pollution make the electric vehicle (EV) become a hot topic. The connection of EVs to the power grid brings great challenges to the electric utilities. This paper proposes a multiobjective network reconfiguration methodology based on quantum-inspired binary particle swarm algorithm that aims at alleviating the adverse impact of plug-in hybrid electric vehicle (PHEV) on distribution system. The fuzzy sets are used to handle the multiobjective. The methodology involves two steps: load level division and network reconfiguration on each load level. Two different charging patterns of PHEV are considered in this analysis: uncoordinated charging and coordinated charging. The simulation results of a 33-bus distribution system show that the proposed methodology has a good effect on achieving the energy loss reduction and improving the voltage quality considering the charging load of PHEV.

Direct Digital Synthesizer has become the first choice for the signal generator in modern electronic devices. But the spur prevents it from replacing traditional frequency synthesizer completely. This paper introduces a new frequency source, conjoining Sigma Delta and CORDIC, to produce high-resolution and low spur analog wave-forms. With phase noise as low as −107dBc/Hz, the generated sine waveforms at MF and HF show the better performances than those produced by general analog signal generator. Furthermore, It does not require a ROM, a multiplier or DAC, easily realized in FPGA(S), CPLD or ASIC.