Metering data from the advanced metering infrastructure can be used to find abnormal electricity behavior for the detection of electricity theft, which causes huge financial losses to electric companies every year. This article proposes an electricity theft detector using metering data based on extreme gradient boosting (XGBoost). The metering data are preprocessed, including recover missing and erroneous values and normalization. The classification model based on XGBoost is trained using both benign and malicious samples. Simulations are done by using the Irish Smart Energy Trails data set with six certain attack types. Compared with the support vector machine, decision tree, and other eight machine learning methods, the proposed method can detect electricity theft with either higher accuracy or lower false-positive rate. Experiment results also demonstrate that the proposed method is robust when the data are imbalanced. Our codes are available at https://github.com/WenHe-Hnu/Electric_Theft_XGBoost .

Harmonic estimation is an important topic in power system signal processing. Windowed interpolation fast Fourier transformation (WIFFT) is an efficient algorithm for power system harmonic estimation, which can eliminate the errors caused by spectral leakage and picket fence effect. However, the fitting polynomial in the interpolation procedure contains both even and odd terms, and this increases the computational burden. This paper proposes a simple symmetrical interpolation FFT algorithm, where the even terms are removed from the fitting polynomial based on the triangular self-convolution windows (TSCW). The polynomials for frequency and amplitude computations are provided. Considerable leakage errors and harmonic interferences can be suppressed by the TSCW. Accurate estimations of harmonic parameters can be obtained via the fitting polynomial and the TSCW, both with adjustable order to fulfill different accuracy and speed requirements of practical power harmonic measurement. Simulation results and measurements have validated the proposed method.

Electricity theft has been a growing concern for the smart grid. It can be defined as follows: illegal customers use energy from electric utilities without a contract or manipulate their meter readings to pay less or not pay the electricity bill. Over the past decade, significant studies have been done to prevent and combat theft. This article aims to provide a general overview of the progress of electricity theft detection, including threat models, datasets and input features adopted, methodologies and techniques, and evaluation metrics. We also make a performance comparison for each detection method. Finally, the challenges and future research directions are presented in this article.

Deep learning has been widely used in recent years for printed circuit board (PCB) defect detection because of its excellent performance. However, deep-learning-based approaches often suffer from the over-fitting problem due to the lack of sufficient training data in real applications. Meanwhile, these approaches still have some challenges to detect these defects with small sizes and irregular shapes. To address these problems, this article has developed a novel deep Siamese semantic segmentation network which integrates the similarity measurement of the Siamese network with the encoder–decoder semantic segmentation network for PCB welding defect detection. This network includes two encoders sharing weighted values, a decoder, and some correlation modules, in which the decoder integrates deep features from two decoders with their feature difference computed by some correlation modules via skipping connections to recover spatial information on multiple output layers, and thus this proposed network can perform PCB welding small defect semantic segmentation. Moreover, via these correlation modules, this proposed network can pay more attention to semantic difference and further alleviate the over-fitting issue because of insufficient defect samples. Finally, we propose a combined loss function which combines the weighted cross-entropy loss, the Lovasz softmax loss, and the weighted precision–recall loss for network training to further improve small defect segmentation and recall improvement. Experimental results demonstrate that the proposed network can be trained on limited training images and achieve high efficiency and outstanding effects for PCB welding small defect segmentation.

The wide bandwidth of phase-locked loop (PLL) will increase the negative real part of the output impedance of the grid-connected inverter (GCI), thus destroying the stability of the weak grids. This article proposes a novel method to improve the system stability through decoupling the PLL and grid impedance. Initially, the coupling relationship between PLL and grid impedance is revealed. It can explain why the choice of PLL bandwidth and the grid impedance magnitude is limited. Then, the decoupling method is proposed where the primary control structure of GCI does not need to change and uses the structured voltage, the product of PCC voltage and grid admittance, as the improved input of PLL. The proposed method can achieve the full-frequency passive of the output impedance of GCI even if the PLL has high bandwidth and the impedance measurement error is up to 60%, which means the adverse stability effect of PLL on the system is eliminated. The theoretical analysis is verified by simulations and experiments.

The phase-locked loop (PLL)-caused couplings on the grid-connected converter (GCC) are strengthened by increasing its bandwidth and then incur synchronization instability. This article proposes a constant-coupling-effect-based PLL (CCE–PLL) to resolve the aforementioned issues. Initially, the feature of the PLL-caused couplings on GCC is explored. It exhibits that the couplings are varied along with the PLL bandwidth hence jeopardizing the system's stability. Subsequently, the CCE–PLL with a constant and low coupling regardless of its bandwidth on the GCC is proposed. It illustrates that the CCE–PLL bandwidth does not influence the GCC's impedance response, bringing the GCC and CCE–PLL can be designed separately. Last, the experiments confirm that using CCE–PLL, the system has high robustness against grid impedance variation, and the GCC is capable of injecting/absorbing 1.0 per unit active power in a very weak grid. Moreover, the system attains good anti-interference ability and transient performance under system disturbances thanks to the permission usage of the high CCE–PLL bandwidth.