Utilities


Robots in Depth with Andrew Graham

Robohub

In this episode of Robots in Depth, Per Sjöborg speaks with Andrew Graham about snake arm robots that can get into impossible locations and do things no other system can. Andrew tells the story about starting OC Robotics as a way to ground his robotics development efforts in a customer need. He felt that making something useful gave a great direction to his projects. We also hear about some of the unique properties of snake arm robots: – They can fit in any space that the tip of the robot can get through – They can operate in very tight locations as they are flexible all along and therefore do not sweep large areas to move – They are easy to seal up so that they don't interact with the environment they operate in – They are set up in two parts where the part exposed to the environment and to risk is the cheaper part Andrew then shares some interesting insights from the many projects he has worked on, from fish processing and suit making to bomb disposal and servicing of nuclear power plants. This interview was recorded in 2015.


Residential Transformer Overloading Risk Assessment Using Clustering Analysis

arXiv.org Artificial Intelligence

Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected failures and life losses that are often caused by transformer overloading. Within the typical power asset portfolio, the residential transformer asset is often large in population, having lowest reliability design, lacking transformer loading data and susceptible to customer loading behaviors such as adoption of distributed energy resources and electric vehicles. On the bright side, the availability of more residential operation data along with the advancement of data analytics techniques have provided a new path to further our understanding of local residential transformer overloading risks statistically. This research developed a new data-driven method to combine clustering analysis and the simulation of transformer temperature rise and insulation life loss to quantitatively and statistically assess the overloading risk of residential transformer population in one area and suggest proper risk management measures according to the assessment results. Case studies from an actual Canadian utility company have been presented and discussed in detail to demonstrate the applicability and usefulness of the proposed method.


Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction

arXiv.org Machine Learning

Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators. While the statistics and machine learning literature offers many proposed methods for electricity price prediction, there is no consensus supporting a single best approach. We test two contrasting machine learning approaches for predicting electricity prices, regression decision trees and recurrent neural networks (RNNs), and compare them to a more traditional ARIMA implementation. We conduct the analysis on a challenging dataset of electricity prices from ERCOT, in Texas, where price fluctuation is especially high. We find that regression decision trees in particular achieves high performance compared to the other methods, suggesting that regression trees should be more carefully considered for electricity price forecasting.


Adversarial Regression for Detecting Attacks in Cyber-Physical Systems

arXiv.org Artificial Intelligence

Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anomalous sensor readings, where each sensor's measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to \emph{stealthy attacks}, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.


No Job for Humans: The Robot Assault on Fukushima

WIRED

The night before the mission, Kenji Matsuzaki could not sleep. For more than a year, Matsuzaki and a team of engineers had been developing their little robot--a bread-loaf-sized, red and white machine equipped with five propellers, a transparent dome, front and rear video cameras, and an array of lights and sensors. Nicknamed Little Sunfish, it was engineered to operate underwater, in total darkness, amid intense radiation. And after three months of testing, training, and fine-tuning, it was deemed ready to fulfill its mission: to find and photograph the melted-down radioactive fuel that had gone missing inside the Fukushima Daiichi nuclear power plant. More than six years had passed since an earthquake and tsunami hammered northeastern Japan and reduced the Fukushima facility to radioactive ruin.


Keeping the balance: How flexible nuclear operation can help add more wind and solar to the grid

MIT News

In the Southwestern United States, the country's sunniest region, sunlight can shine down for up to 14 hours a day. This makes the location ideal for implementing solar energy -- and the perfect test-bed for MIT Energy Initiative (MITEI) researcher Jesse Jenkins and his colleagues at Argonne National Laboratory to model the benefits of pairing renewable resources with more flexible operation of nuclear power plants. They report their findings in a new paper published in Applied Energy. During summer 2015, Jenkins worked as a research fellow with Argonne National Laboratory on two power systems projects: one on the role of energy storage in a low-carbon electricity grid, and the other on the role of nuclear plants. Linking the two projects, he says, is the goal of using new sources of operating flexibility to integrate more renewable resources into the grid.


Artificial intelligence is too powerful to be left to Facebook, Amazon and other tech giants

#artificialintelligence

Facebook CEO Mark Zuckerberg's testimony before Congress made one thing clear: the government needs an Federal Artificial Intelligence Agency. Facebook FB, -0.26% is a canary in the proverbial AI coal mine. AI is going to play an enormous role in our lives and in the global economy. It is the key to self-driving cars, the Amazon AMZN, -0.63% Alexa in your home, autonomous trading desks on Wall Street, innovation in medicine, and cyberwar defenses. Technology is rarely good nor evil -- it's all in how humans use it.


Robot designed for faster, safer pipe cleanup at U.S. Cold War-era uranium plant

The Japan Times

COLUMBUS, OHIO – Ohio crews cleaning up a massive former Cold War-era uranium enrichment plant in Ohio plan this summer to deploy a high-tech helper: an autonomous, radiation-measuring robot that will roll through kilometers of large overhead pipes to spot potentially hazardous residual uranium. Officials say it's safer, more accurate and tremendously faster than having workers take external measurements to identify which pipes need to be removed and decontaminated at the Portsmouth Gaseous Diffusion Plant in Piketon. They say it could save taxpayers tens of millions of dollars on cleanups of that site and one near Paducah, Kentucky, which for decades enriched uranium for nuclear reactors and weapons. The RadPiper robot was developed at Carnegie Mellon University in Pittsburgh for the U.S. Department of Energy, which envisions using similar technology at other nuclear complexes such as the Savannah River Site in Aiken, South Carolina, and the Hanford Site in Richland, Washington. Roboticist William "Red" Whittaker, who began his career developing robots to help clean up the Three Mile Island nuclear power accident and now directs Carnegie Mellon's Field Robotics Center, said technology like RadPiper could transform key tasks in cleaning up the country's nuclear legacy.


Japan looks to use drones for disaster mitigation

The Japan Times

SENDAI – Municipalities and private firms are hoping robots and drones will be able to help with future disaster recovery efforts -- an initiative that incorporates lessons learned from the 2011 Great East Japan Earthquake -- by sending out warnings, gauging damage and accessing places were people cannot. To that end, the Sendai Municipal Government is testing a speaker-equipped drone for sending evacuation warnings during flight. Drones are quieter than helicopters, meaning messages would be easier for those on the ground to hear, city officials said. In such a system, the drone would automatically take flight after receiving a warning from the country's J-Alert early warning system and would issue evacuation messages to local residents. In the 2011 disaster, two city government workers and three volunteer fire department rescuers were killed in the tsunami while warning local residents to evacuate.


Creative Uses Of Drone By Europe's Utility Companies

#artificialintelligence

The number of utility companies in the United States is creating value with drone technology. On the other hand, Europe has also been able to use the UAV's in faster, cheaper and safer completion of the project. One of the companies named'ENGIE' used the drone technology to inspect vital components in power plants. Innovation is the major aim of the ENGIE's development. With the usage of drones in the inspection the risk have been reduced plus the work completion have become much faster.