Optimal Planning of Virtual Inertia Installations to Improve the Power System Frequency Response

In recent years, the power system has seen a fast transformation from one primarily based on fossil energy to one where renewable energy, especially wind and solar power, takes a more significant proportion in the energy profile. With the shift in energy profile come the changes in the electricity generation units. The solar panels and wind turbines replace the synchronous generators in electricity generation. Most solar and wind generation units are converter-interfaced. In contrast, the synchronous generator is connected to the power grid directly. For this reason, the future power system of a high level of renewable penetration will exhibit dynamic properties different from the traditional power system, which poses many challenges. One of the challenges is related to frequency stability. The frequency stability of a traditional power system is secured with a three-level frequency control scheme. The scheme is composed of three frequency regulation mechanisms at different time scales. The fastest control mechanism, named primary frequency control, needs about 5 s to be fully deployed to arrest the frequency drops or overshoots. After that, the other two frequency secondary and tertiary frequency control mechanisms are then slowly deployed to bring the system frequency back to the nominal value. Under this control scheme, the overall active power generation and consumption in a power system get balanced, and the power frequency variation is limited within a narrow range around a nominal value. However, before the primary frequency control is sufficiently deployed, the system relies on the natural inertia response of the synchronous generators to maintain the active power balance at the sacrifice of changes in the generators' rotational speeds. As the power frequency is decided collectively by the rotational speeds of all synchronous generators in the system, larger system inertia means smaller power frequency variation when subject to the same disturbance. Since there is no lack of system inertia in a synchronous generator-dominant power system, the power frequency variation with the help of the tertiary control scheme is usually contained within a limited range. For a future power system with more and more synchronous generators being displaced by converter-interfaced generation (CIG) units, the system inertia decreases. The tertiary frequency control scheme alone can no longer limit the power frequency variation within an acceptable range. For this reason, techniques were proposed to emulate inertia response on a converter-interfaced generation unit. Apart from the level of total system inertia, studies show that the spatial distribution of system inertia can also influence the frequency response. Under this context, a well-planned virtual inertia installation at selected locations can achieve a satisfactory level of improvement on frequency response at a low investment cost. This thesis work aims at developing a systematic method to search for the most economical plan of virtual inertia installations while ensuring a satisfactory level of frequency response. In order to derive the most economical plan of virtual installation, a mathematical optimization problem is proposed with constraints formulated with the help of a newly proposed metric of inertia response that quantifies the influence of inertia on the system frequency response. The formulation of the optimization problem considers all possible combinations of loading and renewable generation profiles. Two methods are proposed to solve the optimization problem of the mixed-integer type. The first one is based on the classic scheme of dynamic programming. The second method adopts a relaxation technique based on the sparsity promotion or Majorize-Minimization (MM) method. Furthermore, parallel and cloud programming techniques are used to facilitate computation speed. Other minor contributions include a design of a supplementary controller on top of the inertia emulation control to improve the voltage stability of a converter-interfaced generation unit. Finally, case studies were conducted on a modified Southeast Australian power system against different types of faults to validate the performance and investment cost of the virtual inertia installation plan givens by the proposed method in comparison with two other methods. The result shows that the virtual inertia installation plan given by the proposed method produces better performance while at lower investment costs

A new quantity for statistical analysis: "Scaling invariable Benford distance"

For the first time, we introduce "Scaling invariable Benford distance" and "Benford cyclic graph", which can be used to analyze any data set. Using the quantity and the graph, we analyze some date sets with common distributions, such as normal, exponent, etc., find that different data set has a much different value of "Scaling invariable Benford distance" and different figure feature of "Benford cyclic graph". We also explore the influence of data size on "Scaling invariable Benford distance", and find that it firstly reduces with data size increasing, then approximate to a fixed value when the size is large enough.Comment: 4 pages,4 figure

Dynamical Behavior of the Stochastic Delay Mutualism System

We discuss the dynamical behavior of the stochastic delay three-specie mutualism system. We develop the technique for stochastic differential equations to deal with the asymptotic property. Using it we obtain the existence of the unique positive solution, the asymptotic properties, and the nonpersistence. Finally, we give the numerical examinations to illustrate our results

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models

Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion. In reality, an ideal robust model will probably behave similarly to the oracle (e.g., the human users), thus a good evaluation protocol is probably to evaluate the models' behaviors in comparison to the oracle. In this paper, we introduce a new robustness measurement that directly measures the image classification model's performance compared with a surrogate oracle (i.e., a foundation model). Besides, we design a simple method that can accomplish the evaluation beyond the scope of the benchmarks. Our method extends the image datasets with new samples that are sufficiently perturbed to be distinct from the ones in the original sets, but are still bounded within the same image-label structure the original test image represents, constrained by a foundation model pretrained with a large amount of samples. As a result, our new method will offer us a new way to evaluate the models' robustness performance, free of limitations of fixed benchmarks or constrained perturbations, although scoped by the power of the oracle. In addition to the evaluation results, we also leverage our generated data to understand the behaviors of the model and our new evaluation strategies

Predicting hyperlinks via hypernetwork loop structure

While links in simple networks describe pairwise interactions between nodes, it is necessary to incorporate hypernetworks for modeling complex systems with arbitrary-sized interactions. In this study, we focus on the hyperlink prediction problem in hypernetworks, for which the current state-of-art methods are latent-feature-based. A practical algorithm via topological features, which can provide understandings of the organizational principles of hypernetworks, is still lacking. For simple networks, local clustering or loop reflects the correlations among nodes; therefore, loop-based link prediction algorithms have achieved accurate performance. Extending the idea to hyperlink prediction faces several challenges. For instance, what is an effective way of defining loops for prediction is not clear yet; besides, directly comparing topological statistics of variable-sized hyperlinks could introduce biases in hyperlink cardinality. In this study, we address the issues and propose a loop-based hyperlink prediction approach. First, we discuss and define the loops in hypernetworks; then, we transfer the loop-features into a hyperlink prediction algorithm via a simple modified logistic regression. Numerical experiments on multiple real-world datasets demonstrate superior performance compared to the state-of-the-art methods

Continual Learning on Dynamic Graphs via Parameter Isolation

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines

PMVT: a lightweight vision transformer for plant disease identification on mobile devices

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios

Intravitreal Melphalan for Vitreous Seeds: Initial Experience in China

Purpose. To evaluate the efficacy of intravitreal melphalan for vitreous seeds from retinoblastoma in Chinese patients. Methods. This is a retrospective review of 17 consecutive Chinese patients (19 eyes) with viable vitreous seeds from retinoblastoma. The patients received multiple intravitreal injections of 20 ug melphalan. Results. The International Classification of Retinoblastoma groups were B in 1 eye, C in 5 eyes, D in 11 eyes, and E in 2 eyes. On average, 6 injections (range: 1–15) were given to each eye at the interval of 2–4 weeks. Successful control of vitreous seeds was achieved in 16 of 19 eyes (84.21%). Globe retention was achieved in 73.68% (14/19) eyes. The patients were followed up for 27 months on average (median: 26; range: 17–42 months). There is a significant difference in response to intravitreal melphalan for cloud, spheres, and dust seeds with a median number of injections of 9, 6, and 3, respectively (P=0.003). Complications related to intravitreal melphalan included vitreous hemorrhage, cataract, salt-and-pepper retinopathy, and pupil posterior synechia. There was no case of epibulbar extension or systemic metastasis within the period of follow-up. Conclusion. Intravitreal melphalan achieved a high local control rate for vitreous seeds without extraocular extension and with acceptable toxicity in Chinese retinoblastoma patients