# Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review

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## Abstract

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## 1. Introduction

#### 1.1. Motivation

- The sensation is the brain’s reflection of various attributes in objective things that directly act on the human sensory organs [14]. Human cognition of objective things starts with sensation. It is the initial detection of complex things and the basis of complex cognitive activities such as perception and behavior. That is similar to the concept of situation detection.
- Based on sensory information, perception processes multiple sensory information in a specific way, interprets the sensory information on individual experience, and taps the deep meaning of sensory information. That is similar to the concept of situation comprehension.
- Based on sensory and perception, behavior refers to human activities after receiving internal and external stimuli. The theory of planned behavior [15] can explain human decision-making behaviors from the perspective of perceptual information processing and predict the future behavioral tendency based on the expectation value theory [16]. That is similar to the concept of situation projection.

- Situation detection. The task of the stage is to detect essential features in the environment. Multi-dimensional data can be collected and completed in this stage. In addition, situation detection is the data basis of situation comprehension and projection.
- Situation comprehension. The essence of the stage is to understand the environment through data analysis. Specifically, the data obtained in the situation detection are integrated, and the connection and potential information between multi-source data are explored.
- Situation projection. The core of situation projection is to achieve the practical application of SA knowledge. Based on the information gained from situation detection and comprehension, this stage can predict the future environmental situation in time.

#### 1.2. Related Work

#### 1.3. Contributions

#### 1.4. Organization

## 2. Description of Situation Awareness for Smart Distribution Networks

#### 2.1. Objectives of Situation Awareness for Smart Distribution Networks

- The primary goal is to achieve real-time or quasi-real-time SA for SDN, which can accurately obtain the critical information of SDN, quickly determine the operating status of the distribution networks, and predict the development trend of SDN at the same time [11]. Based on the historical records of SDN data, SA provides a comprehensive SDN situation to ensure high-quality O&M.
- Observability is a significant technical indicator of SA. High-level SA can provide SDN with a highly visual situation and solve the shortcomings of insufficient measurement devices in the SDN [35].
- SA has a significant contribution to SDN reliability. Specifically, conduct the SDN self-healing technology, detect potential SDN risks, and predict security situations in advance. Finally, a scientific basis for the SDN active defense can be provided [13].
- Through continuous innovation of intelligent algorithms, SA is cultivating SDN self-adaptive capabilities [36]. Based on the information obtained by SA, SDN can independently recognize and improve the situation in an informed way.

#### 2.2. Challenges of Situation Awareness for Smart Distribution Networks

- Situational detection challenges. New measurement technologies such as AMI [37] and phasor measurement units (PMUs) [38] are gradually deployed in SDN. Therefore, the data dimensions collected by SDN scale rapidly, which inevitably increases the computational pressure of SA. Due to the insufficient measurement devices, the collected data are challenging to recognize the poor operating status of the SDN. Therefore, the input data of the SA system are asymmetric, and some missing data are necessary to be accurately completed by calculation. How to comprehensively detect SDN status remains a challenging point in high-quality O&M.
- Situational comprehension challenges. Large-scale DGs lead the traditional dispatch mode to unsuitable. As a result, the phenomenon of reverse power transmission at the distribution network terminals is prominent, and the risk of voltage fluctuations and power loss increases [39]. In addition, different SDN topologies, operation modes, energy types, and automation levels have higher requirements for the compatibility of situational comprehension in different regions. Traditional situation comprehension technology is challenging to adapt to the current SDN. As the decision center of SDN, situation comprehension should assist the high-quality O&M of multi-form SDN. How to accurately understand the operating situation of the SDN is the focus of research.
- Situation projection challenges. Unlike passive distribution networks, SDN has a higher proportion of DGs and electric vehicles (EVs) and more diverse operating modes [40]. The uncertain outputs of DGs and EVs lead to an imbalance between power supply and consumption. Although the SDN flexibility is improved, the RES outputs, three-phase unbalanced load, EV charging, inspection schedule, and stability margin are challenging to determine in the situation projection. Additionally, situation projection for complex scenarios requires sufficient mathematical analysis, computational capability, and robustness capability. How to effectively predict the operational trend of SDN needs to be solved urgently.

## 3. Comprehensive Framework of Situation Awareness

## 4. Critical Technologies of Situation Detection

#### 4.1. Big Data Analytics

#### 4.2. 5G Communication

#### 4.3. Virtual Acquisition

#### 4.4. Optimal Configuration of Measurement

## 5. Critical Technologies of Situation Comprehension

#### 5.1. Uncertain Power Flow Calculation

_{k}is the nodal power of the k

^{th}load node in affine form, g

_{kj}is the admittance of the positive line from the k

^{th}node to the j

^{th}node, U

_{k}is the positive voltage of the k

^{th}node in affine form, U

_{j}is the positive voltage of the j

^{th}node in affine form, and n is the total number of nodes. The interval PFC algorithm provides an essential tool for SDN SA to solve the uncertainties of loads and RES outputs.

_{Load}and Q

_{Load}are the active and reactive load powers, ξ

_{μP}and ξ

_{μQ}are the means of the active power and reactive power, and ξ

_{σP}and ξ

_{σQ}are standard deviations of the active power and reactive power.

^{th}node’s voltage probability distribution:

_{n}is n

^{th}node’s voltage, X

_{n}

_{0}is reference state of n

^{th}node’s voltage, ΔX

_{n}(i) is a variety of n

^{th}node’s voltage, and i is the number of a corresponding expansion sequence group. Simultaneously, the l

^{th}branch flow’s probability distribution can be expressed by the following equation:

_{l}is l

^{th}branch’s power flow, Z

_{l0}is reference state of l

^{th}branch’s power flow, and ΔZ

_{l}(i) is a variety of l

^{th}branch’s power flow. Because of the low demand for the sample size, this method is suitable for SA to analyze the power flow uncertainties of SDN with incomplete measurement information.

#### 5.2. Hybrid State Estimation

_{st-PMU}is the PMU states estimated, μ is the Lagrange multiplier vector, x

_{PMU}and x

_{n}

_{−PMU}indicate the PMU and non-PMU states, R is the covariance matrix, z is a vector consisting the system measurements, vector h(x) includes nonlinear functions which relate the states with the measurements through power flow equations, J(x) is the Jacobian matrix, and c(x) is constraint condition. The condition number, as well as the run time of the HSE method, are significantly better than those of conventional state estimation, which can effectively improve the efficiency of the situation comprehension.

#### 5.3. Reliability Analysis

#### 5.4. Voltage Stability Analysis

_{2}is the receiving end bus voltage and V

_{1}is the sending end bus voltage. δ

_{1}and δ

_{2}are voltage angles at the sending and receiving buses, respectively. The voltage stability indicator includes only the bus voltage and voltage angle, which is suitable for SDN SA with high response speed requirements.

_{0}is an initial operational point of the system and P

_{cri}is the critical point of the system. The novel RATCI assesses the voltage stability by combining DGs and the defined reactive power types, helping SA achieve the optimal penetration rate of the RES while still maintaining voltage security.

#### 5.5. Flexibility Evaluation

#### 5.6. Power Quality Evaluation

_{acc}is determined based on voltage and current of network buses as follows:

_{h}and I

_{h}are acceptable harmonic voltage and current of i

^{th}buses, respectively. If the impedance characteristic is less than the acceptable value, it can be ensured that harmonic voltage limits will be satisfied if harmonic currents are within the standard range.

_{rms}is RMS value of overall voltage, V

_{f_rms}is RMS value of fundamental frequency voltage, I

_{rms}is RMS value of overall current, and I

_{f_rms}is RMS value of fundamental frequency current. Power quality indicators of voltage and frequency fluctuations [104] can be expressed as follows:

_{m}is the measured value of frequency, and f

_{r}is the rated frequency. V

_{a}, V

_{b}, and V

_{c}are post-sag RMS voltages of phases A, B, and C, respectively. Power quality indicator of voltage imbalance VIF [104] can be expressed as follows:

_{ab}, V

_{bc}, and V

_{ca}are three-phase imbalanced line voltages. V

_{abe}is the difference between the line voltage V

_{ab}and the average line voltage, V

_{bce}is the difference between the line voltage V

_{bc}and the average line voltage, and V

_{cae}is the difference between the line voltage V

_{ca}and the average line voltage.

## 6. Critical Technologies of Situation Projection

#### 6.1. Three-Phase Unbalanced Load Prediction

#### 6.2. Renewable Energy Output Prediction Considering Uncertainty

#### 6.3. State-of-Energy Estimation

#### 6.4. Fault Prediction and Inspection Management

#### 6.5. Security Situation Projection

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ICT | Information and communication technologies |

AMI | Advanced metering infrastructure |

ADN | Active distribution networks |

SDN | Smart distribution networks |

DGs | Distributed generations |

SG | Smart grid |

RES | Renewable energy sources |

SCADA | Supervisory control and data acquisition |

DMS | Distribution management systems |

EMS | Energy management systems |

TTU | Transformer terminal unit |

FTU | Feeder terminal unit |

RTU | Remote terminal unit |

DTU | Distribution automation terminal |

O&M | Operation and maintenance |

DTs | Distribution transformers |

IoT | Internet of things |

SA | Situation awareness |

NN | Neural network |

ISRM | Information security risk management |

FA | Factor analysis |

GWO | Gray wolf optimization |

GRNN | Generalized regression neural network |

PMUs | Phasor measurement units |

EVs | Electric vehicles |

FDI | False data injection |

DDoS | Distributed denial of services |

IET | Intelligent edge terminal |

PFC | Power flow calculation |

HSE | Hybrid state estimation |

SMSs | Smart monitoring systems |

LVDC | Low voltage direct current |

RATCI | Relatively available transmission capacity indicator |

SNOP | Soft normally open point |

LSTM | Long short-term memory |

TD-LTE | Time-division long-term evolution |

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**MDPI and ACS Style**

Ge, L.; Li, Y.; Li, Y.; Yan, J.; Sun, Y.
Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review. *Energies* **2022**, *15*, 828.
https://doi.org/10.3390/en15030828

**AMA Style**

Ge L, Li Y, Li Y, Yan J, Sun Y.
Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review. *Energies*. 2022; 15(3):828.
https://doi.org/10.3390/en15030828

**Chicago/Turabian Style**

Ge, Leijiao, Yuanliang Li, Yuanliang Li, Jun Yan, and Yonghui Sun.
2022. "Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review" *Energies* 15, no. 3: 828.
https://doi.org/10.3390/en15030828