Search

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

The parameter extraction problem of solar photovoltaic (PV) models is a highly nonlinear multimodal optimization problem. In this paper, quadratic interpolation learning differential evolution (QILDE) is proposed to solve it. Differential evolution (DE) is a preeminent metaheuristic algorithm with good exploration. However, its exploitation is poor, resulting in low searching precision when applied to the problem. To overcome this deficiency, in QILDE, quadratic interpolation (QI) is embedded in the crossover operation of DE to construct a QI learning-backup crossover operation to enhance the performance of DE. The mutation scheme of DE is primarily responsible for exploring the new search space while QI is mainly in charge of exploiting the local solution space around the best individual, which, therefore, can achieve a good trade-off between exploitation and exploration. QILDE is applied to six different PV cases. The experimental results demonstrate that QI coupled with the mutation scheme DE/best/2 can obtain superior results in solving the parameter extraction problem of PV models. Besides, compared with other advanced algorithms, QILDE shows highly competitive performance in terms of solution quality, extraction accuracy, robust stability, convergence property, computational time, and statistical significance. In addition, the current–voltage characteristics provided by QILDE agree well with the measured data for different PV models under different operating conditions.

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.

Phasor measurement unit (PMU) data manipulation attacks (PDMAs) may blind the control centers to the real-time operating conditions of power systems. Detecting these attacks accurately is essential to ensure the normal operation of power system monitoring and control. Using long-term accumulated historical PMU measurements to train a machine learning model to detect PDMAs has shown promising results. In this paper, deep-autoencoder-based anomaly measurers are deployed throughout the power system to build a distributed PDMA detection framework. The architecture of a deep autoencoder and its training process are introduced. How to convert the historical PMU measurements into data samples for learning is also elaborated upon. Once trained, an anomaly measurer can assess the PDMA existence possibility of the new PMU measurements. By integrating the results of different anomaly measurers, the proposed distributed PDMA detection framework can detect PDMAs in the whole power system. The effectiveness and detection performance of the framework are discussed through experiments.

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.

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.

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.

The gradual adaptation and possibility of divergence hinder the active noise control system from being applied to a wider range of applications. Selective active noise control has been proposed to rapidly reduce noise by selecting a pre-trained control filter for different primary noise detected without an error microphone. For stationary noise, considerable noise reduction performance with a short selection period is obtained. For non-stationary noise, more restrictive requirements are imposed on instant convergence, as it leads to faster tracking and better noise reduction performance. To speed up a selective filtered active noise control system, empirical wavelet transform is introduced here to accurately and instantaneously extract the frequency information of primary noise. The boundary of the first intrinsic mode function of random noises is extracted as the instant signal feature. Primary noise is attenuated immediately by picking the optimal pre-trained control filter labeled by the nearest boundary. The storage requirement for a pre-trained control filter library is reduced. Instant control is obtained, and the instability caused by output saturation is overcome. With more concentrated energy distribution, better noise reduction performance is achieved by the proposed algorithm compared to conventional and selective active noise control algorithms. Simulation results validate these advantages of the proposed algorithm. NRF (Natl Research Foundation, S’pore) Published version

The gradual adaptation and possibility of divergence have been the two main obstacles in the efficient implementation of conventional adaptive active noise control (ANC) to a wider range of applications. Selective ANC (SANC) has been proposed to rapidly reduce noise by selecting a pre-trained control filter for different primary noise detected, and improve the robustness of the system. For stationary noise, considerable noise reduction performance and system stability are obtained by SANC. However, for non-stationary noise, in order to track the variability of the signal, frequency-band-match and selection have to be conducted constantly, which results in high computational burden. To confront this problem, empirical wavelet transform (EWT) is introduced to simplify the matching and selection of SANC in this paper. This EWT based SANC (SANC_EWT) algorithm extracts the first mode of random noises, and attenuates the noise immediately by picking the optimal pre-trained control filter labeled by the first boundary. Therefore, computational complexity is reduced drastically. Simulation results show that convergence could be reached rapidly. Better noise reduction performance is achieved by SANC_EWT compared to conventional FxLMS and SANC algorithms.

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. In this paper, an improved whale optimization algorithm (WOA), referred to as IWOA, is proposed to accurately extract the parameters of different PV models. The original WOA has good local exploitation ability, but it is likely to stagnate and suffer from premature convergence when dealing with complex multimodal problems. To conquer this concerning shortcoming, IWOA develops two prey searching strategies to effectively balance the local exploitation and global exploration, and thereby enhance the performance of WOA. Three benchmark test PV models including single diode, double diode and PV module models, and two practical PV power station models with more modules in the Guizhou Power Grid of China are employed to verify the performance of IWOA. The experimental and comparison results comprehensively demonstrate that IWOA is significantly better than the original WOA and three advanced variants of WOA, and is also highly competitive with the reported results of some recently-developed parameter extraction methods.

The performance of a solid oxide fuel cell (SOFC) is tightly related to relevant parameters associated with the internal multi-physicochemical processes. Accurate identification of these parameters is considerably important for modelling the voltage versus current (V-I) characteristic of SOFCs. In this paper, an improved teaching-learning based algorithm (TLBO) referred to as RTLBO is proposed to identify the exact values for these parameters. The parameter identification of SOFCs is transformed into a minimization optimization problem. The mean square error (MSE) between the measured output voltage and the calculated output voltage is used as the objective function. TLBO has been shown to be competitive with other population-based algorithms. However, its convergence rate is relatively slow especially for complex optimization problems. Inspired by the ranking mechanism in the actual scenarios of teaching-learning process, a ranking based learner selection method is proposed and integrated into both the teacher and learner phases of RTLBO. In RTLBO, poor learners are more likely to be eliminated from the current class in the ranking based teacher phase and good learners are more likely to be chosen to interact with others in the ranking based learner phase, which hence can improve the overall performance of the class quickly. The experimental results on a 5-kW SOFC stack comprehensively demonstrate that RTLBO is able to achieve a better trade-off between the exploration and exploitation compared with twelve advanced TLBO variants and eight popular advanced non-TLBO based methods. In addition, the sensitivity of RTLBO to variations of population size is empirically investigated.

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.

Power system economic dispatch (ED), mathematically, is a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In this paper, a novel variant of competitive swarm optimizer (CSO) referred to as OLCSO is proposed to solve both convex and non-convex ED problems. In the canonical CSO, the loser particle in each pair updates its position by learning from the winner particle. Such a learning strategy is easy to implement, but is not efficient enough because even a bad particle can contain useful information while a good particle may have misleading features. To solve this issue, an orthogonal learning (OL) strategy based on orthogonal experimental design is developed for CSO to quickly discover more useful information that contained in the winner and the loser particles. The OL strategy can easily construct a more promising solution to provide a more systematic search strategy for OLCSO. A set of 24 numerical benchmark functions and three ED cases with diverse complexities and characteristics are used to comprehensively verify the performance of OLCSO. The experimental results and comparison results consistently indicate that OLCSO can be used as a competitive alternative for ED problems.

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.

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.

The push for greater urban sustainability has increased the urgency of the search for noise mitigation solutions that allow for natural ventilation into buildings. Although a viable active noise control (ANC) solution with up to 10 dB of global attenuation between 100 Hz and 1000 Hz was previously developed for an open window, it had limited low-frequency performance below 300 Hz, owing to the small loudspeakers used. To improve the low-frequency attenuation, four passive radiator-based speakers were affixed around the opening of a top-hung ventilation window. The active control performance between 100 Hz and 700 Hz on a single top-hung window in a full-sized mock-up apartment room was examined. Active attenuation came close to the performance of the passive insulation provided by fully closing the window for expressway traffic and motorbike passing noise types. For a jet aircraft flyby, the performance of active attenuation with the window fully opened was similar to that of passive insulation with fully closed windows. In the case of low-frequency compressor noise, active attenuation&rsquo, s performance was significantly better than the passive insulation. Overall, between 8 dB and 12 dB of active attenuation was achieved directly in front of the window opening, and up to 10.5 dB of attenuation was achieved across the entire room.

Cyber-attackers could stealthily interfere with the normal operation of power systems by maliciously tampering the measurements transmitted from field devices to control centers. Although some data-driven methods capable of identifying abnormal measurements have been proposed, there are some remaining shortcomings such as low accuracy and slow convergence rate. This paper devises a novel framework by combining convolutional neural networks (CNN) and long-short-term memory (LSTM) networks for detecting tampering attacks based on recognizing spatial-temporal correlations between measurements. Besides, optimization such as the attention mechanism, Dropout layers and SVM are applied to improve the performance of the proposed framework. This paper also introduces how to implement the proposed framework in practical cyber-physical systems and expounds the synergistic relationships between the data-driven detector and the traditional bad data detection module. Case studies prove that the proposed framework has better learning performance than existing ones.

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.

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.

This paper presents an advanced variant of teaching learning based algorithm (TLBO) called either-or teaching learning based algorithm (EOTLBO) to extract accurate and reliable parameters of solar photovoltaic (PV) models. EOTLBO synergizes three enhanced strategies to accelerate the convergence rate and boost the search efficiency of TLBO. (i) A median learner based teacher phase excluding the mean position used in the original TLBO is designed to avoid infeasible and inefficient learners and to form a rational and advisable moving mechanism around the teacher. (ii) A higher-achieving learner based learner phase using three sorted learners is devised to directionally guide the target learner to jump out of local optima and to move towards a more promising region. (iii) A chaotic map based either-or teaching-learning strategy is developed to give each dimension of each learner a chance to go through either the median learner based teacher phase or the higher-achieving learner based learner phase. EOTLBO is applied to three PV cells/modules including seven cases. Compared with the original TLBO, four non-TLBO variants, and five TLBO variants, experimental results verify the superior performance of EOTLBO in terms of both the quality of final solutions and the convergence speed on all cases. In addition, the current-voltage characteristics yielded by EOTLBO agree well with the measured data independently of different PV models at different operating conditions.

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.

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.

Extracting accurate and reliable values for involved unknown parameters of solar photovoltaic (PV) cells/modules is considerably significant to the characteristic analysis, fault diagnosis, maximum power point tracking, and efficiency evaluation of PV systems. Solving this problem using metaheuristic algorithms has gained increasing attention recently owing to their versatile and promising applications in highly nonlinear multimodal optimization problems. In this paper, an efficient and effective variant of competitive swarm optimizer (CSO) named WLCSODGM is presented to solve the parameter extraction problem of PV models. CSO is an advanced variant of particle swarm optimization and performs well especially on unimodal optimization problems. However, it is easily trapped in local optima when solving complex multimodal optimization problems such as the one considered here due to its poor exploration. In WLCSODGM, two improved components are introduced to remedy the inadequacy of CSO. On the one hand, a winner-leading search strategy is proposed to favor the exploration and help losers locate more promising regions. On the other hand, a dynamic Gaussian mutation operator with stretchable mutation amplitude and adaptive mutation probability is integrated to further enhance the exploration to help individuals jump out of poor local optima. WLCSODGM is applied to four different PV models and verified using a total number of twelve state-of-the-art algorithms. In addition, the influences of improved components and relevant algorithmic parameters are also experimentally evaluated. Results demonstrate that WLCSODGM is significantly better or highly competitive compared with the other algorithms.

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.

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.

The operating time of directional overcurrent relays (DOCRs) can be reduced, while protection coordination is achieved, by setting the time dial setting (TDS) and pickup current of DOCRs. However, in the microgrids, the dual operating modes (grid-connected and islanded) and the introduction of distributed generations (DGs) bring some problems to coordination of DOCRs, due to changes in short-circuit levels. To limit the fault current levels and the effects of DGs on protection coordination, fault current limiters (FCLs) have been proposed. This paper employs grey wolf optimizer (GWO) to optimize the setting parameters of DOCRs and the values of FCLs not only in microgrids’ two operating modes, but also under two different kinds of protection coordination schemes. The comparison between optimized results obtained by five algorithms demonstrates the outstanding performance of GWO in low dimensional optimization problems and its potential for improvement.

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

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.

Active noise control (ANC) is an effective method for reducing low-frequency acoustic noise. An anti-noise wave is transmitted by the secondary source to destructively interfere with the noise wave. A quiet zone is thus formed around the error microphone, which provides the error signal in the adaptation process of an ANC controller. However, the real-world performance of an ANC system is often subjected to the distortion incurred in its electronic components. This distortion has been conventionally treated as a trivial part of the secondary path model. When the distortion is severe but the secondary path model is still forced to be linear, the nonlinearity of the true secondary path is no longer negligible and causes degradation in noise reduction performance or even divergence of the ANC controller. This paper revisits the causes of the amplitude distortion in the audio amplifier and how it influences the convergence of the filtered-x least mean square (FxLMS) algorithm in a feedforward ANC system for tonal noise cancellation.

PMU data manipulation attacks (DMA) which maliciously modify the synchrophasor measurements in power systems, is an emerging type of cyber-attack aiming to mislead the control center into taking erroneous control actions. Widespread application of PMU produces a vast amount of time-synchronized historical operating data, making it possible to turn to machine learning approaches to detect the attacks. However, anomalous events occur rarely in practice. To construct a balanced training dataset, where normal and anomalous instances need to be evenly extracted and labeled from historical operating records, is sure to cost plenty of human efforts and may introduce uncontrollable experience errors. In this paper, we design a novel detection model with Synthetic Minority Over-sampling TEchnique (SMOTE)-based data augmentation and Edited Nearest Neighbors (ENN)-based data cleaning technique to reconstruct the imbalanced training dataset into a balanced one to train the subsequent XGBoost classifier. Experiments show that although rare anomalous historical event records are needed, our model is still able to effectively detect out DMAs in power systems.

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.

Multichannel active noise control (MCANC) systems are commonly used in acoustic noise or vibration control, such as large-dimension ventilation ducts, open windows and mechanical structures. However, its computational load far exceeds the capabilities of digital signal processors (DSPs) and microcontrollers. Even the field programmable gate array (FPGA) cannot straightforwardly cope with the exponential increase in the computation load of MCANC systems. A novel architecture, called the multiple parallel branch with folding, is proposed for the J × J × M (J reference microphones, J secondary sources and Merror microphones) MCANC implementation with the floating-point arithmetic. This architecture addresses the tradeoff between throughput and hardware resource consumption by using parallel execution and folding. The proposed architecture is validated in an experimental setup that carries out a 4 × 4 × 4 multichannel filtered-x least mean square (FxLMS) algorithm achieving the sampling rate and throughput of 24 KHz and 18.4 Mbps, respectively.

Identifying reliable and accurate parameters of a solid oxide fuel cell (SOFC) is very important to simulate and analyze its dynamic conversion behavior. In this paper, a simplified variant of competitive swarm optimizer (SCSO) is proposed to solve the parameter identification problem of SOFC models. CSO performs well especially on unimodal optimization problems. However, it is with the drawbacks of “two steps forward, one step back” and deviating from the promising direction, resulting in low searching efficiency when solving complex multimodal optimization problems. SCSO adopts two simplified components to conquer the drawbacks: (i) a simplified learning equation: the losers just learn from the winners excluding the mean position of the population; and (ii) a renewed way of random numbers: random numbers are renewed for each loser rather than for each dimension of each loser. SCSO is applied to a Siemen Energy cylindrical cell and a 5-kW dynamic tubular stack. In addition, the influence of weight parameter and the benefit of simplified components are also experimentally investigated. Results present that SCSO is highly competitive in terms of accuracy, robustness, convergence and statistics compared with other advanced algorithms.

Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.

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.

The distance backup relays configured with fixed settings may lose their pre-determined coordination, and even form a significant threat to system security due to the variety of system operation condition. There are many special temporary operation conditions in the developing power grids of China, which may be not anticipated in the offline relay coordination. Therefore, periodical online verification of relay coordination is highly desirable to detect improper or incorrect settings under the current system state. However, the verification method is also required to perform verification correctly and efficiently in large-scale power grids, whose importance is similar to that of equal-area criterion for transient stability analysis, and eigenvalue methodology for low frequency oscillation analysis. A new verification method using setting comparison is presented, which performs verification more conveniently and efficiently than conventional method using reach point comparison. The application in large-scale power grids demonstrates the availability of the proposed method.

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.

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running k-means clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

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.

Noise propagating through open windows raises one major concern of livability in metropolises, where residential buildings and arterial roads are densely constructed. Active noise control (ANC) is a possible technique to improve the current situation. Usually, ANC systems adopt the closed-loop control, where the quietness after control is continuously monitored by error microphones. However, ANC systems that attenuate noise propagating through open windows should ideally avoid the use of error microphones for the ease of implementation and maintenance. Furthermore, as open windows are commonly larger than the wavelength of the incident noise, multichannel ANC systems are necessary. Therefore, they result in computational burdens that cannot be handled by low-cost hardware platforms. This paper formulates the ANC system for open windows as a spatial sampling and reconstruction problem, and demonstrates that controlling noise propagating through open windows leads to global reduction of noise levels in the ...