Intelligent identification of multiple power quality (PQ) disturbances is very useful for pollution control of power systems. In this study, we propose a novel detection framework for complex PQ disturbances based on multi-fusion convolutional neural network (MFCNN). Our contributions focus on automatic extraction and fusion of features from multiple sources. First, an information fusion structure is introduced in which the time domain and frequency domain information of the PQ disturbance signal are used as inputs. Additionally, the one-dimensional composite convolution is proposed to improve the diversity of network features based on the standard convolution and dilated convolution. Then, to speed up the training and prevent overfitting, batch normalization is used to adjust the distribution of features. Second, we use several visualization methods to resolve the internal mode of MFCNN, and demonstrate the working mechanism of the proposed method. Finally, we conducted various experiments to verify the effectiveness of the MFCNN. Compared with the handcrafted feature design methods and the general convolutional neural network models, the simulation under different noises and hardware platform-based experiments verify the effectiveness of noise immunity, higher training speed, and better accuracy of the method.

Accurate power quality disturbance (PQD) classification is significantly important for power grid pollution control. However, the use of non-linear loads makes power system signals complex and distorted, and thus increases the difficulty of detecting and classifying PQD signals. To address this issue, this paper first proposes an optimized S-Transform (OST). It optimizes different window parameters to improve time-frequency resolution using maximum energy concentration. A kernel support vector machine (KSVM) classifier is proposed to classify multiple features using a combination of kernels. Integrating OST and KSVM, a classification framework is further proposed to detect and classify various PQD signals. Extensive experiments on computer simulations and experimental signals demonstrate that the proposed classification framework shows better performance than several state-of-art methods in classifying not only single and multiple PQD signals but also PQD signals with different noise levels. More importantly, our framework has superior performance in detecting nonlinearly mixed PQD signals.

Due to the independent controllability and fast power regulation capability, the High Voltage Direct Current (HVDC) system could be a prospective technology to provide multiple ancillary services to the system besides conventional bulk power transmission. However, with the increase of False Data Injection Attacks (FDIAs) on PMU data, the HVDC system could have the wrong response once the collected data that the HVDC system relied on is attacked, thus threatening the system operating security. How to ensure the security of the PMU-based HVDC ancillary service control become an urgent issue. To mitigate the risk, this paper proposed an HVDC attack-defense control based on the FDIAs detection method. Firstly, the Squeeze-Excitation based Double Convolutional Neural Networks (SE-DCNN) is proposed to realize fast identification of the attacking frequency type based on the time and frequency domain signals. The duration time of FDIAs is detected by the local outlier factor. Then, utilizing the results from SE-DCNN, HVDC ancillary service control framework is reorganized and an HVDC attack defense control is proposed for suppressing the potential influence of various types of FDIAs on the HVDC system ancillary service. Different experiments results demonstrate that the proposed method has the ability to significantly mitigate the frequency deviation and oscillation under the FDIA.

Owing to the fast and large power regulating the capacity of the HVDC system, the wide-area measurement system (WAMS) based high voltage direct current (HVDC) system has been regarded as a prospective solution to deal with the low-frequency oscillation issue. However, due to the vulnerability of the WAMS communication, WAMS based HVDC system control can be a prime target of malicious penetrations that could lead to disastrous events. To remediate this adverse effect, an improved WAMS based HVDC damping control framework is proposed. First, a lightweight network named Attack Shuffle convolutional neural Networks (ASNet) is proposed to learn the characteristics of cyber attacks. Then, a model-free-based cyber attack defense framework is introduced to quickly identify the attack types based on the continuous wavelet transform and ASNet. Additionally, an improved control framework of the WAMS and HVDC-based wide-area power oscillation damping control (WH-PODC) is developed to provide different response control for mitigation of the impact of cyber attacks. Finally, the performance of the proposed WH-PODC control framework is evaluated with real PMU data in multiple scenarios in RTDS, where the results indicate that the response intensity can be kept under multiple types of cyber attacks while providing similar effectiveness in oscillation suppression to conventional controls.

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. To address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.