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摘要: 電力系統預測主要包括負荷預測、出力預測以及健康狀態預測等。通過負荷預測，可以優化電力生產規劃，從而更好地實現電能的精細化分配；通過出力預測，可以有效提升新能源電力消納能力，實現電能的充分及合理利用；通過電力設備健康狀態預測，可以及時發現設備運行隱患，從而進一步保障電力系統平穩安全運行。深度學習憑借其卓越的特征分析和預測能力，被廣泛應用于電力系統運行及維護。本文首先歸納介紹了電力系統預測深度學習模型的特點、適用場景；其次，梳理了深度學習在面向民用及工業場景負荷預測、光伏及風電出力預測、機械及非機械設備健康狀態預測中的應用前沿；最后，對深度學習在電力系統預測中所面臨的關鍵問題、發展趨勢進行了總結和展望。Abstract: Power system is one of the largest and complex artificial engineering in the modern society. With the development of intelligence, digitization and long-distance technology, a large number of multi-source, multi-state and heterogeneous operational data have emerged. As a new trend direction of machine learning, deep learning has shown potential in data feature extraction and pattern recognition. Because of its excellent ability in data analysis and prediction, it is widely used in power system, which has a significant impact on optimizing power production planning, improving power production efficiency and energy utilization, and ensuring the smooth operation of the system influence. Based on massive quantities of data and by means of deep learning, it can better fit the nonlinear relationship between the factors affecting the subsequent operational state of the system, so as to further improve the prediction accuracy. Power system prediction includes load forecasting, new energy power prediction and state-of-health prediction. Power production planning can be optimized using load forecasting; thus, electrical energy can be finely dispatched. The capacity of new energy power consumption is improved through power prediction to reasonably use electrical energy. Potential equipment hazards can be timely found using power equipment health state prediction, thereby ensuring safe and smooth operation. First, in this paper, the characteristics and applicable scenarios of typical deep learning models are introduced, among them, deep belief network and stacked auto encoder belong to stack structure, so the structure is flexible and easy to expand, which is suitable for the modeling and feature extraction of unrelated data type; convolutional neural network shares convolution kernel internally to reduce the number of network parameters and is good at processing high-dimensional data type; recurrent neural network has feedforward and feedback connections, so it is suitable for processing sequence data with pre and post dependence. Second, the application frontiers of predictive power systems based on deep learning are reviewed, which include civil and industrial scenarios, photovoltaic and wind power, mechanical and non-mechanical equipment health state prediction. Finally, facing the challenges of power system in energy efficient allocation, high proportion of new energy power consumption, highly stable operation of power equipment and so on, the key problems and future development trends are presented.
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Key words:
- power system /
- deep learning /
- load forecasting /
- new energy power prediction /
- health state prediction
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圖 1 基于雙向LSTM-GRU的居民區用電負荷預測
Figure 1. Residential area load forecasting based on bidirectional LSTM-GRU
圖 2 基于CNN和頻域分解的光伏出力預測
Figure 2. Photovoltaic power forecasting based on CNN and frequency-domain decomposition
圖 3 基于DBN和奇異譜分析的風電出力預測
Figure 3. Wind power forecasting based on DBN and singular spectrum analysis
圖 4 基于SDA的發電機軸承健康階段劃分及健康狀態預測
Figure 4. Health stage segment and health state prediction of generator bearing based on SDA
表 1 典型的深度學習預測模型對比
Table 1. Comparison of typical deep learning prediction models
Model Main feature Apply data type Typical application scenario DBN Unsupervised learning
No need for large number of label data, and the training difficulty is lowSequence data without correlation before and after
(Time series data)Load forecasting, power prediction, equipment health state prediction CNN Supervised learning
Random initial value, sample data without preprocessingSequence data without correlation before and after,
(Multidimensional data)Load forecasting under multi energy spatiotemporal coupling, power prediction considering spatiotemporal correlation, health state forecasting RNN Supervised learning
Both feedforward and feedback connections are includedSequence data correlated before and after Load forecasting and power prediction under the scenario of severe power fluctuation SAE Unsupervised learning
Asymmetric connection, simple structure, easy to expandSequence data without correlation before and after Power prediction, equipment health state prediction 
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