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Over the years, probabilistic nature of renewable energy sources (RES) and its influence on power system adequacy have been well studied. However, rather less attention has been paid to the impact of RES unit itself's and its power conversion system's (PCS') reliability, as well as their various connection topologies. This paper devises a comprehensive sensitivity study on how each of these elements can affect overall generating system reliability. given the plethora of RES configurations and components, it is of import to identify the most vulnerable element in RES. In this work, component importance is extended, for the first time, to generating capacity adequacy assessment (HLI). Measurement index is the centerpiece in reliability importance. New indices have to be introduced to facilitate the study. While the physical meaning of previously developed indices is lost, in this study indices are proposed based on traditional importance measures, of which the physical meaning are strictly retained and consistent with the definitions. With the proposed assessment technique, components in various RES configuration can be ranked according to their reliability importance. It is found in the numerical study that different importance measures (such as risk-achievement based measures and risk-reduction based measures) can result in different rankings. Studies on contributing factors of the reliability importance are also performed. As more and more RES gaining foothold in generating systems, the proposed technique assist to achieve targeted reliability level of the system, by easily identifying and prioritizing reliability improvement tasks among various units/components in the increasing complex system.

This paper proposes a novel adaptive range composite differential evolution (ARCoDE) algorithm to efficiently and accurately solve optimal reactive power dispatch (ORPD) problem. Because of a novel adaptive range strategy for control parameters, the proposed ARCoDE possesses superior exploration and exploitation capabilities that can efficiently handle the ORPD problem involving complicated constraints and discrete and continuous variables. This has been demonstrated in case studies using the IEEE optimal power flow testbeds considering complex wind and demand scenarios. The superior performance of ARCoDE has been further validated through comparisons with several award-winning algorithms in 2014 IEEE Competition on “Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems”, given limited iterations of in evolutionary optimization process.

In this paper our group proposes and designs a lightweight convolutional neural network (CNN) for detecting facial emotions in real-time and in bulk to achieve a better classification effect. We verify whether our model is effective by creating a real-time vision system. This system employs multi-task cascaded convolutional networks (MTCNN) to complete face detection and transmit the obtained face coordinates to the facial emotions classification model we designed firstly. Then it accomplishes the task of emotion classification. Multi-task cascaded convolutional networks have a cascade detection feature, one of which can be used alone, thereby reducing the occupation of memory resources. Our expression classification model employs Global Average Pooling to replace the fully connected layer in the traditional deep convolution neural network model. Each channel of the feature map is associated with the corresponding category, eliminating the black box characteristics of the fully connected layer to a certain extent. At the same time, our model marries the residual modules and depth-wise separable convolutions, reducing large quantities of parameters and making the model more portable. Finally, our model is tested on the FER-2013 dataset. It only takes 3.1% of the 16GB memory, that is, only 0.496GB memory is needed to complete the task of classifying facial expressions. Not only can our model be stored in an 872.9 kilobytes file, but also its accuracy has reached 67% on the FER-2013 dataset. And it has good detection and recognition effects on those figures which are out of the dataset.

Energy storage is essential for balancing the generation and load in power systems. Building a battery energy storage system (BESS) with retired battery packs from electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) is one possible way to subsidize the price of EV/PHEV batteries, and at the same time mitigating forecast error introduced by load and renewable energy sources in power systems. This paper proposes a detailed framework to evaluate end-of-life (EOL) EV/PHEV batteries in BESS application. The framework consists of three parts. A generalized model for battery degradation is first introduced. It is followed by modeling the battery retirement process in its first life. Two vehicle types—EV and PHEV—as well as two retirement modes—nominal and realistic modes—are considered. Finally, the application of the second-life BESS in power systems is modeled in a detailed economic dispatch (ED) problem. This is how second-life BESS’s performance translates into cost savings on power generation. An optimization problem is formulated to maximize total cost savings in power generation over the battery’s second life. This is done by striking a balance between short-term benefit (daily cost savings) and long-term benefit (cost savings through service years). Numerical results validate the effectiveness of the proposed framework/models. They show that battery usage and retirement criterion in its first life directly affect the performance in its second life application. In our case study, EV battery packs possess larger EOL energy capacities and consequently generate more cost savings in the second life. However, the BESS built from retired PHEV batteries has higher cost savings per MWh. It is because, with the proposed degradation model, battery health is better preserved in PHEV applications. Compared to nominal retirement mode, realistic retirement mode results in extra cost savings due to the reduced first-life service years.

Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similarity-based forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.

Monitoring train operation status is one of the most important tasks for ensuring rail operation safety. Pantograph and catenary (PAC) are collecting systems of the electric current from traction power supply system, and the stability of the contact between pantograph and catenary guarantees the stable power. However, most existing contact point (CPT) detection methods are always difficult to achieve precise positioning results, especially in complicated background. This article proposes a novel fast and accurate contact point detection method based on multiple strategies, which combine three modules. First, an improved kernelized correlation filter model in real-time tracking module was adopted to track the contact region. Then the pixel-level detection module was used to detect contact point in contact region via the proposed contact point regression residual network (CPRR-Net). Finally, a filter-based optimization module was added to correct the contact position using the Kalman filter. This work additionally employed a new rail dataset PAC-TPL2020 to prove the effectiveness and feasibility of the proposed multiple strategies in real-world scenarios, and the experimental results demonstrated the robustness and high accuracy (97.07% within 3 pixels and 99.97% within 5 pixels) of our model. It is noteworthy that our mothed runs at 65 frames per second for monitoring PAC contact points.

In this paper, we design a variable-gain first order sliding mode, in which the adaptive gain is state-independent and designed by two types of smooth, monotonically increasing and bounded functions. We modify the structure of the sliding mode even further by using an amplifying overall scaling factor. Both of the variable gain and the scaling factor can improve the control accuracy. Then we apply the newly designed sliding mode to the attitude synchronization of spacecraft. Simulation results show that, compared with the constant gain sliding mode, the proposed design ensures much higher control precision evaluated by the attitude synchronization error without more fuel consumption.

Wildfires could pose a significant danger to electrical transmission lines and cause considerable losses to the power grids and residents nearby. Previous studies of preventing wildfire damages to electrical transmission lines mostly analyze wildfire and power system security independently due to their differences in disciplines and cannot satisfy the requirement of the power grid for active and timely responses. In this paper, we have designed an integrated wildfire early warning system framework for power grids, taking prediction of wildfires and early warning of line outage probability together. First, the proposed model simulates the spatiotemporal process of wildfires via a geography cellular automata model and predicts when and where wildfires initially get into the security buffer of an electrical transmission line. It is developed in the context of electrical transmission line operating with various situations of topography, vegetation, wind and, especially, multiple ignition points. Second, we have proposed a line outage model (LOM), based on wildfire prediction and breakdown mechanisms of the air gap, to predict the breakdown probability varying with time and the most vulnerable poles at the holistic line scale. Finally, to illustrate the validation and rationality of our proposed system, a case study for a 500-kV transmission line near Miyi county, China, is presented, and the results under various wildfire situations are studied and compared. By integrating wildfire prediction into the LOM and alarming the holistic line breakdown probability along time, this paper makes a significant contribution in the early warning system to prevent transmission lines to be damaged by wildfires, illustrating the related breakdown mechanisms at the line operation level rather than laboratory experiments only. Meanwhile, the implementation of cellular automata model under comprehensive environmental conditions and simulation of the breakdown probability for the 500-kV transmission line could serve as references for other studies in the community.

The measurement precision of the optical encoder is influenced by coupling interferences from the system and the environment. To further improve the measurement precision of the optical encoder, in this article, a nonlinear error compensation method is proposed to extract the optimal underlying trend of the measurement error, which is based on the empirical mode decomposition (EMD) with a local-sinusoidal-assisted (LSA) scheme and an adaptive intrinsic mode function (IMF) selection scheme. To solve the inherent problem of mode-mixing phenomena existing in the decomposed IMFs from the measurement error, an LSA scheme is proposed to adaptively change the extreme distribution of the measurement error, which is based on an abnormal segment discrimination rule. An adaptive IMF selection scheme is proposed to construct the optimal underlying trend of the measurement error, which is first formulated as an optimization problem. Next, a significant factor is defined to select the eligible IMFs with more contributions to the underlying trend. Comparison experiments indicate that the eligible IMFs can be adaptively selected, and the proposed compensation method achieves an excellent compensation performance with the root-mean-square error of 0.380 $\mu$ m in the 95% confidence interval of $-$ 0.017–0.016 $\mu$ m, which is superior to the previous EMD-based compensation methods. [ABSTRACT FROM AUTHOR]

This paper investigates the design of effective input signals for low-level probing of power systems. In 2005, 2006, and 2008 the Western Electricity Coordinating Council (WECC) conducted four large-scale system-wide tests of the western interconnected power system where probing signals were injected by modulating the control signal at the Celilo end of the Pacific DC intertie. A major objective of these tests is the accurate estimation of the inter-area electromechanical modes. A key aspect of any such test is the design of an effective probing signal that leads to measured outputs rich in information about the modes. This paper specifically studies low-level probing signal design for power-system identification. The paper describes the design methodology and the advantages of this new probing signal which was successfully applied during these tests. This probing input is a multi-sine signal with its frequency content focused in the range of the inter-area modes. The period of the signal is over 2 min providing high-frequency resolution. Up to 15 cycles of the signal are injected resulting in a processing gain of 15. The resulting system response is studied in the time and frequency domains. Because of the new probing signal characteristics, these results show significant improvement in the output SNR compared to previous tests. [ABSTRACT FROM AUTHOR]

This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data. [ABSTRACT FROM AUTHOR]

The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three mode-estimation block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with Spectral analysis (YWS); and sub-space system identification (N4SID). The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance. [ABSTRACT FROM AUTHOR]

This paper proposes a robust recursive least square (RRLS) algorithm for online identification of power system modes based on measurement data. The measurement data can be either ambient or ringdown. Also, the mode estimation is provided in real-time. The validity of the proposed RRLS algorithm is demonstrated with both simulation data from a 17-machine model and field measurement data from a wide area measurement system (WAMS). Comparison with the conventional recursive least square (RLS) and least mean square (LMS) algorithms shows that the proposed RRLS algorithm can identify the modes from the combined ringdown and ambient signals with outliers and missing data in real-time without noticeable performance degradation. An adaptive detrend algorithm is also proposed to remove the signal trend based on the RRLS algorithm. It is shown that the algorithm can keep up with the measurement data flow and work online to provide real-time mode estimation. [ABSTRACT FROM AUTHOR]

In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals, including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective, even when the probing signal is small. [ABSTRACT FROM AUTHOR]

Applying the fault diagnosis techniques to power electronic equipments is beneficial to improve the stability and reliability of renewable energy system, because power electronic equipments are a key component of renewable energy system that largely defines their performance. This paper presents a novel intelligent fault diagnosis method for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods. Firstly, the three-phase AC currents of the power-electronics energy conversion system are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to preprocess the fault data. After feature transformation, the slopes of current trajectories (transformed features) are not affected by different loads. And then random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier with the processed fault data. Finally, the proposed method is implemented online on an actual three-phase PWM rectifier platform. The results show that the proposed fault diagnosis method can successfully detect and locate the open-circuit faults in IGBTs of the three-phase PWM rectifier. In addition, the proposed method is suitable for most of three-phase power-electronics energy conversion systems.