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The minimum output variance filtered reference least mean square (MOV-FxLMS algorithm is a effective algorithm that utilizes the penalty mechanism to help the active noise control (ANC system achieve noise cancellation with constrained output variance or power. As it can constrain output power, the MOV-FxLMS algorithm can freely determine the ANC system's control effort, avoid output saturation, and improve system stability. However, its performance is determined by a penalty factor, which is normally chosen by trial and error. Hence, this work proposes an optimal penalty factor and its feasible estimation that does not require any assumptions of Gaussian reference signal or input independence. This factor assists MOV-FxLMS in achieving the optimal solution under the target output-variance constraint. Numerical simulations on measure paths demonstrate its effectiveness for various types of noise.

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.

Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthetically-pleasing topology diagrams for power transmission systems. Input with a rough layout, the framework first conducts visibility region analysis to reduce line crossings and then solves a mixed-integer linear programming problem to optimize the arrangement of nodes. Given that the complexity of both modules is pretty high, simplification heuristics are also proposed to enhance the efficiency of the framework. Case studies on several power transmission systems containing up to 2,046 nodes demonstrate the capability of the proposed framework in generating topology diagrams conforming to aesthetic criteria in the power system community. Compared with the widespread force-directed algorithm, the proposed framework can preserve the relative positions of nodes in the original layout to a great extent, which significantly contributes to the identification of electrical elements on the diagrams. Meanwhile, the time consumption is acceptable for practical applications., Comment: 14 pages, 7 figures, accepted by IEEE Transactions on Power Systems

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.

Shutting the window is usually the last resort in mitigating environmental noise, at the expense of natural ventilation. We describe an active sound control system fitted onto the opening of the domestic window that attenuates the incident sound, achieving a global reduction in the room interior while maintaining natural ventilation. The incident sound is actively attenuated by an array of control modules (a small loudspeaker) distributed optimally across the aperture. A single reference microphone provides advance information for the controller to compute the anti-noise signal input to the loudspeakers in real-time. A numerical analysis revealed that the maximum active attenuation potential outperforms the perfect acoustic insulation provided by a fully shut single-glazed window in ideal conditions. To determine the real-world performance of such an active control system, an experimental system is realized in the aperture of a full-sized window installed on a mockup room. Up to 10-dB reduction in energy-averaged sound pressure level was achieved by the active control system in the presence of a recorded real-world broadband noise. However, attenuation in the low-frequency range and its maximum power output is limited by the size of the loudspeakers. 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.

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

This paper presents an evaluation method of strength measure for the static voltage stability analysis of the hybrid multi-infeed dc (HMIdc) system, which comprises both the line-commutated converter-based high voltage dc (LCC-HVdc) and voltage source converter-based HVdc (VSC-HVdc) links. For this purpose, the coupled single-port model proposed originally to assess the static voltage stability of ac power systems is first extended in the HMIdc system. In particular, how the LCC-HVdc and VSC-HVdc links should be modeled in the extended coupled single-port model is further investigated. Then, a generalized effective short-circuit ratio (GESCR) index is proposed as a strength measure for the static voltage stability analysis of the HMIdc system using the extended coupled single-port model. The GESCR can resolve the ambiguity encountered by earlier indices arising from empirical reasoning; thus, it is able to indicate the static voltage stability of the HMIdc system more accurately. Finally, the simulated results based on a classical simplified HMIdc system and a modified IEEE 118-bus system, both including two LCC-HVdc links and a VSC-HVdc link, validate the proposed GESCR.

The leaky filtered-input least mean square (LFxLMS) algorithm is widely used in active noise control applications to minimize the degradation of attenuation performance due to output saturation distortion. However, the leak factor, which is critical in determining the steady-state error and robustness of the algorithm, is usually selected through trial and error. This letter proposes a leak factor selection approach, which ensures the LFxLMS algorithm converges to its optimal solution under the average-output-power constraint and can be readily derived in practice. Both broadband and narrowband cases are considered in the derivation without the independence assumption, and the simulations are conducted based on real primary and secondary paths to verify its effectiveness. Accepted version

This study presents a novel faulted line identification method that is applicable in power systems with only sparse synchronised measurements, and at the same time, with significantly reduced computational burden so that it can be a competitive choice for wide-area backup protection schemes. By revealing the behaviour pattern of bus voltages from the perspective of high-dimensional unitary space, the method is enabled to avoid unnecessary computation and thus to remarkably improve its efficiency. Analysis in high-dimensional unitary space also gives a direct and feasible guideline for phasor measurement unit (PMU) placement, from which a PMU optimal placement strategy is developed. Performed on the IEEE benchmark systems of 39 and 118 buses, the proposed method gives encouraging results.

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.

Fault section diagnosis (FSD) of power systems plays an important role in power system operation. In order to quickly and accurately diagnose the fault section or sections after the occurrence of an event, a novel variant of brain storm optimization (BSO) in objective space algorithm, referred to as BCBSO (binary coded BSO), is proposed in this paper. The FSD problem is transformed into a 0–1 integer programming problem. The difference between the reported alarms and the expected states of protective relays and circuit breakers is used as the objective function. In BCBSO, each population individual is directly encoded as a binary vector and thereby the transcoding process can be avoided when solving the 0–1 integer programming problem. In addition, logical operations instead of floating operations are employed for binary strings, making the evolutionary process more convenient. In order to verify the performance of BCBSO, three test systems, i.e., the typical 4-substation power system, IEEE 118-bus system, and a practical power grid in Jilin province of China with different fault scenarios including single fault and multiple faults with failed and/or malfunctioned protective devices are employed. Six popular metaheuristic methods including ABC, BBO, DE, GA, PSO, and BSO are utilized to validate the effectiveness of BCBSO. The experimental results comprehensively demonstrate the superiority of BCBSO in terms of successful rate, diagnosis error, robustness, computation efficiency, convergence speed, and statistics. In addition, the effect of population size is investigated as well.

Dynamic economic dispatch (DED), mathematically, is a typical highly complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering valve-point effects. In this paper, a hybrid method named BBOSB by combining biogeography-based optimization (BBO) with brain storm optimization (BSO) is proposed. BBO has good local exploitation ability due to its information sharing mechanism. But it is likely to suffer from premature convergence when dealing with complex multimodal problems. Quite the opposite, BSO possesses excellent global exploration ability owing to its grouping evolution strategy which, however, also can drag its global searching process. In such contexts, the hybrid BBOSB method is able to fully take advantages of both BBO and BSO to conquer premature convergence and to accelerate the global searching process simultaneously. The experimental and comparison results on four non-convex benchmark DED test cases with valve-point effects and a practical provincial power system of China comprehensively demonstrate that BBOSB is highly competitive and can be used as a promising alternative for DED problems. In addition, the effect of population size on the optimization performance is investigated as well.

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.

Active noise control (ANC) is gaining credence as an effective approach in mitigating low-frequency urban noise. Current ANC algorithms that attenuate noise across the full audio frequency band, often exert control effort excessively at higher frequencies. Moreover, such unrestrained control unavoidably attenuates some critical sounds, such as alarms and warning signals. In practice, excessive control effort in ANC usually results in output-saturation distortion, which affects the adaptive system stability and degrades the residual audio quality. To provide greater flexibility of control in frequency bands of interest without incurring output saturation, this paper proposes two variants of the filtered reference comb-partitioned frequency-domain adaptive filter (FxCFDAF) algorithm, namely the leaky FxCFDAF and drop-out FxCFDAF algorithms, which exert output-effort constraint at the control filter in the frequency domain. In addition, the comb-partitioning approach in the proposed FxCFDAF algorithms is delayless and computationally-efficient, which are essential for practical implementation, unlike the conventional frequency-domain adaptive algorithm. Experimental simulations carried out on measured primary and secondary paths validate the proposed algorithms’ advantage over the conventional FxLMS algorithm in mitigating large-amplitude noise without output saturation.

Conventional real-time active noise control (ANC) usually employs the adaptive filtered-x least mean square (FxLMS) algorithm to approach optimum coefficients for the control filter. However, lengthy training is usually required, and the perceived noise reduction is not immediately realized. Motivated by the practical implementation, we propose a selective fixed-filter active noise control (SFANC) algorithm, which selects a pre-trained control filter to attenuate the detected primary noise rapidly. On top of improved robustness, the complexity analysis reveals that SFANC appears to be more efficient. The SFANC algorithm chooses the most suitable control filter based on the frequency-band-match approach implemented in a partitioned frequency-domain filter. Through simulations, SFANC is shown to exhibit a satisfactory response time and steady-state noise reduction performance, even for time varying noise and real nonstationary disturbance. NRF (Natl Research Foundation, S’pore) Accepted version

This paper presents a new strategy in order to model the charging power demand due to large-scale plug-in electric vehicles (PEVs) as realistic a fashion as possible and analyze their impact on the residential power distribution system. The strategy takes the charging willingness of PEV users into consideration, and accounts for the difference in charging frequencies among users. A detailed classification, derived from the historical data on users’ driving patterns, on PEV users is conducted in order to ensure that users in the same user set have the same charging properties. Seven probability load models for PEV charging are established for these user sets, and each model accounts the inherent randomness in the usages and recharges of PEVs. After the consideration of charging willingness, the charging demand differs among weekdays. The aggregated charging demand from a user set on each weekday is calculated based on the Law of Large Numbers, and the total charging demand from all PEVs on each weekday can be obtained by accumulating the aggregated charging demand of the user sets with charging willingness. The strategy can ensure a high utilization of the battery capacity, and the aggregated charging demand resulted is more rational and credible. The proposed charging load modeling strategy is finally applied on the electric load profile on a winter day in Manitoba.

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.

By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems, cyber attackers can mislead the control center into taking wrong actions. From the viewpoint of machine learning, normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space. Hence, a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data. However, malicious synchrophasors occur infrequently in practice. It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training. For most existing detectors, if they are directly trained with highly imbalanced datasets, their performances may severely deteriorate. In this paper, a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing, Bagging-based ensemble learning, and the widely recognized eXtreme Gradient Boosting (XGBoost) classifier. Experiments show that although fewer malicious instances are provided, the proposed detector is still capable of detecting malicious synchrophasors.

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.

This paper proposes a probabilistic load flow (PLF) methodology using uniform design sampling (UDS). The correlation between input random variables has been taken into consideration. The random numbers of random variables uniformly distributed in (0, 1) are generated by UDS, and subsequently converted into random numbers of input random variables with desired marginal distributions by marginal transformation. Then these random numbers of input random variables are permutated by a method based on rank correlation to satisfy the desired correlation between input random variables. The statistical properties and probability distributions of node voltage and line flow are calculated by Monte Carlo simulation method and statistical method. Considering the uncertainty of correlated wind power and loads, the performance of the proposed PLF methodology is investigated using modified IEEE 14-bus and IEEE 57-bus test systems.

A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.

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.

This paper proposes an effective method for fault diagnosis of large-scale power systems based on radial basis function (RBF) neural network (NN) and fuzzy integral. It aims at effectively diagnosing the tie lines which connect different adjacent sub-networks in the context of divisional fault diagnosis. First, an overlapping network division method is proposed to divide a large-scale power system into a desired number of eligible sub-networks. Then, for each sub-network, a local RBF NN diagnostic module which is constructed by an exhaustive search-assisted forward recursive algorithm is allocated. Finally, a Choquet fuzzy integral fusion module is constructed for any pair of connected sub-networks. When a fault occurs, local RBF NN diagnostic modules will be selectively triggered according to local alarm information. If it involves a tie line, the corresponding Choquet fuzzy integral fusion module will be triggered to fuse the diagnostic outputs derived from the adjacent sub-networks which are connected by the tie line. Case studies with a 14-bus power system are presented to evaluate the feasibility and efficiency of the proposed method under various complex fault scenarios. The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.

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.

A probabilistic load flow method based on polynomial normal transformation (PNT) and Latin hypercube sampling (LHS) is proposed. The correlation between input random variables has been taken into consideration. The proposed method uses the statistical moments and correlation matrix of input random variables instead of their marginal distribution functions and joint distribution functions, which are very difficult to be obtained, to establish their probability distribution models by PNT and LHS. The statistical moments and probability distribution functions of node voltage and line flow are calculated by Monte Carlo simulation method. Performance of the proposed method is investigated using IEEE 14-bus and IEEE 118-bus test systems. The impacts of correlation factor on the statistical moments of power injections and system operation are analysed. Finally, conclusions are duly drawn.

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 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.

In this paper, the authors describe how there is a world of promise in building and maintaining a data warehouse as the center of the architecture for power system information systems.

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

Protective relay is designed to make a trip decision based on calculation of collected measurements, and thus, the quality of data plays an important role in ensuring the reliability of protective relay. However, abnormity of any part of the data acquisition system (DAS), especially cyber risk, may make the protection system receive fake measurements and misoperate. Besides those efforts spent on the reliability of DAS and cyber security, protective relay itself should be able to tolerate fake measurements and actively minimise the impacts associated with bad data. Based on evidence theory, this study proposes an approach taking advantage of abundant information in digital substations to identify fake measurements and avoid misoperation of protective relay. When protective relay is prone to misoperate after the analysis of fake measurements from several current transformers (CTs) or voltage transformers (VTs), measurements from other CTs/VTs are rapidly collected to indicate whether there is a real fault in the power system. In this way, misoperation because of fake measurements can be prevented and CTs/VTs providing bad data can also be identified. Simulation tests demonstrate the effectiveness of the proposed approach.

The impedance matrix (Z-matrix) of power system contains much useful and unrevealed information. It has been widely used for short-circuit analysis, contingency analysis and economic dispatch. Based on the electrical coupling paths described by physical parameters of equivalent network derived from the reduced impedance matrix, some novel physical topological analysis methods to identify cut edges, cut vertices and radial paths in the relay coordination and fault analysis software are proposed. The formation of the impedance matrix is the fundamental work of the fault analysis and relay coordination. The physical methods proposed thoroughly utilise the topological information concealed in the existent Z-matrix. Compared with the traditional graph-theoretical approaches, the physical one has clearer and more significant physical implication that may help to understand and give insight into the study of the topology of the electrical network topology in power system comprehensively. Examples are presented to prove the consistency of physical topology in power system with the corresponding geometrical topology. The efficiency of the physical methods to improve network topology analysis has also been validated.