Weather, climate, and hydrologic forecasting for the US Southwest: A survey

As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

Temperature and voltage measurement for field test using an Aging-Tolerant monitor

Measuring temperature and voltage (T&V) in a current VLSI is very important in guaranteeing its reliability, because a large variation of temperature or voltage in field will reduce a delay margin and makes the chip behavior unreliable. This paper proposes a novel method of T&V measurement, which can be used for variety of applications, such as field test, online test, or hot-spot monitoring. The method counts frequencies of more than one ring oscillator (RO), which composes an aging-tolerant monitor. Then, the T&V are derived from the frequencies using a multiple regression analysis. To improve the accuracy of measurement, three techniques of an optimal selection of RO types, their calibration, and hierarchical calculation are newly introduced. In order to make sure the proposed method, circuit simulation in 180-, 90-, and 45-nm CMOS technologies is performed. In the 180-nm CMOS technology, the temperature accuracy is within 0.99 °C, and the voltage accuracy is within 4.17 mV. Furthermore, some experimental results using fabricated test chips with 180-nm CMOS technology confirm its feasibility

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

DESIGN OF PMU BASED REAL TIME FUZZY LOGIC SVC DAMPING CONTROLLER TO ENHANCE INTER- AREA OSCILLATION DAMPING

Inter-area oscillation has been identified as a significant problem in the utility systems due to the damages that it may cause as well as the limitation introduced to power transfer capability. A contemporary solution to this issue is by adding power system stabilizer (PSS) to the generator's automatic voltage regulator (AVR). Although nowadays most of the generators are equipped with conventional PSSs, their effects are only noticed on the damping of local oscillations and they do not contribute effectively on damping the inter-area oscillations. Adding auxiliary signals (stabilizing signals) to Flexible AC Transmission System (FACTS) device such as Static VAr Compensator (SVC)&Static Synchronous Compensator (STATCOM) would help in extending the power transfer capability and enhancing the voltage. The stabilizing signals can be derived from damping controller. In this thesis, a Phasor Measurement Unit (PMU) based real-time, Hardware in the Loop, fuzzy logic shunt FACTS controller is proposed to ensure a satisfactory damping of inter-area oscillations which will enhance system stability and increase power transfer capability. The concerned power system has been modeled using Real-Time Digital Simulator (RTDS), where the designed Hardware-in-the-loop damping controller was tested for the sake of evaluating the effectiveness of the proposed controller in enhancing the damping of inter-area oscillations. Time-domain simulations results have shown that the designed Fuzzy damping controller enhance the damping of inter-area oscillations of interconnected power system. This study is aimed to analyze the potential applications of PMU in the interconnected power systems of GCC smart power grid. These systems are expected to face a stability problem of the inter-area mode of oscillations due to the weak tie-lines that connect the systems

Real-Time Machine Learning Models To Detect Cyber And Physical Anomalies In Power Systems

A Smart Grid is a cyber-physical system (CPS) that tightly integrates computation and networking with physical processes to provide reliable two-way communication between electricity companies and customers. However, the grid availability and integrity are constantly threatened by both physical faults and cyber-attacks which may have a detrimental socio-economic impact. The frequency of the faults and attacks is increasing every year due to the extreme weather events and strong reliance on the open internet architecture that is vulnerable to cyber-attacks. In May 2021, for instance, Colonial Pipeline, one of the largest pipeline operators in the U.S., transports refined gasoline and jet fuel from Texas up the East Coast to New York was forced to shut down after being attacked by ransomware, causing prices to rise at gasoline pumps across the country. Enhancing situational awareness within the grid can alleviate these risks and avoid their adverse consequences. As part of this process, the phasor measurement units (PMU) are among the suitable assets since they collect time-synchronized measurements of grid status (30-120 samples/s), enabling the operators to react rapidly to potential anomalies. However, it is still challenging to process and analyze the open-ended source of PMU data as there are more than 2500 PMU distributed across the U.S. and Canada, where each of which generates more than 1.5 TB/month of streamed data. Further, the offline machine learning algorithms cannot be used in this scenario, as they require loading and scanning the entire dataset before processing. The ultimate objective of this dissertation is to develop early detection of cyber and physical anomalies in a real-time streaming environment setting by mining multi-variate large-scale synchrophasor data. To accomplish this objective, we start by investigating the cyber and physical anomalies, analyzing their impact, and critically reviewing the current detection approaches. Then, multiple machine learning models were designed to identify physical and cyber anomalies; the first one is an artificial neural network-based approach for detecting the False Data Injection (FDI) attack. This attack was specifically selected as it poses a serious risk to the integrity and availability of the grid; Secondly, we extend this approach by developing a Random Forest Regressor-based model which not only detects anomalies, but also identifies their location and duration; Lastly, we develop a real-time hoeffding tree-based model for detecting anomalies in steaming networks, and explicitly handling concept drifts. These models have been tested and the experimental results confirmed their superiority over the state-of-the-art models in terms of detection accuracy, false-positive rate, and processing time, making them potential candidates for strengthening the grid\u27s security