Tiecheng Li

Secondary systems in electrical engineering often rely on traditional CAD software (AutoCAD v2024.1.6) or non-structured, paper-based diagrams for fieldwork, posing challenges for digital transformation. Electrical diagram recognition technology bridges this gap by converting traditional diagram operations into a “digital” model, playing a critical role in power system scheduling, operation, and maintenance. However, conventional recognition methods, which primarily rely on partition detection, face significant limitations such as poor adaptability to diverse diagram styles, interference among recognition objects, and reduced accuracy in handling complex and varied electrical diagrams. This paper introduces a novel layered framework for electrical diagram recognition that sequentially extracts the element layer, text layer, and connection relationship layer to address these challenges. First, an improved YOLOv7 model, combined with a multi-scale sliding window strategy, is employed to accurately segment large and small diagram objects. Next, PaddleOCR, trained with electrical-specific terminology, and PaddleClas, using multi-angle classification, are utilized for robust text recognition, effectively mitigating interference from diagram elements. Finally, clustering and adaptive FcF-inpainting algorithms are applied to repair the connection relationship layer, resolving local occlusion issues and enhancing the overall coupling of the diagram. Experimental results demonstrate that the proposed method outperforms existing approaches in robustness and universality, particularly for complex diagrams, providing technical support for intelligent power grid construction and operation.

Photovoltaic solar irradiance meter is defined as a photoelectric type of meter with a detector of solar cell. This paper focuses the calibration of photovoltaic solar irradiance meter in reference to the latest domestic and international standard. The whole theory is based on current methods and techniques of the calibration of pyranometer and irradiance meter. As a result, with principle and characteristic taken into consideration, the dissemination system of photoelectric type solar irradiance meter is established to ensure the accuracy and reliability of the value utilization of solar energy industry. The uncertainty of the measurement is also evaluated while the measurement results are obtained through experiments.

UV LED has been widely used in polymer curing, disinfection and many other fields. Especially, UV curing is one of its most important application directions. Accurate measurement of UV LED radiation has become increasingly important. The LED UV irradiance meter is a reliable and easy-to-use measurement device. In this paper, we developed an UV LED integrating sphere radiation source, which was used to calibrate UV irradiance meters. The experimental result was compared with National Institute of Metrology (NIM), which validated the measurement accuracy of our setup.

With the development of distributed renewable generations, a large number of distributed generations (DGs) are connected to the microgrid. Therefore, distributed cooperative algorithms are more suitable for optimal dispatch of the microgrid than centralized algorithms. This paper proposes a novel distributed cooperative control method for optimal dispatch of microgrids, considering line losses and time delay. First, the optimization model of the microgrid considering line losses is established, and optimality conditions are obtained. Second, a novel distributed optimization method considering line losses is proposed, where the primary control is to achieve optimal dispatch, and the secondary control is to achieve frequency regulation. Third, the stability of the system under time delay is analyzed, and the robust stability conditions are obtained. Finally, simulation results verify the the effectiveness of the proposed method.

With the intensive integration of photovoltaic (PV) sources into the low-voltage distribution networks (LVDN), the nodal voltage limit violations and fluctuation problem cause concerns on the safety operation of a power system. The intermittent, stochastic, and fluctuating characteristics of PV output power leads to the frequent and fast fluctuation of nodal voltages. To address the voltage limit violation and fluctuation problem, this paper proposes a distributed nodal voltage regulation method based on photovoltaic reactive power and on-load tap changer transformers (OLTC). Using the local Q/V (Volt/Var) feedback controller derived from the grid sensitivity matrix, the voltage magnitude information is adopted to adjust the output of PV systems. Moreover, in order to share the burden of voltage regulation among distributed PV systems, a weighted distributed reactive power sharing algorithm is designed to achieve the voltage regulation according to the rated reactive power. Theoretical analysis is provided to show the convergence of the proposed algorithm. Additionally, the coordination strategy for distributed PV systems and OLTC is provided to reduce the reactive power outputs of PV systems. Five simulation case studies are designed to show the effectiveness of the proposed voltage regulation strategy, where the voltage regulation and proportional reactive power sharing can be achieved simultaneously.

The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.

For the maximum penetration capacity problem of distributed PV, a computational model with power equation, node voltage, line capacity and distributed PV capacity as the main constraints is established, and an improved firework algorithm is proposed to solve the above model. Based on the semi-positive cloud generator to improve the explosion spark generation method, a large number of displacement deviations with stable propensity and randomness are obtained, increasing the population diversity and speeding up the convergence; the dimensional difference strategy is used to improve the variance operator, which is conducive to the search for the late jump out local optimum. Apply the algorithm to the CIGER6 Task Force system and verify the validity of the algorithm. The results show that the proposed algorithm is able to solve the penetration level calculation problem of distributed PV.