Срочная публикация научной статьи
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Rakhmonov Ikromjon Usmonovich
DSc, Professor, Tashkent State Technical University,
Tashkent, Republic of Uzbekistan
Kurbonov Nurbek Nurullo ugli
PhD, Associate Professo, Tashkent State Technical University,
Tashkent, Republic of Uzbekistan
Bazarbayeva Aksungul Hamid kizi
Assistant,
Karakalpak State University named after Berdak
Abstract
Neural networks have emerged as transformative tools in electrical grid management, addressing the increasing complexities driven by renewable energy integration, fluctuating demand, and real-time data requirements. By leveraging their ability to analyze vast datasets and capture nonlinear patterns, neural networks improve load forecasting, fault detection, voltage stability, and renewable energy integration. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks enable precise load predictions, enhancing grid planning and reliability. Convolutional neural networks (CNNs) classify fault types, reducing downtime and maintaining stability. Additionally, neural networks support voltage stability assessment, preventing blackouts and ensuring seamless operations, while reinforcement learning optimizes renewable energy integration, promoting cleaner energy transitions. This paper highlights the role of neural networks in modernizing power systems, emphasizing their contributions to resilience, efficiency, and cost-effectiveness. Despite challenges, continued advancements demonstrate their potential to revolutionize grid management, supporting a sustainable and reliable energy future.
Аннотация
Нейронные сети стали трансформационным инструментом в управлении электрическими сетями, решая возрастающие сложности, вызванные интеграцией возобновляемых источников энергии, колебаниями спроса и необходимостью обработки данных в реальном времени. Используя способность анализировать большие объемы данных и выявлять нелинейные закономерности, нейронные сети улучшают прогнозирование нагрузки, обнаружение неисправностей, устойчивость напряжения и интеграцию возобновляемых источников энергии. Рекуррентные нейронные сети (RNN) и сети долгой краткосрочной памяти (LSTM) обеспечивают точное прогнозирование нагрузки, что повышает надежность и эффективность планирования сети. Сверточные нейронные сети (CNN) классифицируют типы неисправностей, сокращая время простоя и поддерживая стабильность системы. Кроме того, нейронные сети способствуют оценке устойчивости напряжения, предотвращая сбои и обеспечивая бесперебойную работу, а методы обучения с подкреплением оптимизируют интеграцию возобновляемой энергии, способствуя переходу к чистым источникам энергии. В статье подчеркивается роль нейронных сетей в модернизации энергосистем, акцентируя их вклад в повышение устойчивости, эффективности и экономичности. Несмотря на существующие вызовы, постоянное развитие демонстрирует их потенциал в революционизации управления электрическими сетями и поддержке устойчивого энергетического будущего.
Key words: Neural networks, electrical grids, power systems, load forecasting, fault detection, voltage stability, renewable energy integration, grid optimization.
Ключевые слова: Нейронные сети, электрические сети, энергетические системы, прогнозирование нагрузки, обнаружение неисправностей, устойчивость напряжения, интеграция возобновляемых источников энергии, оптимизация сетей.
Introduction.
The rapid evolution of power systems, driven by renewable energy integration, fluctuating demand, and real-time data needs, has made electrical grid management increasingly complex. Traditional optimization methods struggle with the data scale and complexity, paving the way for neural networks. Inspired by the human brain, neural networks excel at predictive modeling, fault detection, load forecasting, and grid optimization, enhancing grid efficiency, stability, and resilience [1,2]. This article highlights how neural networks can improve reliability, reduce costs, and support renewable integration, demonstrating their transformative role in modernizing power systems.
1. Load Forecasting Using Neural Networks. Accurate load forecasting is essential for the effective management of electrical grids. Neural networks can capture complex patterns in historical load data, allowing for precise short-term and long-term forecasts. A typical approach involves using a recurrent neural network (RNN) or long short-term memory (LSTM) network to process time-series data. Given historical load data for time
, the neural network aims to predict
using a function
modeled by the network:
[3].
2. Fault Detection and Classification. Fault detection is crucial to maintain grid stability and reduce downtime. Neural networks, especially convolutional neural networks (CNNs), can be trained to classify types of faults based on sensor data or signal patterns. Let be the input vector representing electrical parameters (e.g., voltage, current) during an event (Fig. 1). The network learns to classify the fault type
Figure 1. Fault Classification Accuracy
as:
where is the mapping function learned by the CNN.
Softmax Function for Fault Classification. The network’s output layer typically uses the softmax function for multiclass classification:
where is the logit for class
, and
is the total number of fault classes.
A confusion matrix or a line graph showing the model’s accuracy over multiple fault types could demonstrate classification performance.
3. Voltage Stability Assessment and Control Using Neural Networks
Voltage stability is critical for preventing blackouts and ensuring continuous power supply. Neural networks can enhance voltage stability assessment by predicting instability events and providing control measures in real-time [4]. Feedforward neural networks (FNNs) and deep belief networks (DBNs) are commonly used for voltage stability analysis due to their ability to map nonlinear relationships in large datasets. Let represent the voltage at time ttt. The neural network models a function
that forecasts potential instability based on historical voltage levels. Through continual learning and adaptation, the neural network can issue alerts or corrective actions to maintain stability.
Integrating renewable energy sources like solar and wind into the grid requires addressing their variability, heavily influenced by weather conditions. Neural networks, such as recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, are effective for forecasting renewable energy output ( ) at time
based on weather data (
) and historical energy outputs (
). The forecasted output is given by
, where
represents the neural network and
its learned parameters. This prediction enables proactive management of the grid by estimating renewable generation accurately and in advance.
Reinforcement learning (RL) further enhances grid stability by optimizing a control policy , where
represents grid actions (e.g., adjusting storage or fossil fuel generation), and
represents the grid state, including forecasted renewable output
and demand
. The RL agent maximizes cumulative rewards
, balancing clean energy usage and grid stability. The agent ensures the balancing constraint
, dynamically adjusting grid parameters to minimize fossil fuel reliance and optimize energy distribution. This integration of neural networks and RL promotes efficient, sustainable, and reliable grid management.
Neural networks offer powerful tools for optimizing power systems through applications like load forecasting, fault detection, voltage stability assessment, and renewable energy integration. For load forecasting, networks such as RNNs and LSTMs analyze historical load data to make precise predictions, aiding effective grid management. In fault detection, CNNs classify fault types based on electrical parameters like voltage and current, using functions learned from training data to map input signals to fault classifications. The softmax function further enables multiclass classification, enhancing grid reliability by accurately identifying fault types. These applications underscore neural networks’ potential to improve grid stability, efficiency, and responsiveness. Through case studies and practical applications, it is evident that neural networks enhance grid resilience, reduce operational costs, and support clean energy transitions. Although challenges remain, ongoing research and advancements continue to expand the potential of neural networks in addressing the complexities of modern power systems.
References:
1. Owerko, D., Gama, F., & Ribeiro, A. (2022). Unsupervised Optimal Power Flow Using Graph Neural Networks. arXiv preprint arXiv:2210.09277.
2. Hansen, J. B., Anfinsen, S. N., & Bianchi, F. M. (2021). Power Flow Balancing with Decentralized Graph Neural Networks. arXiv preprint arXiv:2111.02169
3. Singh, M. K., Gupta, S., Kekatos, V., Cavraro, G., & Bernstein, A. (2020). Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks. arXiv preprint arXiv:2007.03728
4. Ф. Хошимов, И.У. Рахмонов. Методы расчета прогнозных значений норм удельного электропотребления на предприятиях с меняющейся величиной потребляемой мощности // Universum: технические науки : электрон. научн. журн. 2021. 10(91). URL: https://7universum.com/ru/tech/ archive/item/12384