AI and Energy: Exploring the Importance of Artificial Intelligence and Data Science for Power Network and Smart Grid Developments

Part 1 – AI and Energy
In your own words, provide your explanation of what the meanings of Artificial Intelligence and Data
Science are.
Artificial intelligence (AI) and data science is simulating human intelligence processes by machines using
a multidisciplinary field of organized or unstructured data that depends on the use of processes and
algorithms. It uses scientific systems and their integration with learning processes, where rules are created
for converting data into information. It is executable to provide the algorithm rules the orders with
sequential steps to complete a specific task by logical thinking which uses appropriate algorithm for the
task to obtain desired result in addition to self-correction that allows continuously controlling the rules.
The promising algorithm is used to achieve the result of possible without human intervention by what is
known as artificial intelligence programming[1], [2].
Explain why these areas are important for the power network and smart grid developments.
The energy grid and developments in the smart grid represent one of the areas affecting the growth of
modern society due to the exploitation of natural resources that create negative impact on the
environment, which constitutes a threat to the ecosystem. so, in order to reduce the impact, artificial
intelligence is been used to create a new model for the production and distribution of electrical. The
artificial neural network, plays an important role in forecasting the demand for electricity and dealing
with threats that impact on the performance of Network through system monitoring [3].
Explain the difference between knowledge driven and data driven approaches. Provide an example of one
technique from each category, and explain how it works.
In order to find the difference between knowledge and data-based modeling, we must identify each type
separately.
First, the knowledge driven depends on logic as a tool to represent the beliefs that the agent
retains as more clarification, the program uses knowledge to solve problems. So, we will find that the
knowledge-based system contains different systems types. The knowledge base represents data and info
about the world in the form of an embedding code as well as graphs of logical concepts [4]. The
reasoning engine so that his work derives from any new knowledge by taking the form of rules coupled
with a sequential approach from front or back and also using logical programming and installers of
automatic theories. For example- smart homes is a knowledge driven approach. It works on sensors and
devices that stream data on activity recognition. through that, devices and appliances works on specific
logic.
Secondly, data-based modeling, where knowledge is the main source of the observed data where
logic is not used as a tool in the modeling process. Also, it is a branch of artificial intelligence because it
depends on algorithms and statistical models for performing specific tasks without using direct
instructions from the user. The predictions and decisions without explicitly programming is called
training data. A form of data-based modeling to do tasks for using machine learning algorithms that
contains a set of applications[5], [6]. For example- Bachman diagram is a data driven technique that
works on conceptual data and their relationship. in this a relational model is designed in which entities
and attributes are related to each other.
Part 2 – Applications of Intelligent Systems ” Circuit breaker condition monitoring,
diagnostics and prediction”

Provide a review of the project or research in terms of the problem it was tackling and expected
benefits.
Electrical circuit breakers are of great importance in electrical networks to provide safety, stability,
reliability and flexibility within energy systems. Therefore, it is very important to maintain the circuit
breakers to avoid breakdowns in them and in addition to extending their life to maintain the efficiency of
their work. The reason is that the problems that may arise are the result of ineffective maintenance and an
early prediction of hazards at the operating mechanism. As most of the failure cases occurs in the
mechanical part of the circuit breaker[7], [8] specifically in terms of current signals for the coil. The
travel curve has the function of effectively detecting the state of the operating mechanism, but when
measuring its profile, it will be difficult to do it in all situations. Therefore, studies were conducted on the
circuit breakers in high voltage networks with regard to the operating mechanism based on the
determination of failure, noise cancellation and modeling to assess its working condition[9].
Detail the AI techniques used and how they were implemented. Explain the AI techniques and how
they operate.
The AI technique used in this was Wavelet Beam. it is a unique noise management system that analyze
image based on mode shapes, strain energy, etc. So, it was used in supporting conveyor machine.
One of the most important issues related to circuit breakers was to assess the situation and find a way to
more easily handle the travel curve profiles by adopting a method to simulate the behavior of the
operating mechanism with the model and approach paired with the rules. One of the methods used is to
use a complex model that requires a lot of inputs, a signal whose purpose is to evaluate the spring the
operating mechanism using the theory of multi-dimensional multi-body dynamics to obtain the travel
curve[10], [11], by sensing the rotating displacement between the travel curve and time of the isolation
switch by systems. [12]. The damping force using the theoretical formula with multi-body dynamics
using the Adams program by displaying a simulation without regard to an error detection approach.
Another method of high voltage modeling of circuit breaker with a hydraulic operating mechanism is by
using collected models, so that it can deal with the hydraulic type and not diagnostic [13]. Vibration is
also a fault diagnosis method for capacity building by using a vector machine to analyze faults and using
a vibration acceleration signal[14]. Wavelet beam technology to classify failure by supporting the
conveyor machine, where studies have shown that there is a clear relationship between failure in the
operating mechanism and diagnostic signals [15]. Therefore the neural network was used so that this
technique diagnoses error from capacity building depending on the vibration of the signals and is known
as a function Radial foundation. Another way to evaluate the operating mechanism is to use auxiliary
contact and current windings to address the relationship between the travel curve and auxiliary contact
[15], [16], [17], [18], The role of the current windings is to evaluate the control and mechanical circuit
breakers, and also employ CBWatch- patented monitoring tools 1 and CBWatch-2, its role is to assess the
gas density of the circuit breakers during the evaluation of the operating mechanism by TC. Whereas,
CBWatch-2 can monitor mechanical operation and switching. Another method of using travel keys or
sensors is the main contact with conventional and fixed auxiliary keys [19]. A method of using optical
travel sensors to measure travel contact is installed inside the mechanism or near the fastening
mechanism. Monitoring systems to monitor the mechanical properties of the circuit breakers so that the
travel curve measurement requires a transducer (linear or rotary) that is attached to the circuit breakers
and monitored via the Internet, but this type of monitoring to deal with the CBS cutter as it requires real-
time measurement of the moving displacement from the cutter contact time Circle and recoil to get a

stroke connection [20]. Another technique is the utility model with coil current and travel curve signal to
monitor circuit breaker status.
The chart shown in Fig. 13 shows a complete database
developed through experiments and by application of the
developed model, and all steps to reach the final probabilistic
model-aided diagnostic approach. It is a presented approach
is independent of the CB type, however the target CB here is
SF6 , 72.5 kV. The initial database, a set of 300 TC profiles, is
provided based on experiments conducted on 12 CBs. Later,
the behavior of operating mechanism has been modeled by a
second order model with respect to the time-varying damping
ratio. Also, the secondary database is developed on four
diagnostic parameters, i.e., timing, speed, stroke, and over
travel. These features are classified into three modes, i.e.,
normal, faulty-1 and faulty-2 based on maximum likelihood
(ML) method. The classified data and subsequently the fitted
probability distribution functions construct the final database
as the prior knowledge of IMM estimator and fuzzy approach
for the condition prediction and failure(s) cause detection. In
order to predict the condition of the CBs, IMM estimator has
been used in rule and model-based approach. It is a cost-
effective and simple approach for the state estimation in
hybrid systems. It helps to estimate the state of dynamic-
systems with several behavior modes [23], [24]. The
methodology is comprised of three main sections: assessment
of present condition of the CB (IV-A), condition estimation of
CB for next operation (IV-B) and the failure cause
identification (V).
Classification Based on Maximum Likelihood (ML)-Condition Assessment
ML is a common methods of the classification is employed to organize the database obtained through
experiments and the developed model into meaningful classes, i.e., normal and faulty. In order to
define, the classes are as follows
C = {C1, C2, . . . , Cnc}
where, nc is the number of the classes. The likelihood of class Ci is defined as follows:

P (Ci | x) = P (x|Ci) × P (Ci) /P (x)
where P(Ci |x): The posterior probability of a data x belonging to the class Ci ; P(x|Ci): Conditional
probability to observe x from the class Ci , or probability density function; P(Ci ): is the priori
information, i.e., the probability that class Ci occurs in the study area; P(x): is the probability that x is
observed, which can be written as:
As the diagnostic features under healthy and faulty conditions in high voltage CBs follow normal
distributions [12], in the light of normal probability density function, P(x|Ci) can be expressed as follows:
where δi and μi are standard deviation and mean value of the data in ith class, Ci [25]. A feature could lie
within the normal or faulty range. However, a feature can be large or small than its normal range.
Consequently, three classes have been considered. To give an illustration, the ML-based classifier is
implemented for dataset corresponding to the diagnostic feature “over travel” as shown in figs below: