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Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems.
Junyu Ren; Li Wang; Shaofan Zhang; Yanchun Cai; Jinfu Chen. Online Critical Unit Detection and Power System Security Control: An Instance-Level Feature Importance Analysis Approach. Applied Sciences 2021, 11, 5460 .
AMA StyleJunyu Ren, Li Wang, Shaofan Zhang, Yanchun Cai, Jinfu Chen. Online Critical Unit Detection and Power System Security Control: An Instance-Level Feature Importance Analysis Approach. Applied Sciences. 2021; 11 (12):5460.
Chicago/Turabian StyleJunyu Ren; Li Wang; Shaofan Zhang; Yanchun Cai; Jinfu Chen. 2021. "Online Critical Unit Detection and Power System Security Control: An Instance-Level Feature Importance Analysis Approach." Applied Sciences 11, no. 12: 5460.
Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization strategy is proposed. First, an augmented dataset covering the fault information is constructed, enabling the transient stability assessment (TSA) model to discriminate the system stability under different fault scenarios. Then, the adaptive synthetic sampling (ADASYN) method is implemented to deal with the imbalanced instances of power systems. Next, an instance-based machine model interpretation tool, Shapley additive explanations (SHAP), is embedded to explain the TSA model’s predictions and to find out the most effective control objects, thus narrowing the number of control objects. Finally, differential evolution is deployed to optimize the generation of TSPC measures, taking into account the security and economy of TSPC. The proposed method’s efficiency and robustness are verified on the New England 39-bus system and the IEEE 54-machine 118-bus system.
Junyu Ren; Benyu Li; Ming Zhao; Hengchu Shi; Hao You; Jinfu Chen. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. Energies 2021, 14, 3430 .
AMA StyleJunyu Ren, Benyu Li, Ming Zhao, Hengchu Shi, Hao You, Jinfu Chen. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. Energies. 2021; 14 (12):3430.
Chicago/Turabian StyleJunyu Ren; Benyu Li; Ming Zhao; Hengchu Shi; Hao You; Jinfu Chen. 2021. "Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset." Energies 14, no. 12: 3430.
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 paper 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.
Jingyu Wang; Dongyuan Shi; Jinfu Chen; Chen-Ching Liu. Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments. IEEE Transactions on Smart Grid 2020, 12, 1541 -1551.
AMA StyleJingyu Wang, Dongyuan Shi, Jinfu Chen, Chen-Ching Liu. Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments. IEEE Transactions on Smart Grid. 2020; 12 (2):1541-1551.
Chicago/Turabian StyleJingyu Wang; Dongyuan Shi; Jinfu Chen; Chen-Ching Liu. 2020. "Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments." IEEE Transactions on Smart Grid 12, no. 2: 1541-1551.
Benefiting to the immunization of distributed capacitance, Bergeron model based current differential protection has become a promising backup protection in HVDC grid. However, its performance strongly relies on the precise transmission line parameter, whereas the error of parameter is inevitable due to the frequency-dependent nature. To solve this problem, an enhanced current differential protection with parameter error tolerability is proposed in this paper. The influence of transmission line parameter error on the compensated current is investigated, where the parameter error is embodied by the characteristic impedance and velocity of traveling wave. It is discovered that the major influence comes from the error of traveling wave velocity. The main idea behind the proposed protection is to mitigate the influence from the perspective of velocity. Wavelet transform modulus maxima (WTMM) is adopted to locate and calibrate the arrival time of traveling wave. The differential current under external fault is reduced effectively, which could have been higher than that of the internal fault when the parameter error exists, thus avoiding the protection misoperation. Finally, the feasibility and robustness of the proposed protection are validated in a four-terminal VSC-HVDC grid.
Tongkun Lan; Hao Xiao; Yinhong Li; Jinfu Chen. Enhanced Current Differential Protection for HVDC Grid Based on Bergeron Model: A Parameter Error Tolerable Solution. IEEE Transactions on Power Delivery 2020, 36, 1869 -1881.
AMA StyleTongkun Lan, Hao Xiao, Yinhong Li, Jinfu Chen. Enhanced Current Differential Protection for HVDC Grid Based on Bergeron Model: A Parameter Error Tolerable Solution. IEEE Transactions on Power Delivery. 2020; 36 (3):1869-1881.
Chicago/Turabian StyleTongkun Lan; Hao Xiao; Yinhong Li; Jinfu Chen. 2020. "Enhanced Current Differential Protection for HVDC Grid Based on Bergeron Model: A Parameter Error Tolerable Solution." IEEE Transactions on Power Delivery 36, no. 3: 1869-1881.
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.
Jinfu Chen; Qiaomu Zhu; Hongyi Li; Lin Zhu; Dongyuan Shi; Yinhong Li; Xianzhong Duan; Yilu Liu. Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction. IEEE Transactions on Sustainable Energy 2019, 11, 1761 -1772.
AMA StyleJinfu Chen, Qiaomu Zhu, Hongyi Li, Lin Zhu, Dongyuan Shi, Yinhong Li, Xianzhong Duan, Yilu Liu. Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction. IEEE Transactions on Sustainable Energy. 2019; 11 (3):1761-1772.
Chicago/Turabian StyleJinfu Chen; Qiaomu Zhu; Hongyi Li; Lin Zhu; Dongyuan Shi; Yinhong Li; Xianzhong Duan; Yilu Liu. 2019. "Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction." IEEE Transactions on Sustainable Energy 11, no. 3: 1761-1772.
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 (SWSMs) 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 (E2E) 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.
Qiaomu Zhu; Jinfu Chen; Dongyuan Shi; Lin Zhu; Xiang Bai; Xianzhong Duan; Yilu Liu. Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction. IEEE Transactions on Sustainable Energy 2019, 11, 509 -523.
AMA StyleQiaomu Zhu, Jinfu Chen, Dongyuan Shi, Lin Zhu, Xiang Bai, Xianzhong Duan, Yilu Liu. Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction. IEEE Transactions on Sustainable Energy. 2019; 11 (1):509-523.
Chicago/Turabian StyleQiaomu Zhu; Jinfu Chen; Dongyuan Shi; Lin Zhu; Xiang Bai; Xianzhong Duan; Yilu Liu. 2019. "Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction." IEEE Transactions on Sustainable Energy 11, no. 1: 509-523.
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. It is now promising to use the long-term accumulated historical PMU measurements to train a machine learning model to detect PDMAs. 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. Once trained, an anomaly measurer can assess the PDMA existence possibility of the new PMU meas-urements. By integrating the results of different anomaly meas-urers, the proposed distributed PDMA detection framework will be able to detect PDMAs in the whole power system. The effec-tiveness and detection performance of the framework are dis-cussed through experiments.
Jingyu Wang; Dongyuan Shi; Yinhong Li; Jinfu Chen; Hongfa Ding; Xianzhong Duan. Distributed Framework for Detecting PMU Data Manipulation Attacks With Deep Autoencoders. IEEE Transactions on Smart Grid 2018, 10, 4401 -4410.
AMA StyleJingyu Wang, Dongyuan Shi, Yinhong Li, Jinfu Chen, Hongfa Ding, Xianzhong Duan. Distributed Framework for Detecting PMU Data Manipulation Attacks With Deep Autoencoders. IEEE Transactions on Smart Grid. 2018; 10 (4):4401-4410.
Chicago/Turabian StyleJingyu Wang; Dongyuan Shi; Yinhong Li; Jinfu Chen; Hongfa Ding; Xianzhong Duan. 2018. "Distributed Framework for Detecting PMU Data Manipulation Attacks With Deep Autoencoders." IEEE Transactions on Smart Grid 10, no. 4: 4401-4410.
A probabilistic load flow (PLF) method based on the sparse polynomial chaos expansion (PCE) is presented here. Previous studies have shown that the generalised polynomial chaos expansion (gPCE) is promising for estimating the probability statistics and distributions of load flow outputs. However, it suffers the problem of curse-of-dimensionality in high-dimensional applications. Here, the compressive sensing technique is applied into the gPCE-based scheme, from which the sparse PCE is built as the surrogate model to perform the PLF in an accurate and efficient manner. The dependence among random input variables is also taken into consideration by making use of the Copula theory. Consequently, the proposed method is able to handle the correlated uncertainties of high-dimensionality and alleviate the computational effort as of popular methods. Finally, the feasibility and the effectiveness of the proposed method are validated by the case studies of two standard test systems.
Xin Sun; Qingrui Tu; Jinfu Chen; Chengwen Zhang; Xianzhong Duan. Probabilistic load flow calculation based on sparse polynomial chaos expansion. IET Generation, Transmission & Distribution 2018, 12, 2735 -2744.
AMA StyleXin Sun, Qingrui Tu, Jinfu Chen, Chengwen Zhang, Xianzhong Duan. Probabilistic load flow calculation based on sparse polynomial chaos expansion. IET Generation, Transmission & Distribution. 2018; 12 (11):2735-2744.
Chicago/Turabian StyleXin Sun; Qingrui Tu; Jinfu Chen; Chengwen Zhang; Xianzhong Duan. 2018. "Probabilistic load flow calculation based on sparse polynomial chaos expansion." IET Generation, Transmission & Distribution 12, no. 11: 2735-2744.
Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
Qiaomu Zhu; Jinfu Chen; Lin Zhu; Xianzhong Duan; Yilu Liu. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach. Energies 2018, 11, 705 .
AMA StyleQiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu Liu. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach. Energies. 2018; 11 (4):705.
Chicago/Turabian StyleQiaomu Zhu; Jinfu Chen; Lin Zhu; Xianzhong Duan; Yilu Liu. 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach." Energies 11, no. 4: 705.
Operation of power system within specified limits of voltage and frequency are the major concerns in power system stability studies. As power system is always prone to disturbances, which consequently affect the voltage instability and optimal power flow, and therefore risks the power systems stability and security. In this paper, a novel technique based on the “Artificial Algae Algorithm” (AAA) is introduced, to identify the optimal location and the parameters setting of Unified Power Flow Controller (UPFC) under N-1 contingency criterion. In the first part, we have carried out a contingency operation and ranking process for the most parlous lines outage contingencies while taking the transmission lines overloading (NOLL) and voltage violation of buses (NVVB) as a performance parameter (PP = NOLL + NVVB). As UPFC possesses too much prohibitive cost and larger size, its optimal location and size must be identified before the actual deployment. In the second part, we have applied a novel AAA technique to identify the optimal location and parameters setting of UPFC under the discovered contingencies. The simulations have been executed on IEEE 14 bus and 30 bus networks. The results reveals that the location of UPFC is significantly optimized using AAA technique, which has improved the stability and security of the power system by curtailing the overloaded transmission lines and limiting the voltage violations of buses.
Muhammad Zahid; Jinfu Chen; Yinhong Li; Xianzhong Duan; Qi Lei; Wang Bo; Ghulam Mohy-Ud-Din; Asad Waqar. New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis. Energies 2017, 10, 1738 .
AMA StyleMuhammad Zahid, Jinfu Chen, Yinhong Li, Xianzhong Duan, Qi Lei, Wang Bo, Ghulam Mohy-Ud-Din, Asad Waqar. New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis. Energies. 2017; 10 (11):1738.
Chicago/Turabian StyleMuhammad Zahid; Jinfu Chen; Yinhong Li; Xianzhong Duan; Qi Lei; Wang Bo; Ghulam Mohy-Ud-Din; Asad Waqar. 2017. "New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis." Energies 10, no. 11: 1738.
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.
Siao Wen; Jinfu Chen; Yinhong Li; Dongyuan Shi; Xianzhong Duan. Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning. Mathematical Problems in Engineering 2017, 2017, 1 -23.
AMA StyleSiao Wen, Jinfu Chen, Yinhong Li, Dongyuan Shi, Xianzhong Duan. Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning. Mathematical Problems in Engineering. 2017; 2017 ():1-23.
Chicago/Turabian StyleSiao Wen; Jinfu Chen; Yinhong Li; Dongyuan Shi; Xianzhong Duan. 2017. "Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning." Mathematical Problems in Engineering 2017, no. : 1-23.
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.
Defu Cai; Dongyuan Shi; Jinfu Chen. Probabilistic load flow computation using Copula and Latin hypercube sampling. IET Generation, Transmission & Distribution 2014, 8, 1539 -1549.
AMA StyleDefu Cai, Dongyuan Shi, Jinfu Chen. Probabilistic load flow computation using Copula and Latin hypercube sampling. IET Generation, Transmission & Distribution. 2014; 8 (9):1539-1549.
Chicago/Turabian StyleDefu Cai; Dongyuan Shi; Jinfu Chen. 2014. "Probabilistic load flow computation using Copula and Latin hypercube sampling." IET Generation, Transmission & Distribution 8, no. 9: 1539-1549.
Taking transmission sections as the monitoring objects of power system security and stability level can largely improve the efficiency of analysis and control in power system operation. However, existing approaches for identifying transmission sections mainly depend on years of experience, which is not suitable for complicated and variable power systems with large scales. Thus, a novel method for automatic identification of transmission sections using complex network theory is proposed. The proposed method presents the fundamental conditions of transmission sections and identifies them from three levels: transmission lines, key transmission links and partition sections. First, based on the small-world characteristics of power grid, the index of transmission betweenness is presented to identify key transmission links from transmission lines. Then clustering algorithm of complex network is used to divide the power grid and to obtain partition sections from the key transmission links. Finally, the combinations of partition sections are selected and ranked as the transmission sections. Numerical results with CEPRI-36 system and a provincial system are provided to demonstrate the effectiveness and adaptability of the proposed method.
Luo Gang; Shi Dongyuan; Chen Jinfu; Duan Xianzhong. Automatic identification of transmission sections based on complex network theory. IET Generation, Transmission & Distribution 2014, 8, 1203 -1210.
AMA StyleLuo Gang, Shi Dongyuan, Chen Jinfu, Duan Xianzhong. Automatic identification of transmission sections based on complex network theory. IET Generation, Transmission & Distribution. 2014; 8 (7):1203-1210.
Chicago/Turabian StyleLuo Gang; Shi Dongyuan; Chen Jinfu; Duan Xianzhong. 2014. "Automatic identification of transmission sections based on complex network theory." IET Generation, Transmission & Distribution 8, no. 7: 1203-1210.