Data-driven wind power prediction model based on improved generative adversarial network

A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction

The high cost of operation and maintenance (O&M) management has become an important factor hindering the sustainable development of the wind power industry. Performing accurate condition assessment of wind turbine components to optimize the structural design and O&M strategy has become a research trend. However, the random and varying operating conditions of wind turbines make this problem difficult and challenging. A Supervisory Control and Data Acquisition (SCADA) system collects signals that contain a large amount of raw and useful information from critical wind turbine sub-assemblies. Extracting key information from the SCADA data is an economical and effective way for condition assessment. A real-time reliability assessment method of wind turbine components using a Back-Propagation Neural Network (BPNN) and SCADA data is presented in this paper. The normal behavior models are established with the processed SCADA data, and the real-time reliability of wind turbine components are assessed based on the prediction result. For verification, the BPNN-based reliability assessment method is applied to a gearbox with real SCADA data of a 1.5MW onshore wind turbine located along the southeast coast of China. The results show the capability of the proposed model in assessing the reliability of wind turbine components continuously and in real time.

In view of the growing depletion of traditional fossil fuels and their adverse impact on natural environment, wind energy has gained increasing popularity across the globe. Characterized by wide distribution, low cost, and well-rounded technology, it has achieved fast-growing installed capacity in recent years. However, wind power is volatile and random in nature and the power ramping events caused by extreme weather always threaten the safe, stable, and economic operation of the power grid. To address the problems of insufficient sample data and low prediction accuracy in existing ramping prediction methods, a new way of wind power prediction considering ramping events based on Generative Adversarial Network (GAN) is proposed. First of all, the ramping events get identified and separated from the database of historical wind power, and the feature set of historical ramping events is then extracted according to the waveform and meteorological factors. Taking the feature set which integrates similar feature with historical one as the input of GAN, the simulated ramping data are continuously produced through the adversarial training of the generator and discriminator, thus enriching the ramping database. After that, the expanded ramping database can be applied to predict the ramping power through the LSTM model. An experiment based on the wind power dataset in a certain area of northwest China further verifies the effectiveness and superiority of this method compared with traditional ones.

The supervisory control and data acquisition (SCADA) systems monitor and control industrial control systems in many industrial and economic sectors such as water treatment, power plants, railroads, and gas pipelines. The integration of SCADA systems with the internet and corporate enterprise networks for various economical reasons exposes SCADA systems to attacks by hackers who could remotely exploit and gain access to SCADA systems to damage the infrastructure and thereby harming people’s lives. The simplicity of datasets and possible overfitting of models to training data are some of the issues in the previous research. In this paper, we present detecting and classifying malicious command and response packets in a SCADA network by analyzing attribute differences and history of packets using k-means clustering. This study presents a solution to classify SCADA cyber attacks to detect and classify SCADA attacks with high accuracy using a big data framework that comprises of Apache Hadoop and Apache Mahout. Apache Mahout’s random forest classification algorithm is applied on SCADA’s gas pipeline dataset to categorize attacks. When 70% of the data is used for training the classifier, our approach resulted in 5–17% improvement in accuracy for the classification of read response attacks and 2–8% improvement in accuracy for write command attacks with respect to using the original dataset.

SCADA (Supervisory Control and Data Acquisition), these are systems based on real-time processing, control and design for real time infrastructure such as water pumping stations, oil purification plants and rail control centers etc. In the initial structure of SCADA there is no security mechanism that prevents or detects the attacks in SCADA communication network. SCADA technology is not new but was deployed in limited networks; currently these systems have been deployed and accessible by many networks such as LAN/WAN, Mobile networks, Web Browsers and GPRS through internet technology. Using Modern communication facilities SCADA systems are vulnerable from different types of internet attacks that create major security problems for SCADA communication. The current paper proposes cryptography solutions for securing SCADA communication. In first part, we propose Cryptography solution using Advance Encryption Standard (AES) and SHA-1 algorithms to secure each end of SCADA communication by implementing security services such as authentication, integrity, and confidentiality In Second part, we propose hybrid cryptography solution for SCADA and give solution to prevent the major attacks that in- Securing the SCADA communication. Hybrid Cryptography Secure Simulation Model is used to discuss and give solutions for putting security services such as authentication, integrity, confidentiality, and non-repudiation in APDU (Application Protocol Data Unit) and LPDU (Link Protocol Data Unit) within SCADA DNP3 protocol, and Rest of paper provide solutions to overcome/prevent attacks such as Guessing Shared Key, Brute force from Authentication; Eavesdropping, Key Cracking, Man in the Middle from Confidentiality; Frame Injection, Data Replay, Data Deletion from Integrity. We use DNP3Sec as framework. Current paper gives research Directions and methods to more secure real time systems.

In the modern industrial control landscape, Supervisory Control and Data Acquisition (SCADA) systems serve as critical intermediaries in automating and managing complex processes across many industries, including energy, manufacturing, and utilities. While maintaining the availability and efficiency of these systems is essential, their increased integration into networked environments has exposed them to a growing array of cyber threats. As SCADA systems continue to grow and be deployed globally, malicious actors are increasingly target critical infrastructure, making security management progressively more challenging. Our research explores the benefits of conducting forensic investigations focused on SCADA Human-Machine Interface (HMI) systems, emphasizing how centralized equipment analysis can help overcome the unique challenges these systems present. To assist investigators in efficiently collecting system-wide data from SCADA HMI environments, we developed a forensic analysis framework that enables the evaluation of system discrepancies and evidence of potential cyberattacks. Additionally, we present a comprehensive forensic analysis of a SCADA testbed featuring Ignition, a popular SCADA software platform developed by Inductive Automation. Our investigation reveals that SCADA HMI forensics can provide valuable insight into the system's state, allowing for the identification and cross-analysis of critical equipment and operational data across memory, network, and disk. This framework is further supported by the creation of the Ignition Forensic Artifact Carving Tool (IFACT), a SCADA HMI Forensic Analysis Tool designed to assist in extracting artifacts and parsing system information from forensic data sourced from Ignition HMIs. Using IFACT, we achieve accurate insights into device and equipment data in SCADA systems, investigating data persistence in volatile memory and the variability of its lifetime based on the system state.

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

With the large-scale wind integration into power grid, the intermittent and stochastic nature of wind power possess a threat to the safety of power grid, and wind power prediction has become one of the most important solutions to the current grid connection problem. However, the prediction error in point prediction of wind power cannot be ignored, so it is necessary to make probability prediction of wind power and improve reliable information to the power grid dispatch department. In this paper, by combing with the wavelet decomposition technology and the long short-term memory (LSTM) network, an ultra short-term probability model is proposed. Firstly, the original time series are smoothed by wavelet decomposition technology, then the LSTM network prediction model of each sub-series sample is developed. The Gaussian distribution function of the prediction error is obtained by using the maximum likelihood estimation method, and finally the probability interval prediction of the future wind power in four hours could be realized. Finally, by using the data collected from a wind farm in northeast china, a numerical example is presented to illustrate the usefulness of the proposed method, which show that combining wavelet decomposition with deep learning method can improve the accuracy of prediction, improve the interval reliability of probability prediction, and enhance the generalization ability of the model.

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature and the wind direction.

A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks

Supervisory Control and Data Acquisition (SCADA) is used in modern power system for monitoring, controlling, gathering the information, transfer to information back to a central station, carry out necessary analysis and control carry out necessary analysis and control. A SCADA system involves the integrated technologies of computer, communication, electric devices etc. Here the communication media provides the physical medium for transferring, controlling and acquisition of data within SCADA from remote locations. Although wireless communications media become an attractive option as communications networks for SCADA system, compared with wired communications, wireless communications in power system environments presents a number of security and reliability issues. As in SCADA system the communication media plays an important role, to meet objectives of the SCADA systems, different wired and wireless communications media have been applied in modern power system environment. This paper presents a concept about SCADA system using Cognitive Radio based IEEE 802.22 standard for overcoming all existing limitations of SCADA's communication media for future power system where details design, analyses of feasibility and benefits over wired and other wireless systems are also described.

This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction.

Detroit's Remarkable New SCADA SystemThe City of Detroit Water and Sewerage Department (DWSD), the third largest water and sewer utility in the United States, has just completed a new SCADA system of unmatched size in the wastewater and water industry. DWSD's new Supervisory Control and Data Acquisition (SCADA) system replaced the old control systems in the Systems Control Center (SCC), which controls all water distribution and...Author(s)P.J. DadaThomas J. DeLauraSourceProceedings of the Water Environment FederationSubjectSession 39: SCADA, SCADA Applications, and SCADA Integration IDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2008ISSN1938-6478SICI1938-6478(20080101)2008:14L.2896;1-DOI10.2175/193864708788734728Volume / Issue2008 / 14Content sourceWEFTECFirst / last page(s)2896 - 2899Copyright2008Word count149