e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献_第9页

Anomergy - A novel strategic data-driven framework for detecting anomalies in residential community using temporal intelligence Anomergy -一个新的战略数据驱动框架，用于使用时间智能检测住宅社区的异常情况

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-23 DOI: 10.1016/j.prime.2025.101049

Vishnu Dharssini A․C․, Charles Raja S․, Praveen Kumar M․, Arun Mozhi S․

Efficient energy management is critical in modern residential systems, particularly when integrating grid-supplied and renewable energy sources. This study proposes a robust anomaly detection framework—Anomergy—leveraging a stacked Long Short-Term Memory (LSTM) network to identify inefficiencies, equipment malfunctions, and unauthorized energy consumption. Using a comprehensive year-long hourly dataset from ten residential units in the ARMD Complex, Theni, Tamil Nadu, Anomergy significantly outperformed traditional models. Specifically, it achieved precision and recall rates of 0.85 and 0.80, respectively, surpassing conventional methods such as Support Vector Machines and Random Forest. Advanced data preprocessing techniques, including time-series decomposition, normalization, and Synthetic Minority Oversampling Technique (SMOTE), improved anomaly detection recall by 23 %, addressing significant dataset imbalance. Adaptive thresholding further reduced false positives by 18 %, enhancing detection accuracy. Real-time implementation demonstrated substantial operational savings, achieving average monthly energy savings of 21.75 kWh per household and annual community-wide carbon emission reductions of approximately 2214.6 kg CO₂. These findings underscore the transformative potential of Anomergy for real-time, scalable, and sustainable energy practices in smart residential ecosystems.

有效的能源管理对现代住宅系统至关重要，特别是在整合电网供应和可再生能源时。本研究提出了一种鲁棒异常检测框架——anomergy——利用堆叠长短期记忆（LSTM）网络来识别效率低下、设备故障和未经授权的能源消耗。使用来自泰米尔纳德邦Theni ARMD综合体10个住宅单元的综合一年一小时数据集，Anomergy显著优于传统模型。具体来说，它的准确率和召回率分别为0.85和0.80，超过了传统的方法，如支持向量机和随机森林。先进的数据预处理技术，包括时间序列分解、归一化和合成少数派过采样技术（SMOTE），将异常检测召回率提高了23%，解决了严重的数据不平衡问题。自适应阈值进一步降低了假阳性18%，提高了检测精度。实时实施显示了大量的运营节约，每个家庭平均每月节约能源21.75千瓦时，每年减少社区碳排放量约2214.6千克二氧化碳。这些发现强调了Anomergy在智能住宅生态系统中实现实时、可扩展和可持续能源实践的变革潜力。

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引用次数: 0

Design and investigation of a delay controlled ALU employing FinFET& CNTFET technologies 采用finfet和cnfet技术的延迟控制ALU的设计与研究

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-21 DOI: 10.1016/j.prime.2025.101051

Ch JayaPrakash , Ashok Battula , SurendraBabu Velagaleti

The Arithmetic and Logic Unit (ALU) is a crucial logical element of real-time semiconductor devices. Conventional ALUs that are built using Complementary Metal Oxide Semiconductor (CMOS) technology exhibit increased power usage and processing delays. This investigation intends to develop a delay-controlled reconfigurable ALU employing advancements in FinFET (Fin Field Effect Transistor) Carbon Nano Tube Field Effect Transistor and (CNTFET) technology. The first development is the integration of Carry Output Predicted Adders (COPA) along with Carry Input Selective Adders (CISA) employing multiplexing selection circuitry, allowing the development of adders and subtractors with controllable delay. The Delay Controllable Reconfiguration ALU may be built by integrating arithmetic and logical functions. Nanotechnology-based techniques exceed traditional adders and subtractors regarding power efficiency and latency. The outcome results have been obtained with Cadence Virtuoso utilizing 18 nm finFET technological advances, having supply voltages between 0.8 V and 1 V and temperatures between -25 °C and 50 °C. Simulation findings indicate that the suggested 4-bit ALU has remarkable stability. The proposed CNTFET 4-bit Arithmetic Logical Unit (ALU) exhibits a 30 % decrease in power consumption and a 40 % decrease in delay time relative to existing 4-bit ALUs.

算术逻辑单元（ALU）是实时半导体器件中至关重要的逻辑元件。使用互补金属氧化物半导体（CMOS）技术构建的传统alu会增加功耗和处理延迟。本研究旨在利用FinFET（翅片场效应晶体管）和碳纳米管场效应晶体管（CNTFET）技术的进展，开发一种延迟控制的可重构ALU。第一个发展是采用多路选择电路的进位输出预测加法器（COPA）和进位输入选择性加法器（CISA）的集成，允许开发具有可控延迟的加法器和减法器。延迟可控重构逻辑单元可以通过集成算法和逻辑功能来实现。基于纳米技术的技术在功率效率和延迟方面优于传统的加法器和减法器。该结果是由Cadence Virtuoso利用18nm finFET技术进步获得的，电源电压在0.8 V至1v之间，温度在-25°C至50°C之间。仿真结果表明，所提出的4位ALU具有良好的稳定性。与现有的4位ALU相比，提出的CNTFET 4位算术逻辑单元（ALU）的功耗降低了30%，延迟时间降低了40%。

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引用次数: 0

An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization 基于动态权重优化的多模糊自适应变压器故障诊断模型

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-19 DOI: 10.1016/j.prime.2025.101048

Kim-Anh Nguyen, Huy Hoang Le, Ba Tu Phung

Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures. Traditional DGA interpretation methods like Duval Triangle, IEC ratio, Roger ratio, Doernenburg ratio and Key Gas are inconsistent and vary in accuracy, especially for multiple fault conditions. We propose an Adaptive Multi-Fuzzy Logic (AMFL) model integrating multiple DGA methods with fuzzy logic and a dynamic weight adjustment mechanism. Unlike existing approaches with fixed weights, this system iteratively evaluates each method's diagnostic performance, identifies multiple fault types, and adjusts weights based on fault prediction accuracy. A feedback-based optimization recalibrates weights after each cycle to ensure optimal solution convergence. The model, implemented in MATLAB/Simulink, is validated against DGA datasets with known error conditions. Results show the AMFL model significantly improves diagnostic accuracy, especially in complex error scenarios, and enhances adaptability to new datasets. Comparative analysis demonstrates the proposed method outperforms traditional fixed weight multi-fuzzy systems in accuracy, consistency, and reliability of error detection. This work provides a robust, flexible diagnostic tool for transformer condition monitoring and supports more accurate asset management decisions.

溶解气体分析是电力变压器早期故障诊断的重要手段。传统的DGA解释方法如Duval三角、IEC比值、Roger比值、Doernenburg比值、Key Gas等存在不一致性和精度差异，特别是在多故障条件下。提出了一种基于模糊逻辑和动态权值调整机制的自适应多模糊逻辑（AMFL）模型。与现有的固定权重方法不同，该系统迭代评估每种方法的诊断性能，识别多种故障类型，并根据故障预测精度调整权重。基于反馈的优化在每个周期后重新校准权重，以确保最优解收敛。在MATLAB/Simulink中实现了该模型，并对已知误差条件下的DGA数据集进行了验证。结果表明，AMFL模型显著提高了诊断准确率，特别是在复杂的错误场景下，并增强了对新数据集的适应性。对比分析表明，该方法在误差检测的准确性、一致性和可靠性方面优于传统的定权多模糊系统。这项工作为变压器状态监测提供了一个强大、灵活的诊断工具，并支持更准确的资产管理决策。

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引用次数: 0

Design and implementation of FinFET and GnrFET based nano arithmetic logic unit 基于FinFET和gnfet的纳米算术逻辑单元的设计与实现

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-18 DOI: 10.1016/j.prime.2025.101050

Samanthapudi Swathi , Nirmal Sharma , S. Neeraja

An Arithmetic Logic Unit (ALU) is a fundamental digital circuit within a central processing unit (CPU) or microprocessor that performs arithmetic and logical operations on binary data. However, the conventional complementary metal oxide semiconductor (CMOS) based ALUs were consumed a greater number of transistors, power, and delay consumptions. So, this work focused on development of nanotechnology based ALU (Nano-ALU) using Fin field effect transistors (FinFET) and graphene nano-ribbon field effect transistors (GnrFET). Initially, the dynamic path auto-configurable joint-adder-subtractor (DPAJAS) is developed by using multiplexer path selectable full adder (MPFA), enhanced full adder with carry prediction (EFOCP) modules. Then, the dynamic path auto-configurable multiplier is (DPAM) is developed by adopting redundant booth encoding (RBE) and dynamic path auto-configurable adder (DPAA) property of DPAJAS, which contains the multiplexer logics to perform fast switching of data. Finally, the Nano-ALU developed by combining the DPAJAS, DPAM modules, and logical operations. The proposed Nano-ALU outperforms the compared traditional ALUs with an average reduction of approximately 15.7 % in Total Energy Consumption (TEC), about 14.6 % in Total Path Delay (TPD), approximately 15.3 % in Carry Out Rise Delay (CORD), roughly 13.2 % in Carry Out Fall Delay (COFD), approximately 11.0 % in Sum Rise Delay (SRD), about 11.0 % in Sum Fall Delay (SFD), and approximately 6.3 % in Average Power Consumption (APC).

算术逻辑单元（ALU）是中央处理单元（CPU）或微处理器内的基本数字电路，用于对二进制数据执行算术和逻辑操作。然而，传统的基于互补金属氧化物半导体（CMOS）的alu消耗了更多的晶体管，功率和延迟消耗。因此，本研究的重点是利用翅片场效应晶体管（FinFET）和石墨烯纳米带场效应晶体管（GnrFET）开发基于纳米技术的ALU （Nano-ALU）。首先，采用多路可选全加法器（MPFA）和带进位预测的增强型全加法器（EFOCP）模块开发了动态路径自配置联合加减法器（DPAJAS）。然后，利用冗余摊位编码（RBE）和DPAJAS的动态路径自配置加法器（DPAA）特性，开发了动态路径自配置乘法器（DPAM），其中包含多路复用逻辑，实现数据的快速交换。最后，将DPAJAS、dpaam模块和逻辑运算相结合，开发了Nano-ALU。所提出的纳米alu优于传统alu，总能耗（TEC）平均降低约15.7%，总路径延迟（TPD）平均降低约14.6%，执行上升延迟（CORD）平均降低约15.3%，执行下降延迟（COFD）平均降低约13.2%，总上升延迟（SRD）平均降低约11.0%，总下降延迟（SFD）平均降低约11.0%，平均功耗（APC）平均降低约6.3%。

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引用次数: 0

Two-stage photovoltaic system with a high-gain fifth-order boost converter 具有高增益五阶升压变换器的两级光伏系统

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-18 DOI: 10.1016/j.prime.2025.101038

Chikezie M. Emeghara, Satish M. Mahajan, Ali Arzani

This paper introduces a novel two-stage PV system comprising a high-gain, efficient fifth-order boost converter. The dynamic modeling of the system components, including the PV array, fifth-order boost converter-based PV-side converter (FOBC-PVSC), grid-side converter (GSC), and grid is analyzed. The system’s dynamic and small-signal analyses are evaluated to determine how FOBC-PVSC input capacitor changes affect the system’s overall performance. The FOBC-PVSC’s eigenvalue and PV input-to-output transfer function analyses are used to determine the stability and allowable range of the DC-stage input capacitor values. The grid-side converter’s eigenvalue analysis evaluates the proposed model’s robustness to grid short-circuit ratio changes and band-pass filter damping ratio variations in grid voltage measurement. The grid-side LCL filter stability analysis is considered and different passive damping models evaluated to address resonance issues. Also, the proposed system’s MPPT and GSC controllers are modeled to obtain the maximum available PV power, boost the voltage to 800 DC volts, and convert the DC output to AC grid-rated value.

本文介绍了一种新型的两级光伏系统，该系统由一个高增益、高效的五阶升压变换器组成。分析了光伏阵列、基于五阶升压变换器的PV侧变换器（FOBC-PVSC）、电网侧变换器（GSC）和电网等系统组件的动态建模。对系统的动态和小信号分析进行评估，以确定FOBC-PVSC输入电容的变化如何影响系统的整体性能。FOBC-PVSC的特征值和PV输入输出传递函数分析用于确定直流级输入电容值的稳定性和允许范围。并网变换器的特征值分析评估了该模型对电网电压测量中电网短路比变化和带通滤波器阻尼比变化的鲁棒性。考虑了电网侧LCL滤波器的稳定性分析，并评估了不同的被动阻尼模型来解决谐振问题。此外，对系统的MPPT和GSC控制器进行建模，以获得最大可用光伏功率，将电压提升到800直流伏，并将直流输出转换为交流电网额定值。

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引用次数: 0

Physics-informed and machine learning-based design optimization of Solid Oxide Fuel Cells 基于物理和机器学习的固体氧化物燃料电池设计优化

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-16 DOI: 10.1016/j.prime.2025.101032

Temitayo O. Olowu, Tyler L. Westover, Micah J. Casteel

This paper presents a two-phase approach towards a multiphysics and constrained multiobjective design optimization of a solid oxide fuel cell (SOFC) system. The first phase of the proposed approach uses a directly coupled multiphysics model (including electrochemistry, thermal, and fluid physics submodels) with a heuristic algorithm to determine the effects of the design variables on the performance of the SOFC system for extensive combinations of the design variables. In the second phase, four machine learning (ML) models are developed for regression purposes. The ML models include ensemble regression (ER), Gaussian process regression (GPR), support vector machine regression (SVMR), and neural network regression (NNR). The ML models are trained using data generated from extensive multiphysics simulations. The trained ML models are subsequently integrated within a constrained multiobjective optimization algorithm to determine optimal values of the design parameters for the SOFC system. The design variables for both phases include the physical geometry of the SOFC, as well as the fuel and air velocities. The optimization objectives include minimizing the temperature gradient, minimizing the cost per unit Watt as well as maximizing the power density of the SOFC. The Pareto Optimal Solutions (POS) obtained using this approach were evaluated to benefit future SOFC design efforts. The results show very good performance of the trained ML models in predicting the proposed objectives from the SOFC. For this application, the NNR and GPR models performed somewhat better than the EF and SVMR models. The NNR’s normalized RMSE values for predicting the temperature gradient, power density and cost per unit watt are 0.0423 °C,

0.007 W / {cm}^{2}

and

$ 0.0126 / W

respectively while that of GPR is predicting the same parameters are 0.0415 °C,

0.0052 {W/cm}^{2}

and

$ 0.013 / W

respectively. The POS solutions present valuable information to SOFC designers regarding trade-offs in optimizing the geometry and operation of SOFC systems.

本文提出了固体氧化物燃料电池（SOFC）系统多物理场约束多目标设计优化的两阶段方法。该方法的第一阶段使用直接耦合的多物理场模型（包括电化学、热学和流体物理子模型），并使用启发式算法来确定设计变量对SOFC系统性能的影响，以实现设计变量的广泛组合。在第二阶段，为回归目的开发了四个机器学习（ML）模型。ML模型包括集成回归（ER）、高斯过程回归（GPR）、支持向量机回归（SVMR）和神经网络回归（NNR）。机器学习模型使用广泛的多物理场模拟生成的数据进行训练。随后，将训练好的ML模型集成到约束多目标优化算法中，以确定SOFC系统设计参数的最优值。这两个阶段的设计变量包括SOFC的物理几何形状，以及燃料和空气速度。优化目标包括最小化温度梯度、最小化单位瓦成本以及最大化SOFC的功率密度。使用该方法获得的Pareto最优解（POS）进行了评估，以使未来的SOFC设计工作受益。结果表明，训练后的机器学习模型在预测SOFC提出的目标方面表现良好。对于这个应用，NNR和GPR模型比EF和SVMR模型表现得更好。NNR预测温度梯度、功率密度和单位瓦成本的归一化RMSE值分别为0.0423°C、0.007W/cm2和$0.0126/W， GPR预测相同参数的归一化RMSE值分别为0.0415°C、0.0052W/cm2和$0.013/W。POS解决方案为SOFC设计人员在优化SOFC系统的几何形状和操作方面提供了有价值的信息。

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引用次数: 0

A high step-up double-dual-boost converter with continuous output current 具有连续输出电流的高升压双升压变换器

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-13 DOI: 10.1016/j.prime.2025.101047

Mohammad Lotfi-Nejad , Amir Abbas Aghajani , Hossein Hajisadeghian , Ali Akbar Moti Birjandi , Ehsan Adib

Although recently proposed double-dual-boost (DDB) DC-DC converters can be used in various applications, they are unable to achieve a higher voltage gain than the conventional DDB converter and typically suffer from a discontinuous output current. This paper presents a non-isolated DDB converter specifically designed to address these limitations, offering continuous output current and high step-up voltage gain. The proposed converter retains the advantages of the conventional DDB converter while employing only two active devices. Moreover, it features low input current ripple, continuous input and output currents, and a voltage gain equal to the combined gains of a quadratic boost converter and a quadratic buck-boost converter. Maintaining non-pulsating input and output currents reduces the risk of damaging renewable energy sources connected to the converter. Therefore, the proposed converter is a suitable option for fuel cell and photovoltaic applications. To demonstrate the benefits of the proposed converter in terms of current continuity, current ripple, the number of active devices, and voltage gain, a detailed comparison with existing DDB converters is provided. Finally, an experimental prototype of the proposed converter is built to validate its performance.

虽然最近提出的双-双升压（DDB） DC-DC变换器可以用于各种应用，但它们无法获得比传统DDB变换器更高的电压增益，并且通常受到输出电流不连续的影响。本文提出了一种非隔离DDB转换器，专门设计用于解决这些限制，提供连续输出电流和高升压增益。该变换器保留了传统DDB变换器的优点，同时仅采用两个有源器件。此外，它具有低输入电流纹波，连续输入和输出电流，电压增益等于二次升压变换器和二次降压-升压变换器的组合增益。保持非脉动输入和输出电流降低了连接到转换器的可再生能源损坏的风险。因此，所提出的转换器是燃料电池和光伏应用的合适选择。为了证明所提出的变换器在电流连续性、电流纹波、有源器件数量和电压增益方面的优势，与现有DDB变换器进行了详细的比较。最后，建立了该变换器的实验样机以验证其性能。

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引用次数: 0

Efficient double-stage control scheme for improving power system frequency stability with electric vehicles 提高电动汽车电力系统频率稳定性的高效双级控制方案

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-13 DOI: 10.1016/j.prime.2025.101046

Morsy Nour , Gaber Magdy

The increasing integration of intermittent and uncertain renewable energy sources (RESs) poses significant challenges to power system frequency control due to their non-dispatchable nature and the resulting decline in system inertia. Addressing these challenges requires the development of advanced controller designs, effective optimization techniques for parameter tuning, and the inclusion of flexible resources such as electric vehicles (EVs). Recent studies have explored a wide range of control strategies, including integer-order, fractional-order, cascade, and advanced controllers, to improve frequency stability in RES-dominated systems. While integer-order controllers are simple and well-established, they often fail to meet the dynamic requirements of modern grids. Conversely, advanced controllers offer superior performance but are complex and harder to implement. Therefore, this paper proposes a developed double-stage control scheme for the frequency control of real renewable power grids, e.g., Egyptian power system (EPS) under the 2035 scenario, which features a significant proportion of RESs. The proposed control design combines a tilt fractional-order integral derivative (TFOID) controller with a proportional-integral (PI) controller. The controller provides very good performance and a good level of simplicity, making it easy to tune. The suggested TFOID-PI controller’s parameters are tuned using the Artificial Hummingbird Algorithm (AHA), a state-of-the-art powerful heuristic algorithm. Moreover, the study examines the effectiveness of EVs sharing in frequency control. The analysis considers a scenario in which EVs function in the grid to vehicle (G2V) only as a controllable load and another scenario where they function in G2V and vehicle to grid. The study findings prove the superiority of the proposed new controller over popular controllers, considering large variations in RESs and loads and system uncertainties. Additionally, it demonstrates the efficacy of EV contribution in frequency control when operating solely in charging mode or in both charging and discharging modes. The findings clarify the ability to satisfy EV owners' needs while providing frequency regulation service by smart charging of EVs operating in G2V mode.

由于间歇性和不确定性可再生能源（RESs）的不可调度特性和由此导致的系统惯性下降，对电力系统频率控制提出了重大挑战。解决这些挑战需要开发先进的控制器设计，有效的参数调整优化技术，以及包括电动汽车（ev）等灵活资源。最近的研究已经探索了广泛的控制策略，包括整阶、分数阶、级联和高级控制器，以提高res主导系统的频率稳定性。虽然整阶控制器是简单和完善的，但它们往往不能满足现代电网的动态要求。相反，高级控制器提供卓越的性能，但复杂且难以实现。因此，本文提出了一种成熟的双级控制方案，用于2035情景下实际可再生电网的频率控制，例如埃及电力系统（EPS），其中RESs占比较大。所提出的控制设计结合了倾斜分数阶积分导数（TFOID）控制器和比例积分（PI）控制器。该控制器提供了非常好的性能和良好的简单性，使其易于调整。建议的TFOID-PI控制器的参数使用人工蜂鸟算法（AHA）进行调整，这是一种最先进的强大启发式算法。此外，该研究还检验了电动汽车共享频率控制的有效性。该分析考虑了电动汽车仅作为可控负载在电网到车辆（G2V）中发挥作用的场景，以及电动汽车在G2V和车辆到电网中发挥作用的场景。研究结果证明了所提出的新控制器比常用控制器的优越性，考虑到RESs和负载以及系统不确定性的大变化。此外，它还证明了纯充电模式或充放电模式下电动汽车在频率控制方面的有效性。研究结果阐明了在G2V模式下运行的电动汽车通过智能充电提供频率调节服务的同时满足电动汽车车主需求的能力。

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引用次数: 0

Design of half-select free 12T SRAM cell with high performance for health care applications 设计半选择自由的高性能12T SRAM单元，用于医疗保健应用

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-12 DOI: 10.1016/j.prime.2025.101045

Manoj Kumar R․ , Sai Susmitha Naidu N․

Static Random Access Memory (SRAM) plays a crucial role in applications related to health like Body Area Networks (BANs) which require increased battery lives for the sensor nodes in BANs. So, a new 12T SRAM cell has been proposed which ensures enhanced write ability, read dynamic noise margin, low write delay, write power and read power. The Proposed 12T (P12T) SRAM cell also operates in bit interleaving architecture which eliminates the half-select issue. All the metrics of SRAM cell designs were evaluated after post-layout simulation using Cadence Virtuoso in 45 nm technology node. P12T SRAM cell has 8.04 ×, 2.14 × higher write ability than the 9T and SEPPN10T SRAM cell at 0.6 V V_DD at worst process corner SF. Write 1 delay of P12T SRAM is lower by 2.05 ×, 2.98 ×, 5.24 ×, 6.72 × than the 9T/SEPPN10T/FDWA10T/12T SRAM cells respectively at 0.6 V, worst process corner SS. P12T SRAM cell exhibits lower Write 0 power by 4.27 ×, 2.04 × than 9T and SEPPN10T SRAM cell at 0.6 V V_DD, SS corner. The read delay for P12T SRAM is reduced by 1.78 ×, 3.1 × compared to 9T and SEPPN10T SRAM cell at worst process corner SS,0.6 V cell. The illustration of half-select removal in the P12T SRAM cell is shown using 1000 Monte-Carlo simulations.

静态随机存取存储器（SRAM）在身体区域网络（ban）等与健康相关的应用中起着至关重要的作用，这些应用需要延长ban中传感器节点的电池寿命。因此，本文提出了一种新的12T SRAM单元，该单元具有较强的写入能力、较低的读动态噪声余量、较低的写延迟、较低的写功耗和较低的读功耗。提出的12T (P12T) SRAM单元也在位交错结构中工作，从而消除了半选择问题。采用Cadence Virtuoso在45纳米技术节点上进行布局后仿真，评估SRAM单元设计的所有指标。在0.6 V VDD时，P12T SRAM单元的写入能力比9T和SEPPN10T SRAM单元高8.04倍、2.14倍。在0.6 V时，P12T SRAM的Write 1延迟分别比9T/SEPPN10T/FDWA10T/12T SRAM电池低2.05倍、2.98倍、5.24倍、6.72倍，在0.6 V VDD， SS角时，P12T SRAM电池比9T和SEPPN10T SRAM电池低4.27倍、2.04倍。与9T和SEPPN10T SRAM电池相比，P12T SRAM电池在最差工艺角SS （0.6 V电池）下的读延迟分别降低了1.78倍和3.1倍。使用1000个蒙特卡罗模拟显示了P12T SRAM单元中半选择去除的说明。

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引用次数: 0

“Power quality event detection and classification using wavelet-alienation based scheme” 基于小波异化的电能质量事件检测与分类

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-06-10 DOI: 10.1016/j.prime.2025.101040

Bhuvnesh Rathore , Anil Kumar Raghav , Ravi Soni

In recent times, the issue of power quality has garnered significant attention from both utility providers and consumers, aiming to pinpoint the origins of disruptions to safeguard equipment. Power quality problems (PQ) encompass a range of abnormalities in voltage, current, or frequency, leading to equipment malfunction or failure. This research introduces a novel Wavelet-Alienation-algorithm designed for the detection and classification of Power Quality (PQ) events. The algorithm is engineered to swiftly detect PQ events within a quarter cycle time-frame, leveraging alienation coefficients derived from approximate wavelet coefficients. The variation of these alienation coefficients over time indicates PQ events, as their values surpass a predefined threshold, whereas they remain significantly lower in the case of pure sine waves. This methodology has been rigorously tested across various PQ events, including single-stage disturbances such as swells, sags, notches, harmonics, flickers, interruptions, and impulsive transients, as well as complex PQ disturbances formed by combinations of these simpler events. Encouragingly, the algorithm consistently demonstrates satisfactory performance in accurately detecting these events.

最近，电力质量问题引起了公用事业供应商和消费者的极大关注，旨在查明中断的根源，以保护设备。电能质量问题（PQ）包括一系列电压、电流或频率异常，导致设备故障或失效。提出了一种用于电能质量事件检测与分类的小波疏离算法。该算法被设计成在四分之一周期的时间框架内快速检测PQ事件，利用从近似小波系数导出的异化系数。随着时间的推移，这些异化系数的变化表明PQ事件，因为它们的值超过了预定义的阈值，而在纯正弦波的情况下，它们仍然明显较低。该方法已经过各种PQ事件的严格测试，包括单级干扰，如膨胀、凹陷、陷波、谐波、闪烁、中断和脉冲瞬态，以及由这些简单事件组合形成的复杂PQ干扰。令人鼓舞的是，该算法在准确检测这些事件方面始终表现出令人满意的性能。

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引用次数: 0