The owner of the wrecked Fukushima No. 1 power plant is trying this week to touch melted fuel at the bottom of the plant for the first time since the disaster almost eight years ago, a tiny but key step toward retrieving the radioactive material amid a ¥21.5 trillion ($195 billion) cleanup effort. Tokyo Electric Power Co. Holdings Inc. will on Wednesday insert a robot developed by Toshiba Corp. to make contact with material believed to contain melted fuel inside the containment vessel of the unit 2 reactor, one of three units that melted down after the March 2011 earthquake and tsunami. "We plan to confirm if we can move or lift the debris or if it crumbles," Joji Hara, a spokesman for Tepco said by phone Friday. Tepco doesn't plan to collect samples during the survey. The country is seeking to clean up the Fukushima disaster, the world's worst atomic accident since Chernobyl, which prompted a mass shutdown of its reactors.
In enterprise AI, C3 (formerly C3 IoT) is amassing an impressive and seemingly unmatched record, one that the company has extended with its latest win, the expansion of a five-year engagement with Enel, Europe's largest power utility, to encompass nearly 50 million smart meters in homes and businesses. This follows C3 contract wins last year with Royal Dutch Shell, the U.S. Air Force and 3M, along with partnerships with AWS, Google Cloud and Microsoft Azure. In the large utilities space, other customers include Con Edison, covering the New York metropolitan area, and Engie, one of the biggest utilities in France. The new contract (dollar amount not disclosed) expands on C3's existing, five-year engagement for Enel in Italy involving 32 million smart meters. C3 will provide the €74.6 billion utility with AI and smart grid analytics applications enabling Enel to deploy the Unified Virtual Data Lake, integrating data across its retail, distribution, trading, renewables and conventional generation businesses.
YOKOHAMA - Toshiba Corp. unveiled a remote-controlled robot with tongs on Monday that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and grip chunks of highly radioactive melted fuel. The device is designed to slide down an extendable 11-meter (36-foot) long pipe and touch melted fuel inside reactor 2's primary containment vessel. The reactor was built by Toshiba and GE. An earlier probe carrying a camera captured images of pieces of melted fuel in the reactor last year, and robotic probes in the two other reactors have detected traces of damaged fuel, but the exact location, contents and other details remain largely unknown. Toshiba's energy systems unit said experiments with the new probe planned in February are key to determining the proper equipment and technologies needed to remove the fuel debris, the most challenging part of the decommissioning process expected to take decades.
Toshiba unveiled a remote-controlled robot with tongs on Monday that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and grip chunks of highly radioactive melted fuel. The device is designed to slide down an extendable 11-meter (36-foot) long pipe and touch melted fuel inside the Unit 2 reactor's primary containment vessel. The reactor was built by Toshiba and GE. An earlier probe carrying a camera captured images of pieces of melted fuel in the reactor last year, and robotic probes in the two other reactors have detected traces of damaged fuel, but the exact location, contents and other details remain largely unknown. Toshiba unveiled the device carrying tongs that comes out of a long telescopic pipe for an internal probe in one of three damaged reactor chambers at Japan's tsunami-hit Fukushima nuclear plant - this time to touch chunks of melted fuel Toshiba's energy systems unit said experiments with the new probe planned in February are key to determining the proper equipment and technologies needed to remove the fuel debris, the most challenging part of the decommissioning process expected to take decades.
A new training model developed by MIT and Microsoft can help identify and correct an autonomous car's AI when it makes potentially deadly mistakes. Since their introduction several years ago, autonomous vehicles have slowly been making their way onto the road in greater and greater numbers, but the public remains wary of them despite the undeniable safety advantages they offer the public. Autonomous vehicle companies are fully aware of the public's skepticism. Every crash makes it more difficult to gain public trust and the fear is that if companies do not manage the autonomous vehicle roll-out properly, the backlash might close the door on self-driving car technology the way the Three Mile Island accident shut down the growth of nuclear power plants in the United States in the 1970's. Making autonomous vehicles safer than they already are means identifying those cases that programmers might never have thought of and that the AI will fail to respond to appropriately, but that a human driver will understand intuitively as a potentially dangerous situation.
What does a nuclear power plant disaster have to do with machine learning? The safety plan for Fukushima Daiichi nuclear power plant was designed using the historical data for past 400 years. The structural engineers designed the plant to withstand an earthquake of 8.6 intensity on Richter scale and a tsunami as high as 5.7 meters. These threshold numbers were decided using predictive modeling. So, they had the data for earthquakes(intensity and annual frequency) in last 400 years and they were looking for a model that can help predict the earthquakes in future.
A system under development at Purdue University uses artificial intelligence to detect cracks captured in videos of nuclear reactors and represents a future inspection technology to help reduce accidents and maintenance costs. "Regular inspection of nuclear power plant components is important to guarantee safe operations," said Mohammad R. Jahanshahi, an assistant professor in Purdue's Lyles School of Civil Engineering. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors." Complicating the inspection process is that nuclear reactors are submerged in water to maintain cooling. Consequently, direct manual inspection of a reactor's components is not feasible due to high temperatures and radiation hazards.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.
Abstract--In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used to failure prediction. However, this mathematical model cannot incorporate asset condition data such as inspection or testing results. As a result, the prediction cannot be very specific and accurate for individual assets. To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset statuses. Furthermore, an index called average aging rate is defined to quantify, track and estimate the relationship between asset physical age and conditional age. This approach was applied to an urban distribution system in West Canada to predict medium-voltage cable failures. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates superior performance and practicality for predicting asset class failures in power systems. I. INTRODUCTION oday, more and more electric utilities are mandated by regulators to develop cost-effective long-term asset management strategies to reduce overall cost while maintaining system reliability [1-2]. Sophisticated and optimal asset management strategies can only be established based on the accurate prediction of asset failures in the future.