Purpose

The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.

Design/methodology/approach

This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.

Findings

The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.

Originality/value

What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

中文翻译：

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用于增强发电系统可靠性预测的机器学习驱动的支持向量回归模型

目的

本文的目的是通过准确评估电力系统的可靠性来实现发电可靠性的高精度预测。本研究使用RTS-79可靠性测试系统来衡量该方法的有效性，并使用平均绝对百分比误差作为性能指标。准确的可靠性预测可以为与系统设计、扩展和维护相关的关键决策提供信息，使这项研究与电力系统规划和管理相关。

设计/方法论/途径

本文提出了一种使用基于径向基核函数的支持向量回归方法来准确评估电力系统可靠性的新方法。该方法选择相关的系统功能，并使用分析单元附加算法计算负载预期损失 (LOLE) 和预期未供应能量 (EENS)。所提出的方法在两种情况下进行评估，变化应用于负载需求方或发电系统和负载曲线。

发现

该方法可以高精度预测 LOLE 和 EENS，尤其是在第一种情况下。结果证明了该方法在预测发电可靠性方面的有效性。准确的可靠性预测可以为与系统设计、扩展和维护相关的关键决策提供信息。因此，本研究的结果对电力系统规划和管理具有重要意义。

原创性/价值

这种方法的独特之处在于从电力系统的发电侧和负载侧提取了多个特征，代表了对该领域的独特贡献。